In the early days of a startup, speed is everything. Founders and lean teams focus on solving customer problems quickly, shipping features fast, and keeping momentum high. But without the right technical processes in place, this rapid progress can quietly create a growing burden: technical debt. Over time, quick fixes and undocumented workarounds become blockers, slowing down development, introducing bugs, and making it harder to scale.

In this guide, we’ll share how we scaled from a 10-person team to 60+, managing multiple products while improving velocity, code quality, and collaboration. The goal: show you how to implement lightweight but effective tech processes that support sustainable growth without slowing you down.

For non-technical founders, this is a critical consideration when developing an idea and building a roadmap that emphasizes precision, foresight, and informed decision-making.

Why startups accumulate technical debt

What “make it work” looks like

At the earliest stage, our approach was simple: get things done. Whether it was fixing a bug or building a new feature, the focus was always on immediate outcomes. If the code worked, it shipped. No time was spent perfecting it, documenting decisions, or aligning with broader standards. By working together and moving fast, we delivered results to customers. Whether it was a critical bug or a new requested feature, the team would do what it took to deliver short-term results.

The hidden cost of quick fixes

This approach worked… until it didn’t. Every shortcut and quick win added up to a growing pile of tech debt. Code became harder to maintain. New team members struggled to onboard. Only a few senior developers understood the full architecture. The risk? Slowed progress, bugs, and over-reliance on key individuals. Small wins without a clear outcome will eventually pile up and create complex roadblocks.

Growth reveals the cracks

The bottleneck of the “star developer”

In many startups, one developer becomes the hero. They know every part of the system and solve issues quickly. But as the team grows, this model breaks. Every decision funnels through one person. While having the same developer throughout the entire process can be valuable, it must be balanced with collaboration across the entire team. Without that balance, the workflow can stall, and critical knowledge can become siloed.

Hiring more developers doesn’t solve this problem. Without shared context, new team members can’t contribute efficiently. You need scalable processes that distribute knowledge, enforce consistency, and allow others to pick up where someone else left off. You can’t fix this by simply adding more developers. Everyone working on the code must understand how it works and why each feature matters to the business. Only with this shared understanding can the product evolve coherently and sustainably.

Learn more about the main reasons SaaS development slows down and how them fix it on this guide.

The restructuring we needed

When founders begin to think about long-term sustainability, one of the first things to evaluate is how knowledge and responsibility are distributed across the team.

As mentioned before, too often critical knowledge is concentrated in one person, usually the technical founder or first developer. That person ends up managing everything: business logic, frontend interfaces, infrastructure, and databases. While this works in the early stages, it creates fragility and burnout risks as the product evolves.

The solution is gradual technical specialisation. By defining clear roles and areas of expertise, you build a team that can scale, allowing each discipline to go deeper and move faster:

• Backend specialists: Focus on business logic and infrastructure, enabling more robust and scalable architectures
• Frontend engineers: Improve the user experience by building intuitive, accessible, and responsive interfaces
• Mobile developers: Master platform-specific capabilities, boosting performance and user satisfaction on iOS and Android

With this structure in place, we introduced shared development guidelines to keep teams aligned. These helped ensure consistent quality, reduce rework, and accelerate onboarding as the company grew.

Setting the rules of the game

As teams scale, inconsistent processes quickly become a liability. In this case, each development team had its own way of approaching the same tasks, a flexibility that worked early on but later introduced friction.

Without a shared methodology, cross-team collaboration became inefficient. Developers had to “translate” how other teams operated, which led to delays, rework, and misalignment.

To address this, a standardised set of development guidelines was introduced. These rules were designed to be clear, lightweight, and practical, ensuring every team could adopt them without disrupting productivity.

Alongside the new methodology, all processes were documented to create a single source of truth. This not only reduced confusion but also made onboarding easier and kept projects aligned as the organisation grew.

Technical implementation: Starting with the basics

Clear rules and procedures were established for writing code across each technology area: web applications, mobile apps, and server-side systems. Each set of standards was defined and maintained by the technical lead responsible for that domain.

These guidelines covered foundational practices such as:

• Code formatting
• Variable naming
• File organisation

Style guides ensured that code was not only consistent but also purposeful, supporting product goals rather than introducing unnecessary complexity. The impact was immediate. New projects began to look and feel consistent across teams, making it easier for developers to contribute across codebases, follow a shared roadmap, and maintain momentum as the organization scaled.

The challenge of existing projects

While new projects could be aligned with updated development standards from the outset, legacy codebases presented a different challenge. Older projects built under less structured systems often required significant modernization, and in many cases, a full-scale refactor wasn’t feasible due to timeline constraints or resource allocation.

To bridge this gap without disrupting delivery, a phased implementation strategy was introduced for updating older codebases:

Level 1: Basic format and style

Standardise code formatting, including spacing, indentation, and file structure. These small changes help improve readability, professionalism, and maintainability.

Level 2: Best practices and error prevention

Introduce rules to minimize common mistakes and improve code reliability. This includes consistent validation logic and structured error handling across the codebase.

Level 3: Architecture and scalability

Apply architectural improvements that support long-term maintainability and growth. These changes are more complex but create the greatest return in scalability and flexibility.

Each level was implemented gradually, allowing teams to make steady progress without disrupting their day-to-day responsibilities. This approach ensured meaningful improvements without derailing delivery timelines.

For a broader perspective, this guide explores app modernization strategies for businesses.

The results behind the process

The structured implementation was designed to achieve three key outcomes:

• Elevate code quality standards across all projects
• Identify and resolve issues early before they impact performance
• Enable sustainable product growth without sacrificing stability

As a result, code reviews became more efficient, with developers aligned around shared conventions. New team members ramped up more quickly, thanks to predictable project structures and consistent formatting.

Here’s a table to understand how structured implementation prevents tech debt:

The role of documentation in scaling

To ensure consistency and accessibility, all rules and processes were centralized within a collaborative documentation platform (e.g., Confluence). This internal knowledge base served as an operational manual, offering clear guidance on:

• How to structure new projects
• The process for implementing specific features
• Standard practices for writing and reviewing code

Maintaining up-to-date documentation requires ongoing discipline, but the return on investment is clear. It reduces dependence on any single team member and allows the organization to operate smoothly even as teams grow or shift.

The tools that make it work: Automating consistency and collaboration

As processes mature, tooling becomes the glue that keeps quality high without slowing teams down. While process and culture matter most, here are some key tools that helped enforce structure and scale without micromanagement:

• CI/CD pipelines (e.g., GitHub Actions, Bitbucket Pipelines): Automate tests, linting, and formatting at every pull request
• Code linters and formatters (ESLint, Prettier, Black): Enforce style and prevent formatting debates during code review
• Architecture enforcement tools (Nx, DepCheck): Alert developers when they violate project architecture rules
• Documentation platforms (Confluence, Notion, GitBook): Keep processes accessible, editable, and discoverable by the whole team
• Error monitoring (Sentry, Rollbar): Surface bugs before they snowball into debt or missed SLA targets

These tools aren’t magic, however they reinforce your standards, save time in review, and reduce reliance on memory or manual oversight. Teams can move faster when the rules are enforced by the environment they’re working in.

Key lessons for sustainable implementation

Several core insights emerged during the rollout of these development standards:

• Gradual change is more effective than sweeping overhauls: Incremental implementation reduces resistance and supports better team adoption
• Flexibility matters: Rules should be evaluated continuously and adjusted based on real-world team needs, not enforced rigidly for the sake of structure
• Team involvement drives success: Processes created with input from those who use them daily are far more likely to be followed and maintained

These lessons helped shape a culture of continuous improvement, one where processes evolve alongside the product, and quality scales with the team.

Build fast, but build smart

Technical debt is easy to ignore in the early days, but it compounds quickly. What feels like “moving fast” can turn into bottlenecks, rework, and stalled progress just when your product needs to scale.

The good news? You don’t need to choose between speed and sustainability. By building with clarity, documentation, and process from the start, and scaling your team structure intentionally, you increase the odds of creating a product that grows without breaking.

At Designli, we help founders strike this balance. Whether you’re validating an MVP or preparing for a next-stage rebuild, investing in process now pays off in momentum later.

Let speed be your advantage, not your liability. Schedule a consultation.

The post How to implement a tech process to avoid tech debt appeared first on Business of Apps.

In this series, Liftoff Mobile highlights inspirational women from across the mobile app ecosystem to share their stories, achievements, and insights on the future of the industry.

This month we chatted with Büşra Amanlar, Senior User Acquisition Manager at Snowprint Studios.

Can you walk us through your early career and how you found your way to Snowprint Studios?

I didn’t originally study marketing. My bachelor’s degree is actually in Translation and Interpretation, and I started my professional career as a Project Assistant. After two years in that role, I realized I wanted to transition into marketing, so I began taking online courses and interviewing with tech companies in Turkey. That led me to my first marketing role as a User Acquisition Specialist at Masomo, a mobile gaming company. Initially, my focus was largely on influencer marketing, but over time, my responsibilities expanded into performance marketing and paid user acquisition.

In 2020, I relocated to Germany and joined Stillfront as a UA Manager. There, I managed multiple game titles and gained hands-on experience across different platforms and markets, which significantly deepened my UA expertise.

Then in 2022, I was ready to take the next step in my career and crossed paths with Snowprint Studios, where I’ve been working for the past two and a half years as a Senior User Acquisition Manager. Our team focuses on operational UA execution, partner management, and driving the overall user acquisition strategy for our game, Warhammer 40,000: Tacticus.

What were some of the early “wow” moments or surprises about how UA works there?

From the very beginning, and still to this day, I’ve been really impressed by how lean and efficient Snowprint Studios is. Despite the company’s strong growth and success over the past three years, we’ve remained a team of fewer than 70 people.

That lean structure directly shapes how UA works here. There’s a strong sense of ownership and self-leadership, fast decision-making, and close collaboration across teams. It was a real “wow” moment to see how much impact a small, focused team can have when processes are clear, and everyone is empowered to move quickly.

How do you divide your time between strategic work, analytics, creative testing, and new channel exploration?

At Snowprint, UA is very hands-on, so my time is spread fairly evenly across several areas. A significant portion goes into strategic work such as defining channel strategy, setting and adjusting budgets, and aligning closely with the wider team on priorities and goals.

Data and analytics are a constant across everything we do. We’re continuously monitoring performance, digging into results, and using those insights to guide decisions across channels and markets. Creative testing is another core focus, as we strongly believe that creative is one of the biggest growth drivers in UA. That means close collaboration with our marketing creative team and external partners.

Finally, we always dedicate time to explore new channels. Even if they don’t scale immediately, testing and learning are important for staying ahead in a competitive mobile landscape. Overall, the mix is dynamic, but that flexibility is what makes UA at Snowprint both challenging and exciting.

How do you prioritize which platforms or formats to focus on for the different games you publish?

We currently run UA for a single title, Warhammer 40,000: Tacticus, which allows us to be very focused and intentional with our platform and format choices. That said, my approach is strongly shaped by my previous experience working across multiple games.

Prioritization always starts with a deep understanding of the product itself. Different platforms and formats perform better depending on a game’s genre, audience, monetization model (e.g. in-app purchases or in-app ads-heavy), and lifetime value.

I’m a strong believer that successful user acquisition can’t exist in isolation. UA and product need to work closely together. The alignment between UA and Product is one of the most important drivers of sustainable growth and long-term success.

What are the core metrics you and your team track? Are there any metrics you feel are undervalued by UA teams?

Like most UA teams, we closely track upper-funnel metrics. They give us early signals on creative and channel performance. We also pay close attention to cost per install (CPI) and early return on ad spend (ROAS), which help us evaluate UA efficiency and short-term performance.

Revenue per install (RPI), also known as average revenue per user (ARPU), is another essential metric we monitor closely, as it provides valuable insight into user quality across different channels and platforms. Looking beyond basic metrics and understanding how users actually engage with our game allows us to make more informed decisions and optimize for long-term value rather than short-term efficiency.

How do recent privacy changes and increasing ad costs impact your UA strategy at Snowprint?

It definitely changed a lot. From a user perspective, these privacy changes are absolutely a positive development—but from a marketing standpoint, they forced a fundamental shift in how we evaluate performance.

With the introduction of iOS privacy changes and despite SKAdNetwork reporting, campaign-level data became much more limited, making it harder to draw clear, data-driven conclusions purely from attribution. At Snowprint, we’re fortunate to take a more holistic approach to marketing, looking beyond attribution alone and combining multiple data sources to guide decisions.

How do you ensure UA efforts are tightly linked to LTV and ROAS, especially in mid-core games where monetization mechanics are more complex?

We rely heavily on cohort analysis, early monetization and engagement signals, and ROAS trends over time to understand which channels, creatives, and audiences bring in high-quality players. Creative testing also plays an important role here.

Strong alignment between UA and the product is essential. At Snowprint, we’re fortunate to work with experts across disciplines who truly understand this connection. We make a conscious effort to ensure that UA activity is closely aligned with the product roadmap and live ops.

Mid-core audiences can be surprisingly emotionally invested. What’s been the most unexpected layer behaviour, community trend, or sentiment that influenced your UA thinking?

We have a highly engaged Discord community, and our community team stays in close contact with players, collecting feedback, understanding sentiment, and relaying those insights directly to the product team.

That player feedback plays an important role in how we think about both product and UA decisions, especially when it comes to creative production. Because we work with a Warhammer IP, we see how emotionally connected players are to specific factions and characters, and that passion has a real impact on how we develop new UA creatives.

Ultimately, we’re here to make great games. That’s literally one of our company’s ethos! Maintaining an open dialogue with our community helps ensure we stay aligned with player expectations and continue building experiences that players truly care about.

Snowprint was acquired by Modern Times Group in 2023. Did that acquisition bring any significant changes in the way you work?

Yes, absolutely. One of the biggest benefits of the MTG acquisition has been increased access to industry knowledge and resources. In UA, gaining broader insights and learning from how others approach similar challenges is incredibly valuable.

What non-traditional UA levers or hybrid tactics are you currently exploring or scaling?

Since Warhammer 40,000: Tacticus is a strong IP game, the community plays a particularly important role in our strategy.

We actively support community initiatives and content creators who are passionate about the game, and we see this as a powerful complement to our activities. Working closely with creators helps us reach highly engaged, high-intent audiences, while also reinforcing authenticity and long-term player trust.

What barriers have you faced in your career, and how have you addressed them?

Coming from a degree in Translation and Interpretation, I initially had to work harder to build credibility and close knowledge gaps. I addressed this by investing heavily in self-learning, including taking online courses, asking questions, and learning from hands-on experience.

As a woman in a technical space like UA, there were also moments where I had to be more intentional about making my voice heard. Over time, I learned to trust my expertise, speak up with confidence, and let results speak for themselves. Having supportive teams and mentors along the way made a huge difference, and those experiences ultimately helped me become a more confident UA professional.

For other women looking to build a career in UA or mobile growth, what one piece of advice would you give?

Don’t wait until you feel “ready” to take the next step. UA and mobile growth are fields where you learn by doing, and confidence often follows action, not the other way around. Be curious, ask questions, and invest time in understanding both the data and the product.

Most importantly, don’t be afraid to take up space. Your perspective matters, and the industry truly benefits from having more women in these roles.

Looking ahead to 2026, what do you believe is one of the biggest opportunities and biggest risks for UA in mobile mid-core games?

I think the biggest opportunity is in stronger alignment between creative, product, and community. Teams that can clearly communicate deep gameplay and long-term value through their creatives will be better positioned to attract high-quality and highly engaged players.

The biggest risk is over-optimizing for short-term signals in an increasingly complex data environment. For mid-core games with longer retention, losing sight of LTV in favor of surface-level metrics can hurt sustainable growth.

Beyond metrics and growth levers, what part of this job energizes you the most?

The team, without a doubt!  Working with smart, passionate people who genuinely care about the game and the players is what energises me the most. The collaboration, self-leadership, and trust within the team make even the toughest challenges exciting to tackle.

If you imagine a future Senior UA Manager ten years from now, what’s one skill you think they’ll need to master?

They’ll need to be very comfortable making decisions without having perfect data. As tracking becomes more limited, strong judgment, product understanding, and an instinct for what players actually care about will matter more than ever.

Storytelling will also be a key skill. Standing out won’t just be about optimization; it will be about clearly communicating why a game is meaningful and fun to the right audience. Finally, collaboration will be essential. Working closely with product, creative, and community teams to build long-term player relationships rather than focusing only on short-term growth.

The post Building growth in mid-core mobile: Büşra Amanlar on user acquisition at Snowprint Studios appeared first on Business of Apps.

AI is now the dominant narrative across advertising. But long before generative AI entered every conversation, machine learning had already become foundational to programmatic infrastructure.

Having built and scaled adtech companies for over a decade, and now leading Kayzen, Tim Koschella has witnessed programmatic evolve from early automation systems to today’s AI-infused ecosystems.

In this conversation, he shares his perspective on where AI truly adds value, where it is overstated, and why human accountability must remain central as systems become more intelligent.

What’s actually new and what isn’t

While AI dominates headlines today, Tim grounds the conversation in history.

“Let’s establish a fact first: AI has been at the center of adtech for more than ten years already. Advertising is one of the earliest markets where AI was applied at scale.”

What most people now call AI has long been part of programmatic advertising, where machine learning systems operate at a massive scale. These systems rely on structured data to predict outcomes, whether a user is likely to click, convert, or purchase, and then return those predictions within milliseconds.

Generative AI changes the interface layer. “It’s more flexible. It handles unstructured data. It tolerates imperfect inputs. It’s a much better companion for humans.” That shift, however, does not make it universally superior. “In RTB, speed matters. Inference calls need to run within milliseconds. Generative AI often needs seconds. That time lag makes it less useful for machine-to-machine communication.”

Usefulness, in his view, is practical rather than philosophical. “Anything that improves measurable outcomes for advertisers or helps humans do their job faster and better is useful. As long as you put the customer problem first, and find ways AI can solve it, it’s a path worth pursuing.

Why performance is still hard to explain

Despite AI being embedded in advertising infrastructure for years, many teams still struggle to clearly explain performance outcomes. The issue, he argues, is less about model sophistication and more about definition. Performance, he emphasizes, has to start with a deep understanding of the advertiser’s business and the goals the company aims to achieve with its marketing investment.

“At its core, performance is used in so many different ways that it remains vague until it’s clearly defined in context. A brand agency managing Coca-Cola’s mobile ad budget in Europe and a mobile RPG game looking for growth in Japan will likely have a very different perspective on how to define and measure performance.”

Optimization conversations often stop at the metric label rather than the business reality behind it. “Take ROAS as an example. When someone says they optimize for ROAS, I immediately ask: which ROAS? D7? D30? Lifetime? How does your product generate revenue? Is your revenue model driven by outliers? How do you measure incrementality versus attribution, and what do you do when they point in different directions?”

The complexity is not purely technical; it is strategic. “We need smart professionals to make sense of this complexity. AI can help, but humans have to stay at the center of decision-making.”

The pressure to have an “AI story”

If AI has been embedded in advertising systems for years, why does the current moment feel so amplified? Much of it, Tim suggests, comes down to narrative pressure, particularly from investors and capital markets.

“The pressure to include AI everywhere is tremendous, especially for companies backed by large investors or listed publicly. Every CEO is expected to have an AI equity story.”

He does not dispute that AI can improve most businesses. What concerns him is how it is framed. “AI doesn’t have to be the fabric that connects everything in your business, like the tissue in your body. In many cases, it’s simply a useful tool to improve efficiency and quality.”

When AI becomes an identity rather than a capability, expectations can quickly outpace reality. “If those intentionally created expectations are not being met down the road, it may create a sentiment of delusion. And those companies which actually have value-generating applications of AI may need to make an extra effort to explain themselves.”

He does not argue against AI adoption. What concerns him is the inflation of narrative beyond practical value. In many cases, he notes, AI is a tool to improve efficiency and quality, not a reinvention of how businesses operate. The differentiator, then, is not how aggressively AI is introduced, but whether it solves real customer problems and delivers measurable value.

Principles before technology

That concern leads to a deeper question: as AI becomes more embedded in how businesses operate, what should guide its use?

“In programmatic advertising, there are many data signals that AI can process, interpret, and act upon. But there is also noise in the signal that can lead to false or misleading interpretations. AI can easily be misled.”

At the scale of the global programmatic ad market, the consequences of misinterpretation can be severe. “A wrong action can lead to losses that can render entire companies bankrupt overnight. It’s similar to financial markets. If you were to automate financial transactions without imposing a system of limits and checks and balances, you could just blow up the whole thing in one day.”

Human oversight, therefore, is structural rather than optional. “AI will never replace the critical thinking and decision-making humans bring to the table. Ultimately, the responsibility and accountability for the actions of the AI we use lie with us humans.”

There is, however, a distinction between interpretation and execution. “Few people are actually good at interpreting large, complex datasets. So the chances that a human will ‘get it wrong’ are likely much higher than the AI getting it wrong.” But when automation directly controls capital without guardrails, the risk profile changes. “If AI gets it wrong, it may spend an unapproved $1M budget in the wrong place faster than you can blink. These kinds of major high-impact mistakes are less likely to be made by humans.”

Where AI is underused

Where should AI create the most value before money is spent?

“In programmatic, that’s clearly the planning phase of a campaign. Today, AI is very active during the optimization phase. But AI is rarely used in pre-campaign planning, where the strategy is decided, and the expectations are set.”

Intelligence has been disproportionately focused on live campaign execution, while the strategic phase, where assumptions are formed and expectations are set, remains comparatively underdeveloped. Before execution begins, AI can help teams model scenarios and make systematic use of structured first-party signals. “The most valuable asset before the first money is spent is advertiser first-party data, data about their existing users, funnel metrics, seed audiences for lookalikes, and related segmentation inputs. This is where systematic use of AI can add a lot of value without requiring campaigns to spend money.”

Once campaigns are live, intelligence should extend beyond acceleration and efficiency to support understanding. “AI should help teams learn rather than just react. It should surface anomalies and highlight trade-offs.” As systems become more capable, clearly defined guardrails become even more important. “For any major decision that operates outside of defined boundaries, humans should serve as the final decision layer.”

He illustrates this with a practical scenario: a $20,000 daily campaign targeting a $100 CPA. If one pocket of inventory suddenly shows a $10 CPA, the system will scale into it aggressively. But if flawed conversion data feeds that signal, the efficiency logic may amplify the mistake before it is detected. “A human, with good judgment, will be able to understand there is an anomaly and may decide to pause. The boundaries have to be defined carefully. That can be quite tricky, but essential.”

The distinction is not between human and machine capability, but between automation and accountability.

The outcome: Empowerment

The conversation ultimately shifts from infrastructure to mindset. Tim challenges one of the industry’s core assumptions: “One common myth is that you need a huge team of data scientists to compete with big incumbents. What matters more is the quality of your team members and the way they collaborate to establish a strong understanding of the problems that need to be solved.” Alignment across disciplines — data science, engineering, and business stakeholders — matters more than scale alone.

He is equally cautious of exaggerated technical mystique. “Beware of small companies that claim their smart team of scientists and ex-NASA engineers have built solutions no one else ever could come up with. It’s likely a nice sales pitch.”

If AI is deployed thoughtfully, marketers should not feel replaced. They should feel capable of doing more with clarity and control. “Nowadays, a team of five can easily manage a $500M annual marketing budget while still being in control of making real-time campaign flight adjustments and optimization decisions every single day.”

For Tim, intelligence should scale capability without eroding responsibility. “AI should empower decisions. It should never replace judgment.”

The post Building smarter AI without giving up control appeared first on Business of Apps.

2025 marked a transition in mobile performance marketing, not because the industry resolved its structural challenges, but because teams adapted to them. After years of platform-driven disruption, performance stacks have stabilized into constrained but workable operating modes across measurement, channels, and buying. These baselines remain imperfect and uneven, but they are now broadly understood.

Contextual buying has moved from fallback to core execution. Creative has shifted from a downstream output to a primary optimization input. CTV is beginning to support performance objectives beyond awareness. On iOS, SKAdNetwork is now treated as the default performance measurement layer despite its limitations, while on Android, the winding down of Privacy Sandbox has left GAID-based attribution and retargeting largely intact. The result is a bifurcated measurement reality that performance teams must actively manage.

With fewer near-term platform resets expected, 2026 is defined less by reacting to new rules and more by how teams operate within these constraints. The most meaningful shifts are not driven by new channels or frameworks, but by how teams structure learning, allocate budget, and determine where incremental performance is generated. Against this backdrop, these five shifts will shape mobile performance marketing in 2026.

AI shifts from optimization support to system orchestration

For mobile performance teams, AI is no longer confined to bid-level prediction. In 2026, its role expands across campaign structure, test prioritization, and the way learning is operationalized across channels.

At the creative layer, AI-assisted asset generation and modular frameworks reduce production as a constraint on scale. Teams can test broader combinations of formats, messages, and contexts without proportional increases in cost or turnaround time. This expands the creative learning surface and accelerates feedback loops, particularly in CTV and in-app environments where creative-context fit can outweigh audience selection.

More importantly, AI is moving from output optimization to workflow orchestration. ML-based bidding remains the core engine, with predictive models assigning impression-level value, but orchestration layers increasingly determine how that intelligence is applied. By connecting creative, supply, measurement, and post-install quality signals, agentic systems manage experimentation, budget redistribution, and performance diagnostics across campaigns – surfacing stalled learning, highlighting variables that require exploration, and signaling when to scale or constrain spend. This shift is already underway: according to Appsflyer, 57% of marketers use AI agents today, with 32% deploying them specifically for campaign optimization.

In practice, ML-based bidding remains the core engine of AI performance, with predictive models determining value at the impression level. What’s changing is how that intelligence is deployed across campaigns: orchestration layers now connect creative, supply, measurement, and post-install quality signals, directing where bidding models learn, test, and scale most efficiently.

Contextual becomes the main execution layer for optimization.

This year, contextual targeting will complete its shift from a supporting role into one of the primary optimization surfaces for mobile UA. User-level identifiers are becoming an increasingly fragile foundation for scale, especially on iOS, where only a minority of iOS users consent to share their IDFA, and Apple has made fingerprinting even less reliable by freezing the iOS version in Safari’s user-agent string. As a result, optimization will keep reorganizing around contextual signals as core decision inputs.

Modern optimization models focus on the specific moment of delivery rather than broad app categories. At bid time, they evaluate placement type, session context, device characteristics, and creative fit, turning contextual signals into live inputs that shape bids and budgets.

By combining semantic, visual, and structural signals with auction data and aggregated postbacks, including SKAN, these models can identify inventory that consistently delivers value even when user identifiers are scarce. Efficiency and scale therefore depend less on audience resolution and more on the depth and reliability of the contextual learning surface. The same intelligence now informs campaign setup, with deep-learning recommendation models surfacing refined whitelists and structurally similar environments to guide efficient expansion.

CTV establishes itself into core performance planning

CTV is moving beyond its traditional top-funnel role and into core mobile UA planning. Rather than operating as a parallel awareness layer, it is increasingly planned, tested, and optimized within UA stacks alongside other performance channels.

This shift is being enabled by supply dynamics. FAST platforms continue to expand inventory faster than advertiser demand, placing downward pressure on CPMs and creating room for performance testing, creative iteration, and tighter frequency control. This pricing compression does not however imply a general decline in quality – investment in FAST continues to raise the top end of supply, while premium SVOD environments still deliver higher-attention placements with more consistent post-exposure behavior. Together, these layers make CTV a viable learning surface for mobile acquisition.

That said, as supply scales, inventory quality dispersion does increase. Expansion inherently introduces a wider range of attention levels, from professionally programmed environments to low-signal filler. In this context, selection intelligence becomes a key differentiator. DSPs and growth platforms that can identify high-performing inventory early and suppress low-quality placements will outperform those that rely on access alone.

Measurement is evolving accordingly. CTV will not be judged only on last-touch attribution, but also through assisted and influence-based models that capture its upstream impact on mobile conversions. Aggregated outcomes at the show, channel, and placement level, alongside incrementality testing, will be used to validate whether CTV spend is additive and to calibrate acceptable CPIs and contribution windows. As a result, sparse direct attribution is no longer a blocker, but an expected characteristic of performance-led CTV.

App proliferation raises the strategic importance of retargeting

AI-assisted development is lowering the barrier to building apps, accelerating proliferation across categories and pushing markets into deeper saturation. More apps now compete for the same user pool within the same acquisition auctions, often with increasingly similar value propositions. The result is inflation in UA: CPIs rise, differentiation weakens, and the efficiency of marginal installs declines.

For established apps, this changes the growth equation. As acquiring net-new users becomes more competitive and less predictable, incremental budget shifts away from repeated discovery and toward extracting more value from users already acquired. Global retargeting spend already reached $31.3 billion in 2025, up 37% YoY, as re-engagement moves from a secondary optimization layer to a core growth lever focused on owned audiences.

The implication is structural. Performance success shouldn’t be defined solely by first installs, but by the ability to compound value over time. Growth systems must optimize across reactivation, repeat usage and lifetime contribution – using retargeting to extend and amortize prior acquisition spend rather than continually resetting the funnel. In saturated app markets, the ability to efficiently re-enter users into the product experience becomes as critical as the ability to acquire them in the first place.

Programmatic buying becomes more efficient and automated

Programmatic buying is becoming structurally more competitive with walled gardens as independent DSPs and SSPs shorten supply paths, tighten curation, and remove friction from the transaction chain. Vertically integrated stacks have set a higher efficiency baseline, and open platforms are reorganizing infrastructure to close that gap through tighter supply control and supply path optimization. As a result, curated marketplaces and SPO are moving from hygiene into core infrastructure for performance budgets.

Automation is also pushing deeper into daily operations. AI-driven systems increasingly manage routine workflows, surface inefficiencies, and guide budget allocation with less manual intervention. In parallel, more specialized contextual marketplaces are emerging. While static app-level taxonomies remain coarse, SSPs and exchanges are using semantic and AI-based classification to build richer, ID-less contextual supply pools, giving DSPs new ways to scale without relying on user identifiers.

What this means for growth teams

Taken together, these shifts reflect a common reality in 2026: performance teams are operating within known constraints, from aggregated measurement on iOS to contextual optimization, rising competition for installs, and CTV entering the mix with imperfect attribution.

What has changed is where leverage comes from. Advantage increasingly lies in how teams combine creative testing, contextual delivery, supply selection, and post-install quality signals – and how deliberately they decide where to explore, and where to better use the inventory, formats, and users already in play.

As automation absorbs the mechanics of buying and pacing, the role of growth teams continues to shift away from manual optimization and toward deciding what is worth testing, how learning is applied across channels, and how budget is allocated between acquisition, retargeting, and channels like CTV.

In 2026, performance differentiation comes less from reacting faster and more from making better trade-offs inside a constrained system.

The post Scaling mobile performance in 2026: Five shifts within known constraints appeared first on Business of Apps.

Matt Hudson, Founder of BILDIT, has spent enough time inside large retail organisations to know where mobile app growth actually stalls. It is rarely the product. The app works; the engineering is competent. The problem is that nobody in the building has a strong enough reason to care whether anyone uses it.

Matt made this argument on the latest episode of Branch‘s How I Grew This, and it is worth taking seriously because it reframes a question most retailers treat as technical into one that is fundamentally about organisational design. When bonuses are tied to channel-level performance, when email teams own their open rates, and web teams own their traffic, the app becomes everyone’s second priority.

If the mobile app does not improve return on ad spend, nobody will do anything with it. The implication runs further than it first appears. Attribution that cannot connect app engagement to measurable outcomes means the app remains peripheral in planning conversations regardless of how good the product is. Store teams will not promote something that does not help their numbers. Email teams will not route into an experience that does not show up in their reporting. The app sits in a corner accumulating respectful neglect.

The channel conflict problem

The web versus app split that persists in many retail organisations is less a strategic disagreement than an accounting artefact. Separate budgets and separate attribution models create teams with an incentive to defend their own traffic, and that defence regularly produces worse outcomes for the customer.

However, the customer does not know or care which internal team owns the surface they are using. If the app is the fastest path to conversion on mobile, routing users away from it introduces friction at precisely the moment the brand needs to remove it.

The uncomfortable operational consequence is that internal competition can be structurally built into the organisation. Solving it requires changing what gets measured and what gets rewarded, which is often harder than shipping a feature.

When an app is actually worth building

For smaller retailers still deciding whether a mobile app makes sense, several signals matter: catalogue depth, physical retail presence, loyalty mechanics. But the variable that matters most is purchase frequency.

A customer who returns regularly benefits from an app in ways that are concrete and measurable — easy login flow, reduced checkout friction, faster time to purchase. This is not a demographic question about younger vs older shoppers. Loyal customers use whatever surface creates the least resistance, and for high-frequency retail relationships, a well-built app is usually that surface.

The returning customer segment is where most of the revenue actually lives. It is smaller in volume, concentrated in value, and more sensitive to friction than acquisition metrics tend to suggest. An app that removes one unnecessary step from a repeat purchase pays for itself in ways that are difficult to see in channel-level reporting but show up clearly in cohort analysis.

AI as a distribution question

Summarisation in search results is already reducing click-through to retail sites, and the question of whether a brand’s products appear accurately in AI-generated responses is now an operational one.

AI systems are not trying to find a relevant link. They are trying to produce a response that appears reliable and correct, which is a meaningfully different objective that rewards different kinds of content.

Backlink-heavy SEO instincts are poorly matched to this environment. What reads as trustworthy to a model doing inference is closer to what reads as trustworthy to a careful human reader: forums, FAQs, human-validated sources, content with a clear structure and a specific answer to a specific question.

Product descriptions written for keyword density fail that test. FAQ-style content tends to pass it, partly because conversational queries are themselves structured as questions, and a model evaluating correctness finds it easier to work with content that mirrors that structure.

Automated agents crawling or browsing a site behave differently depending on their purpose — some pull raw HTML, some simulate a browser, some attempt to mimic human sessions. Understanding which is which, and what each is doing, is the baseline for making any informed response to AI-driven traffic rather than simply being subject to it.

Data as the underlying constraint

Product data has become a distribution asset. Descriptions, attributes, structured metadata, Q&A content determine whether a product appears correctly in AI responses, not just how it ranks in traditional search. Most retail organisations still treat this as a housekeeping problem. However, it is closer to a channel problem, and it compounds until the gap between what a brand sells and what AI systems can accurately represent becomes wide enough to matter in revenue terms.

The broader pattern is that friction accumulates wherever organisational incentives, technical architecture, and data quality diverge from how customers actually behave. Retailers tend to discover this one surface at a time, and each discovery tends to reveal the same underlying misalignment wearing a different set of clothes.

Tune in below to listen to the episode, or check out all the episodes here.

The post Your mobile app strategy is backward appeared first on Business of Apps.

In 2025, the global mobile app market experienced a fundamental shift. Traffic-driven growth approaches reached their limits, and broad, volume-oriented strategies began to lose traction. Instead, value-led operations, technology-enabled optimization, and deep localization emerged as the primary drivers of sustainable growth.

SocialPeta has partnered with industry leader AdChampagne to publish the Global mobile app marketing trends 2026 whitepaper. The report is enriched with exclusive insights from industry leaders, including PlayableMaker, AppNava, YouAppi, App Masters, Alison.ai, MAF, TMUG Club, web2wave, and Spiral.

The white paper offers an in-depth view of the latest global mobile app marketing trends, uncovering several critical shifts:

• A more concentrated and competitive market: Top players increase creative output despite a shrinking advertiser base
• AI as a core capability: Leading AI apps scale through volume and quality, while weaker products exit
• Sector divergence: Finance prioritises trust and lifetime value; other verticals favor established, patient players
• North America as a touchstone: Rational users and strict standards test scalable models
• From traffic to capabilities: Success now depends on content quality, production systems, AI integration, and long-term growth

The report decodes the growth dynamics shaping AI-powered app categories and delivers a comprehensive analysis of marketing trends and regional strategies across key verticals, including finance, social, and health. Serving as an end-to-end guide to global expansion, it offers actionable insights spanning strategic planning through execution.

Mobile apps in 2025

From an overall market perspective, mobile app marketing in 2025 shows a typical “scissor gap” phenomenon. The average monthly number of active advertisers decreased by approximately 16.7% year-over-year. Meanwhile, the average monthly volume of ad creatives per advertiser increased by 73.3% year-over-year.

Advertising trends in global mobile apps in 2025

Source: SocialPeta

What does this mean? The market is shifting from a phase where “everyone could participate” to one where “only those who sustain continuous investment can remain.” A decline in the number of advertisers does not indicate market contraction; rather, it reflects a significantly higher competitive threshold. Leading and mid-tier advertisers are competing for user attention through higher-frequency and more sophisticated creative testing.

From a category perspective, business & productivity, utilities, entertainment, and finance continue to account for the largest shares of advertisers. From a creative volume standpoint, entertainment (short dramas), reading, and AI-related apps hold a dominant position.

Mobile app advertising by category in 2025

Source: SocialPeta

Another notable shift is at the system level: the advertising distribution ratio between iOS and Android has stabilised at 4:6. However, the average number of creatives per advertiser on iOS is significantly higher than on Android. This reflects the higher demand for refined content and high-quality creatives in high-paying markets.

Advertising of mobile apps on iOS and Android in 2025

Source: SocialPeta

From traffic to creativity: Video, static, and playable ads in 2025

From data of AdChampagne, in 2025, total user acquisition (UA) spend reached $78 billion, representing a 13% year-over-year increase. Notably, this growth was driven almost entirely by iOS. Investment in Apple’s ecosystem surged by 35%, while Android budgets effectively stagnated, declining by approximately 1%. This divergence reflects advertisers’ increasing focus on platforms that deliver stronger monetization and higher-value users.

Non-gaming verticals emerged as the primary drivers of marketing spend growth. Sectors such as eCommerce, FinTech, and Betting continued to scale investment, underscoring how mature industries are prioritizing sustainable growth and long-term user value over short-term traffic expansion.

Top 5 non-gaming verticals in 2025

Source: AdChampagne

At the same time, performance marketing in 2025 is undergoing several structural shifts. AI-generated content has significantly reduced creative production costs, enabling brands to test a broader range of formats at scale. Platform algorithms across Meta, TikTok, YouTube, and VK Ads are increasingly optimizing toward creative performance rather than audience targeting.

Ad formats are becoming shorter, faster, and more native, with effective value delivery in the first two to three seconds now critical. In this environment, Creative Rate of Efficiency (CRE) has emerged as a core KPI, measuring how quickly and cost-effectively individual creatives generate results.

Across ad formats, video remains dominant in 2025, accounting for approximately 70% of total social ad inventory. Algorithmic prioritization, stronger attention retention, higher CTR and CVR in mid-funnel stages, and the ability to demonstrate product value within the opening seconds continue to drive video’s leadership.

Video formats in 2025

Source: AdChampagne

Despite video’s dominance, static ads remain highly relevant, particularly during testing phases and for Android traffic. With the lowest production costs, static creatives enable rapid hypothesis validation, efficient A/B testing of offers and USPs, and strong performance in quick-conversion scenarios. They are especially effective for advertisers distributing through alternative app stores.

Meanwhile, playable ads are among the fastest-growing formats in 2025. Media platforms continue to expand interactive inventory due to playable ads’ ability to generate deeper engagement signals, which are increasingly critical for algorithmic optimization and user quality assessment.

Playable ads in 2025

Source: AdChampagne

Performance marketing in 2025 has shifted from a competition for traffic scale to a competition driven by creative capability, system efficiency, and long-term value creation, with each ad format playing a more clearly defined role within the growth funnel.

AI: The most certain growth curve

If short dramas capture attention, then AI apps address efficiency and trust. In 2025, one of the most prominent trends is that AI is no longer just a tool used in marketing — it is becoming a foundational capability of marketing itself. Across most utility app marketing copy, developers are increasingly, and almost implicitly, positioning their products with AI as the most prominent title or selling point.

Top 20 AI apps by Advertising in 2025

Source: SocialPeta

In 2025, the AI sector entered an elimination phase. Many small and mid-sized products exited the competition due to weak monetization capabilities and severe product homogeneity. As a result, the total number of advertisers dropped by nearly half. However, leading companies continued to expand their efforts, attempting to capture greater market share through increased creative output. With the arrival of year-end marketing campaigns, the average monthly creative volume also peaked in December.

Trends in advertising and creatives for global AI apps in 2025

Source: SocialPeta

Other verticals

Among other verticals, financial apps have maintained a steady yet measured growth pace. This is not because finance is easier to operate; on the contrary, it is one of the sectors with the highest requirements for marketing systems and the lowest tolerance for error.

Marketing for financial apps in 2025 has clearly demonstrated several characteristics: rather than pursuing explosive traffic growth, advertisers are placing greater emphasis on user quality and lifetime value. Creative messaging is becoming more rational, with deliberately sensational content significantly reduced.

Compliance, trust, and the cost of explanation have become prerequisites for marketing. This also means that marketing cycles for financial apps tend to be slower, but once a model proves successful, it delivers strong stability and compounding returns.

Top 20 finance apps by advertising in 2025

Source: SocialPeta

Leading financial apps have not shown significant changes in their advertising investment.

In this white paper, we also present detailed marketing case studies of overseas financial products such as Holafina, WeBull, and DiDi Finanzas. While each product’s ad creatives reflect its unique brand identity, their core messaging consistently reinforces the cognitive anchors of “security, transparency, and clarity.”

Compared with AI, the marketing approach pursued by financial apps is not focused on rapid bursts of growth, but rather on long-term trust building, which requires greater patience.

Meanwhile, other sectors such as utilities, education, and health have clearly entered a stage where strong players continue to strengthen their dominance. Opportunities still exist, but they increasingly favor companies with established foundations, strong teams, and long-term commitment. Trends and representative case studies for these sectors have been analyzed in detail in the full version of the white paper, which we encourage readers to explore.

Regional perspective

From a global perspective, North America remained the most important — and most selective — market for mobile app overseas expansion in 2025. It not only offers the highest user spending power but also imposes the strictest requirements on content quality, brand communication, and compliance standards.

Non-game apps’ advertising strategies in North America

Source: SocialPeta

Based on the earlier insights from our report, whether in AI or financial apps, the North American market has entered the phase of “moving away from gimmicks and focusing on value” earlier than other regions. Users tend to make more rational decisions, and conversion cycles are longer. However, once trust is established, lifetime value is significantly higher than in other markets.

For this reason, North America serves more like a touchstone — products that successfully establish scalable models here often possess the long-term capability to replicate their success in other markets. Conversely, models that rely solely on short-term traffic and media buying tactics will eventually encounter a growth ceiling in this market.

Analysis of popular ad creatives for non-game apps in North America

Source: SocialPeta

Looking back at mobile app marketing from the vantage point of 2026, a clear consensus is emerging: real competition has shifted from traffic acquisition to capability building. AI is no longer a conceptual enhancement, but a foundational capability deeply embedded in both production and marketing workflows. Meanwhile, sectors such as finance are demonstrating the value of steady, long-term growth through a long-term strategic approach.

Spanning 90 pages of in-depth analysis, the report delivers actionable insights to help mobile app marketers scale globally, refine performance strategies, and unlock long-term user value.

Download your copy to identify growth opportunities across global markets.

The post Decoding the breakout AI across finance, social, and health apps appeared first on Business of Apps.

App store search has historically functioned as a matching system: a user’s query is matched against indexed terms, and results are ranked accordingly. What’s changing is that the stores are increasingly doing interpretive work before they do matching work, aka inferring what a user means from a conversational phrase. That shift from matching to interpretation has consequences for how optimisation should be approached that go beyond adding long-tail keywords to a metadata sheet.

In a recent App Talk, Dave Bell, CEO at Gummicube, outlined the mechanics of that shift. His core argument is that optimisation strategies built around a narrow set of high-volume keywords are losing effectiveness because keyword ranking increasingly captures only part of the surface on which discovery actually happens.

How the search result itself became a conversion surface

User behaviour in search results has shifted toward faster, more glanceable evaluation. A significant share of download decisions now happen directly from the results page, without the user opening the full product listing. This compresses the window in which an app has to register and shifts optimisation priority toward the elements visible in that compressed view.

Apple has responded to this by expanding the information density of search results: app tags, additional visual indicators, and other signals that affect how an app is categorised and perceived before a user consciously engages. These additions matter because they alter conversion rates directly and immediately. When the presentation layer changes, performance changes with it, independent of any keyword ranking movement.

The query side is also shifting. Apple’s interface now allows users to refine searches by tapping suggested terms, building more specific phrases from an initial query. The practical effect is a more distributed traffic pattern. Instead of a small number of dominant keywords accounting for the bulk of impressions, traffic spreads across a wider range of specific combinations. As Dave explained, an optimisation strategy anchored to a primary keyword is structurally misaligned with how queries are actually being generated.

Natural language search changes what the store has to do

Apple has introduced natural language search in the App Store, anticipating a shift in query behaviour already visible on the web where AI tools have accustomed users to phrasing requests as full sentences or descriptions of problems rather than abbreviated keyword strings. The store now has to interpret a phrase like “app for splitting bills with housemates” rather than simply matching it against indexed terms.

That interpretive step has a meaningful consequence. Even when the underlying intent is identical, a conversational query can produce different results from a direct keyword search, because the ranking process now involves inference about meaning before it involves matching. This introduces variability into results and distributes impressions more broadly across mid- and long-tail terms.

For ASO, this means that feature-based and use-case-specific language in listings becomes more valuable, not as a replacement for core keyword targeting, but because it determines how an app performs across the growing share of queries that don’t map cleanly onto a single high-volume term. Listings that describe what an app does in the same register users use to describe their problems are better positioned for natural language matching than those optimised purely around search volume data.

Source: Business of Apps via YouTube

LLMs as a parallel discovery channel

ChatGPT, Gemini, Perplexity, and similar tools are beginning to surface app recommendations in response to conversational prompts, with direct links to store listings. This constitutes a discovery path that operates outside the store’s own ranking algorithm entirely.

The relevance signals that determine whether an app appears in an LLM response are different from those governing in-store ranking. LLMs draw on web-indexed material — blog posts, third-party reviews, developer sites, publicly accessible descriptions — alongside store metadata. An app with strong in-store optimisation but limited coherent web presence may be largely invisible in this context.

In other words, this expands the optimisation perimeter. Store metadata alone is insufficient. Teams need to consider how their app’s functionality and value would be described in natural language by someone unfamiliar with it, and ensure those descriptions are present in web-facing content. The test is not whether the content ranks for keywords but whether it communicates clearly to a system trying to match a user’s stated problem to a relevant tool.

Apple’s web indexation decision

In late 2025, Apple made its App Store listings fully crawlable, reversing a long-standing position that had kept store content largely confined to the in-app environment. Listings are now broadly indexable, including by the AI systems that rely on web crawling.

Restricting web visibility was a deliberate choice on Apple’s part maintained for years. Opening it suggests Apple sees external search — including AI-driven discovery — as a channel worth feeding rather than one to be contained. For app developers, this means store metadata now functions in a more open environment, where its effectiveness depends partly on how it reads to systems with no specialised knowledge of App Store conventions.

The strategic shift from keyword targeting to distributed visibility

The cumulative effect of these changes is a movement away from a traffic model concentrated around a small set of high-volume terms, toward one distributed across a broader range of specific, intent-driven queries. Search is becoming more personalised and more specific, a trend driven by both interface changes and the behavioural influence of AI tools.

The appropriate response is not to abandon core keyword targeting but to treat it as one component of a broader visibility strategy. Performing well across a wider range of queries generates richer conversion signals for the stores, which in turn supports sustained ranking across that diversified landscape. The feedback loop favours breadth.

What this amounts to in practice is a shift in how ASO work is scoped. Keyword research remains necessary but is no longer sufficient to define the optimisation surface. Feature descriptions, use-case language, web-facing content, and the coherence of how an app presents itself outside the store all become relevant variables, not as branding considerations, but as functional inputs to discoverability in an environment where the systems mediating search are increasingly doing interpretive rather than purely matching work.

The post How AI is reshaping app store search appeared first on Business of Apps.

Micro-dramas have moved well beyond experimentation. As the market heads into 2026, global competition is shifting from rapid expansion to a more structured phase—where success is driven less by novelty and more by scalable marketing systems, creative efficiency, and long-term IP value.

Against this backdrop, SocialPeta and AMO Pictures jointly conducted an in-depth analysis of the global micro-drama ecosystem. By combining advertising intelligence with production-side insights, the report Insights into global micro-drama app marketing 2026 explores how micro-drama app marketing is evolving—and what will ultimately define sustainable growth in the industry’s next stage.

The report also incorporates first-hand industry perspectives from experts across the short drama ecosystem, including contributors from the Short Drama Alliance, Sooper, and AMO Pictures, offering a more comprehensive view of both market dynamics and execution strategies.

Advertising momentum: Creative scale becomes the bew baseline

By the end of 2025, short-drama app marketing had reached a new level of intensity:

• Over 700 monthly active micro-drama app advertisers, with 63.61% year-over-year growth
• Monthly creatives per advertiser increased by 144.9% YoY
• Clear seasonal peaks in creative output, indicating standardised and repeatable launch cycles

These signals suggest that heading into 2026, creative scale is no longer a competitive advantage — it is a minimum requirement. The key differentiator is shifting toward how efficiently advertisers can test, iterate, and localise creatives across markets.

Top 20 going-global micro-drama  apps by advertising in 2025

Source: SocialPeta

Regional concentration: Core markets still define performance

Advertising activity remains highly concentrated:

• North America leads in advertiser participation
• Europe ranks first globally in total creative volume
• Nearly half of all micro-drama creatives are released in Europe and North America

For 2026, these regions continue to function as revenue anchors and creative testing hubs, where advertising feedback loops are fastest and monetization efficiency is highest. Emerging markets offer growth potential, but mature regions remain critical for validating genres, creatives, and pricing strategies before broader expansion.

Micro-drama app advertising trends in top countries/regions in 2025

Source: SocialPeta

Creative performance: Three structures that consistently convert

SocialPeta’s analysis of high-performing short-drama ads reveals three creative formulas that continue to dominate global campaigns and are expected to remain effective in 2026:

Formula 1: Intense conflict + highlight clips + cliffhanger ending

Optimized for the first three seconds, this structure maximizes completion rates and aligns with short-video platform algorithms that prioritize engagement.

Formula 2: Local scenes + Universal emotions + clear calls to action

By reducing cultural friction and clarifying emotional stakes, this formula improves both retention and conversion—particularly in region-specific campaigns.

Formula 3: Plot twists + hard-hitting lines + dynamic editing

Strong “memory points” increase shareability and organic distribution, helping ads break beyond paid traffic into broader audience segments.

Across all three structures, one pattern is consistent: emotional clarity outperforms narrative complexity in driving acquisition performance.

Formula 3: Plot twists + hard-hitting lines + dynamic editing

Source: SocialPeta

Audience-first, data-driven IP at scale

As marketing systems mature, production studios are also redefining their position in the short-drama value chain. AMO Pictures illustrates how vertical drama studios are transitioning from execution partners to IP-driven media companies.

Operating across 55+ countries and generating over 3 billion monthly views, AMO has built scale through standardized production pipelines and performance-oriented storytelling. In 2025 alone, the studio produced 55 vertical dramas and launched 24 proprietary IP titles.

At the core of this strategy is audience-first development. Continuous analysis of short-form performance data highlights consistent behavioral patterns:

• The first seconds determine success or failure
• Emotion consistently outperforms exposition
• Injustice, empathy, and suspense drive retention
• Sustained momentum is critical in vertical viewing environments

Data informs decisions across scripting, pacing, casting, and editing, creating a feedback loop between audience behavior and creative execution. Each IP is developed with multi-platform deployment in mind, allowing flexible editing and broader monetization across ecosystems.

For AMO, owning IP is not about isolated hits, but about building scalable, long-term content assets that compound in value as the market matures.

Key takeaways

Source: Amo Pictures

The full report spans over 30 pages, providing detailed analyses of top-performing short drama apps, trends in advertising creatives, and exposure strategies for micro-dramas across key global markets.

Download the full report to explore actionable insights for micro-drama app marketing in 2026.

The post From scale to sustainability: How short drama app marketing will be redefined in 2026 appeared first on Business of Apps.

At Business of Apps Berlin 2025, Omri Argaman, Co-Founder and CMO at Zoomd, made the case for looking elsewhere. Zoomd helps brands run user acquisition campaigns outside the dominant platforms — across programmatic exchanges, OEM placements, affiliate networks, and regionally strong social channels, brought together into a single execution layer. The logic is simple: when the largest ecosystems get expensive, you need somewhere else to go.

Beyond the familiar

The appeal of the major platforms is largely psychological. Teams know how to build campaigns there, how to read the signals, how to push toward an objective. That familiarity makes everything feel lower-risk.

Outside those environments, execution is harder and oversight demands are higher. But the audience supply is still there. Device manufacturers, programmatic exchanges, affiliate networks, and regional social platforms all aggregate users at real scale.

The constraint isn’t reach — it’s the integration and management complexity that comes with operating across fragmented channels. For advertisers on performance contracts, where spend has to justify itself against outcomes, that complexity is a solvable operational problem rather than a reason to stay put.

Where you operate determines what works

Acquisition performance varies enormously by region, shaped by payment infrastructure, regulation, and how mature a given category is in a given market. And these aren’t simply surface-level differences that a global playbook can smooth over.

In Latin America, for example, fintech growth is driven by structural gaps in banking. In Mexico, multiple digital wallets compete at scale because large parts of the population have never had meaningful access to traditional financial services. The demand is infrastructural, existing independently of any marketing cycle.

In the US, regulation shapes category economics in ways that are easy to underestimate. Certain social casino formats have lost momentum while sweepstakes-based formats have grown. Short-form drama subscription apps have also gained ground, building recurring revenue around episodic mobile viewing. What works in one market often reflects conditions that simply don’t exist somewhere else.

App growth beyond Meta, Google, TikTok

Source: Business of Apps via YouTube

AI as execution infrastructure

Artificial intelligence is changing performance marketing at the operational level rather than the strategic one. Automation handles media buying across channels at volumes that would otherwise require much larger teams. Predictive systems can model performance ranges before capital is fully committed, changing how scaling decisions are made. Creative generation tools speed up asset production on platforms where fatigue sets in within days.

For teams managing multi-million-dollar monthly budgets, the practical effect is expanded bandwidth. More campaigns running simultaneously, more tests in flight, more granular alignment between creative variants and audience segments. The strategy doesn’t change, but the capacity to execute it does.

New inventory, new contexts

Retail and platform media are starting to move into app install advertising. Amazon and Microsoft are building install-focused products. Twitch has introduced app install formats for iOS. These platforms come with something the open web largely lacks: authenticated users and first-party behavioural data at scale.

Their entry into direct response acquisition reflects a broader convergence between commerce, entertainment, and performance advertising, and for advertisers, it opens up inventory tied to specific behavioural contexts rather than broad audience targeting. Whether it fits a given strategy depends on category and market, but the direction is clear.

Planning around attention

Global events compress and redirect attention in ways that distort normal acquisition economics. The FIFA World Cup — spanning three host countries and an extended competition window — defines the whole year around it, for example, not just the weeks of the tournament itself.

For brands without official sponsorship, the opportunity lies in preparation. Building targetable audiences ahead of peak periods reduces dependence on inventory that gets expensive fast when demand spikes. The brands that benefit most from these windows are usually the ones who started planning before they felt urgent.

The system matters more than the channel

App acquisition is still outcome-driven. What’s changed is the environment around it — dominant platforms concentrating demand, regional conditions fragmenting opportunity, creative cycles compressing, new inventory formats emerging from unexpected directions.

Growth that holds up over time tends to come from teams that have built systems capable of handling that complexity: integrating fragmented supply, adapting to local constraints, deploying automation where it adds capacity, and planning ahead of the moments when everyone else is reacting.

The post Growth beyond Google, Meta, TikTok appeared first on Business of Apps.

AI adoption in SaaS isn’t just hype it’s reshaping how teams build products, prioritise features, and define competitive differentiation. Based on a cross‑industry survey of founders, product leaders, and operators, this article highlights how SaaS teams are thinking about AI, what opportunities they see, and how they are validating AI‑powered ideas today.

To understand how SaaS companies are building with AI today, we surveyed founders, product managers, and operators from sectors including healthcare, marketing, sports tech, IoT, B2B tools, and platform‑as‑a‑service products. Our goal wasn’t to glorify AI or warn against it, but to map how teams are learning, and adapting in real time.

SaaS teams are actively integrating it into products and workflows. But adoption is uneven. Technical and non‑technical founders view AI through different lenses, and teams are cautious about how and when to embed it.

Technical vs. non‑technical founders: Two perspectives on AI

We wanted to speak firsthand to non-technical founders and discuss how they are adapting to AI disruption in their business models. Asking them how confident they feel, where they struggle, and what support they wish they had earlier.

A key finding from the study is that background influences AI thinking:

• Technical founders (engineering, product, development backgrounds) tend to focus on implementation challenges:
  • How AI impacts existing architecture
  • Compute and maintenance costs
  • Data accessibility and control
• Non‑technical founders (business, product, operations) emphasize outcomes:
  • Where AI creates user value
  • How it differentiates the product
  • Concerns about reliability or building the wrong feature

This split isn’t a divide, it’s actually complementary. Together, these perspectives shape how teams prioritize AI in their roadmaps.

Where founders believe AI can help most

Across industries and company stages, the answers consistently pointed toward practical, efficiency-focused applications rather than flashy, experimental AI features. Founders are seeking ways to automate manual tasks, accelerate customer value, and make more informed product decisions.

Internal automation and operational efficiency

• Automating billing, reconciliation, manual workflows
• Supporting QA automation
• Reducing repetitive admin tasks
• Streamlining internal decision‑making

→ This reflects a growing realization: AI doesn’t need to be customer–facing to generate value.

AI‑assisted workflows for productivity

• Automating analysis and insight generation
• Drafting, summarizing, or tagging content
• Reducing steps in complex workflows

→ This indicates a preference for tools that remove friction rather than add complexity.

Smarter onboarding and personalized guidance

• Adaptive onboarding
• Context‑aware recommendations
• Intelligent user support

→ This aligns with broader SaaS trends: the faster users achieve value, the better retention and product engagement become.

Predictive analytics and customer insights

• Forecasting trends
• Identifying risk or opportunity signals early
• Informing product or business decisions

Users can easily integrate AI tools that enhance automation and work as a decision support.

Where do you think AI could make the biggest impact on your product?

Source: Designli

The core worries behind AI adoption

When asked about their biggest concern, founders didn’t focus on whether AI is “trendy”, they focused on risk, clarity, and execution.

• Building the wrong thing too early: Wasting time on unvalidated AI features
• Maintenance and scalability: Ongoing costs, model updates, and reliability
• Overcomplicating the product: Introducing friction or confusing UX
• Security and data privacy: Particularly around sensitive data and compliance and protecting user trust when introducing AI into core workflows.

These concerns suggest that founders aren’t resistant to AI, they are just cautious. They want clarity on timing, ROI, and execution risk before fully committing.

Signals that make founders feel it’s “time” for AI

• There is clear user demand or repeated requests for a specific AI-driven capability.
• The use case ties directly to measurable outcomes, such as time saved, costs reduced, or engagement improved.
• The team has clean, reliable data and a clear integration path.
• AI supports an existing workflow rather than introducing an entirely new one.

For teams navigating AI decisions today, the pattern is clear: AI adoption isn’t about moving first, but intentionally, when the risk is manageable. They are deliberately cautious, waiting for clearer signals around ROI, readiness, and integration complexity.

Our complete guide, which explains the most effective ways to add AI to your app, is available here.

How founders prioritize features and shape roadmaps

We asked founders how they decide what to build next, how much AI trends influence those decisions, and how teams validate AI-powered ideas before committing to them.

• Customer feedback and requests
• Internal workflow pain points
• Revenue or retention impact
• Competitive pressure

Founders emphasized practical needs over theoretical innovation. AI trends influence awareness and discovery, but they rarely drive decisions alone.

This underscores that leaders ask not “Can we build this?” but “Should we build this now?”

How teams prototype and validate AI features

Founders approach AI experimentation with methodology. Key priorities when prototyping:

• Speed of learning: Early insights over perfect products
• Vision alignment: AI must fit with what the product stands for
• UX clarity: AI interactions should feel obvious, not magical
• Ethical considerations: Trust matters even in early tests

Common validation methods include internal dogfooding to catch issues early, limited betas to observe real user behavior, and public iteration to shorten feedback cycles. Rather than obsessing over perfect releases, effective teams iterate based on direct user feedback and measurable outcomes.

How do you test new AI features before launch?

Source: Designli

AI features founders want, but haven’t built yet

When asked about AI capabilities, founders wish they had.

Intelligent automation:

• Auto-generated task lists from natural language
• Workflow automation to reduce manual effort

Smarter insights and summaries:

• Performance recaps and decision-support tools
• Clear dashboards that surface what matters most

UX-enhancing capabilities:

• Touchscreen memory
• Context-aware interfaces
• Simpler, more intuitive experiences powered by AI

These features remain unbuilt due to a lack of technical confidence or capacity, unclear ROI vs. effort, and the risk of over-automation.

Founders are not short on AI ideas, but they are disciplined about execution. Many teams recognize the potential of intelligent automation and insights, but choose to wait until they have the right technical foundation, cost clarity, and confidence that the feature will genuinely improve user outcomes rather than add complexity.

Download and read our full report on how AI reshapes SaaS product strategy, and get the insight of founders, operators, and product leaders across industries.

Scaling challenges and product differentiation

Founders described scaling as a growth and execution challenge, not a product-idea problem.

• Customer acquisition at scale: Moving from early adopters to a repeatable, predictable growth engine.
• Sales and go-to-market clarity: Defining who the product is really for and how to sell it consistently.
• Resource constraints: Limited engineering, operational bandwidth, or funding slowing momentum.
• Internal alignment: Balancing investor expectations, roadmap pressure, and team capacity.

Interestingly, AI was rarely cited as a primary differentiator. Instead, teams focused on niche specialization, workflow and UX improvements, strong service and support quality, and simplicity over breadth. Across the survey, a clear pattern emerges: scaling is not a product idea concern but a growth and execution challenge. AI can enhance differentiation, but it does not replace the fundamentals of product–market fit and user experience.

Intentional AI, not automatic AI

AI is now a permanent part of the SaaS landscape. Used well, it can automate repetitive tasks, surface insights faster, and remove friction for both internal teams and end users. Ignored entirely, it becomes a missed opportunity. Overused or rushed, it can just as easily introduce complexity, inflate costs, and distract teams from solving the right problems. That balance matters.

We believe AI works best as an enabler, not a headline feature by default. Just because AI can be added to a product doesn’t mean it should sit at the front of the roadmap or define the core value proposition. In many cases, the most impactful AI implementations happen quietly in the background, improving workflows, accelerating decision-making, or reducing operational drag without fundamentally changing how the product feels to users.

If you are ready to learn more about AI and software development, Designli is ready to help. Schedule your consultation.

The post AI disruption in 2026: What SaaS founders are actually doing appeared first on Business of Apps.

As the global mobile gaming market shifts from rapid expansion driven by new user acquisition to an increasingly competitive battle for existing users, 2025 marks a decisive turning point for marketers worldwide.

Against this backdrop, SocialPeta, in collaboration with Singular and Aarki, officially releases the Mobile gaming marketing trends whitepaper 2026. The report is powered by core data support from Funtap Games and enriched with exclusive insights from industry leaders, including Rzain Consulting, Two & Half Gamers, Gamigion, Playable Factory, AdQuantum, Mobidictum, asomehdi.com, Yodo1, Gamelight, Reforged Labs, and Jampp.

The white paper offers an in-depth view of the latest global mobile game marketing trends, uncovering several critical shifts:

From traffic competition to refined operations

Source: SocialPeta

Advertising analysis of global mobile games by genre, 2025

Source: SocialPeta

Marketing analysis of mobile games in top regions, 2025

Source: SocialPeta

Mobile game UA shifts from “volatility” to disciplined growth

Gaming user acquisition in 2025

Source: Singular

Source: Singular

A full-fnnel growth framework redefining the future of mobile gaming

Source: Aarki

The fantastic five

Source: Aarki

The new rules of retention:

• Retention starts on Day 0: Build engagement directly into onboarding to demonstrate value before the first purchase
• Measure relationship depth, not just reach: Focus on engagement frequency, session depth, and reactivation cost to assess true user value
• Refresh before fatigue sets in: CTR can drop by up to 45% after four exposures without a refresh, making proactive iteration essential
• Reactivation is R&D: Winning RT insights should continuously feed back into UA optimization
• Scale what works, pause what plateaus: Stable reactivation budgets lead to smoother ROI curves and lower volatility

Aarki emphasizes that future competitive advantage lies in building ecosystems of user nurturing rather than traffic consumption, enabled by unified data, creative intelligence, and disciplined pacing.

Competitive dynamics and creative trends in 4X games

In 2025, strategy games continued to anchor the global mobile market, driven by high user value and long lifecycles. Among them, 4X strategy games demonstrated exceptional resilience through gameplay hybridization and refined live operations.

Drawing on its global publishing experience, Funtap Games outlines the early indicators required for global scalability. To remain competitive, D1 retention must reach 30–35% (35–40% on iOS), while D7 retention should exceed 15–18%. Titles with D1 below 25% face structural disadvantages in paid UA auctions.

Social integration proved decisive. More than 60% of players must join an Alliance before Day 3 to sustain healthy D7 retention. Alliance participants generate 3–4 times higher playtime than solo players, reinforcing the central role of social mechanics.

The competitive scorecard

Source: Funtap Games

Playtime behavior analysis for the first week

Source: Funtap Games

Analysis of popular ad creatives for SLGs

Source: SocialPeta

Case studies and market coverage

The report also analyzes the full-year marketing cycles of four highly representative titles:

• MapleStory: Idle RPG (classic IP idle RPG)
• New Mahjong Wonders
• Gossip Harbor, a top-performing merge title
• Kingshot, a breakout casual SLG hit throughout 2025

Download your copy today and stay ahead in an increasingly competitive mobile gaming market.

The post Mobile gaming marketing trends whitepaper 2026 appeared first on Business of Apps.

Marketing measurement is still built on convenience.

Clicks are easy to report. Attribution dashboards are easy to screenshot. But neither answers the structural question boards are increasingly asking: what growth did media create, and what would have happened anyway?

In a recent App Talk with David Murphy, Dane Buchanan, Chief Data & Analytics Officer at M+C Saatchi Performance, positioned incrementality not as a technical upgrade, but as a shift in accountability. Attribution describes correlation. Incrementality isolates causation. The distinction becomes material the moment budget scrutiny intensifies.

Correlation is not growth

Sales movements rarely result from a single driver. Promotional cycles, seasonality, and external demand all contribute to fluctuations. Black Friday illustrates the point: revenue increases regardless of paid activity. Media may amplify that effect, but it does not generate the entire uplift.

If a campaign coincides with a 10% sales increase, some portion of that lift reflects baseline demand. Incrementality isolates the additional contribution of media. Without that separation, correlation is mistaken for causation.

The objective is not to capture consumers who were already intent on purchasing. The objective is to influence those who were not. Measurement frameworks should reflect that commercial logic.

When measurement becomes fragmented

Most organisations operate multiple measurement systems in parallel: platform attribution, media mix modelling, and controlled incrementality testing. Each method may be technically robust. In practice, they are often owned by different teams and produce conflicting interpretations.

Fragmentation shifts effort away from allocation decisions and toward reconciliation. The consequence is slower optimisation and weaker strategic clarity. A unified framework, by contrast, aligns modelling approaches into a single decision environment, reducing ambiguity around what is working, why it is working, and how budgets should adjust.

Measurement should simplify decisions, not complicate them.

Measuring real-world impact in app marketing

Source: Business of Apps via YouTube

Privacy volatility and the need for continuity

The introduction of App Tracking Transparency altered access to user-level data. Regulatory changes continue to reshape data availability across regions. When measurement depends heavily on deterministic identifiers, reported ROI can shift overnight.

An aggregated, privacy-resilient approach mitigates that volatility. By modelling performance without reliance on individual-level tracking, brands retain continuity over time. ROI measured today remains comparable with future performance, regardless of regulatory evolution.

That continuity carries internal implications. Marketing leaders frequently defend budget allocations to CFOs and CEOs. If reported returns fluctuate due to methodological changes rather than business reality, credibility erodes. Stable measurement frameworks protect that credibility.

AI as infrastructure, not label

Artificial intelligence operates within this system as an enabler rather than a headline feature. Model development benefits from accelerated iteration and automation, shortening the cycle between hypothesis and validation while maintaining human oversight.

Generative systems extend this further by translating model outputs into structured insights. Rather than simply presenting statistical results, the platform surfaces implications and recommended actions. The value lies in compressing the interval between detection and decision.

Capabilities in this area continue to advance, particularly in development velocity. Features that previously required extended engineering cycles can now be tested and deployed more rapidly.

Expanding the scope of ROI

One brand using the platform evaluated media impact exclusively against online sales, despite operating significant offline channels. When offline impact was incorporated into the modelling framework, measured ROI increased threefold. The underlying performance had not changed. The definition of performance had expanded.

With a more comprehensive view, the brand shifted from conservative allocation to confident scaling. The same framework enabled pre-campaign forecasting, allowing projected impact to inform budget decisions before spend was committed.

Improved measurement does not guarantee favourable outcomes. It may reveal inefficiency or overinvestment. That transparency is equally valuable. Capital allocation improves when incremental contribution is visible, whether positive or negative.

From reporting to decision support

The emphasis throughout the discussion was not on dashboards but on decision architecture. Measurement systems should provide a stable, privacy-compliant view of incremental impact, integrate multiple modelling approaches, and support forward-looking budget decisions.

When growth is defined as incremental contribution rather than attributed activity, media investment becomes easier to defend, optimise, and scale.

The post Measuring real-world impact in app marketing appeared first on Business of Apps.

In app growth, failure is rarely sudden. It announces itself early, often quietly, through patterns that experienced operators learn to recognize long before a chart collapses or a budget runs dry. The difficulty is not that these signals are obscure, but that they are easy to rationalise away while momentum still exists.

That diagnostic perspective shaped a recent App Talk recorded at Business of Apps Berlin 2025, where Samet Durgun, better known as the Growth Therapist, approached app growth less as a problem of tactics and more as a system that either supports durable outcomes or undermines them. Rather than prescribing optimisations, he focused on the recurring conditions that separate leaders from laggards across categories, business models, and stages of maturity.

What follows is not a checklist of tricks, but a map of structural fault lines that tend to appear well before performance collapses.

Cash flow as a structural constraint, not a finance problem

One of the first warning signs appears long before campaigns collapse or retention curves flatten. It shows up in timing.

App businesses, particularly subscription-led ones, live with a structural delay between revenue generation and payout. That lag, often stretching to six weeks via app stores, turns scale into a risk multiplier if it is not explicitly modelled.

“You are already spending money that you have not really received yet,” Durgun noted, pointing out that growth decisions made without a reliable LTV baseline can burn future runway. The danger is not ignorance, but optimism. Teams assume the next payout will arrive in time, until it doesn’t.

Cash flow discipline, in this framing, is less about austerity than visibility. If growth requires bridging gaps, those gaps need names, numbers, and contingency plans, not faith.

Retention as the only metric that compounds

Retention is often discussed as a post-install metric, something to be optimised once acquisition is stable. That sequencing misses the point.

From the first app open, retention is already being shaped by onboarding friction, paywall clarity, perceived value, and the coherence of the initial experience. “It’s not just day seven or day thirty,” Durgun observed. “It’s everything from the first second to whether someone still cares months later.”

“It’s not just day seven or day thirty. It’s everything from the first second to whether someone still cares months later.”

Rather than chasing absolute benchmarks, he argued for working within healthy ranges, informed by credible industry medians and top-quartile data, rather than averages that flatten meaningful variation. Retention, treated this way, becomes less a single KPI and more a continuous diagnostic signal.

Diagnosing broken app growth

Source: Business of Apps via YouTube

Instrumentation as strategy, not plumbing

Event setup is usually framed as a technical prerequisite rather than a strategic decision. In practice, it determines what a growth system is capable of learning.

Many apps optimise around signals that are convenient rather than meaningful. A trial start, for example, may look like progress while masking immediate cancellation behaviour. More predictive signals often sit a step later in the journey, requiring deliberate definition and consistent transmission across analytics, attribution, and media platforms.

Instrumentation failures tend to surface indirectly. Teams struggle to explain performance differences between channels, cannot reconcile app store data with MMP reports, or adjust campaigns without understanding which behaviours actually correlate with revenue. These problems rarely originate in media buying. They begin upstream, in decisions about what the system is allowed to measure.

Attribution as an organising system

Attribution problems rarely announce themselves. They accumulate through small inconsistencies: delayed signals, missing parameters, revenue visible in one system but not another.

What stood out was how often these gaps persist even at scale. “I still see companies doing millions,” Durgun said, “but not really knowing which channel is driving what.”

The issue is rarely tooling. Most stacks already contain the necessary components. The failure lies in orchestration, deciding when and how data moves between product, MMPs, and acquisition platforms. Without that map, optimisation becomes guesswork disguised as analysis.

Creative scale and the illusion of cheap AI

The growing availability of AI-generated creative has shifted the conversation from scarcity to abundance. Volume is no longer difficult to achieve. What has become harder to see is the true cost of producing a winning asset.

Creative production carries opportunity cost, whether the labor is human or machine-assisted. Prompting, iteration, testing, and interpretation all consume time and attention. Measuring success by output count obscures the more relevant question: how much did it cost, in total effort, to arrive at a creative that actually performs?

The more useful metric is not cost per asset, but cost per winner. Without that lens, teams risk optimising for throughput while eroding emotional resonance, mistaking speed for effectiveness.

Spend without learning is not scale

Statistically significant data requires investment, but feeding algorithms without clear hypotheses quickly becomes wasteful. Spend that is not designed to generate learning is indistinguishable from spend that simply fails.

The warning sign here is activity that increases complexity without increasing clarity. More campaigns, more creatives, more channels, paired with diminishing insight into what actually works. Growth becomes louder but less informed.

Product-market fit as an ongoing test

The final signal is also the most familiar, and often the most misinterpreted.

Product-market fit is frequently treated as a milestone crossed early, then assumed permanent. In reality, it degrades unless actively maintained. Markets shift, expectations rise, and user tolerance narrows.

Apps built from genuine personal friction tend to recalibrate faster, not because they are purer, but because their creators feel the problem directly. “If you would not use your own app,” Durgun said, “it’s very hard to know when it stops helping.”

“If you would not use your own app. It’s very hard to know when it stops helping.”

Focus as a growth strategy

The conversation ended not with predictions, but with restraint.

Rather than chasing new tactics, channels, or formats, the recommendation for the year ahead was focus. Focus on what already works. Focus on the strongest product. Focus on signals that matter.

Referencing Jeff Bezos, Durgun framed it simply: what matters most is what will not change. Relevance, clarity, and value remain stubbornly resistant to trend cycles.

In growth, as in therapy, progress often begins not by adding something new, but by understanding what is undermining the system already in place.

The post Diagnosing broken app growth appeared first on Business of Apps.

When Viktor Orlov stepped on stage at Business of Apps Berlin 2025, he opened with a small admission. Two years earlier, in the same room, his workshop had tried to explain too much. Too much theory. Too much showing what he knew about Apple’s systems, not enough guidance on what practitioners could actually do differently the next morning.

This time, the ambition was narrower and more practical. Apple Search Ads, Orlov argued, is not a mysterious channel. It is a constrained system with clear incentives. Teams struggle not because it is opaque, but because they optimise for the wrong things.

Why Apple Search Ads still behaves differently

Orlov’s first point was almost unfashionable in a market obsessed with novelty. Apple Search Ads remains one of the strongest acquisition channels in mobile.

The reason is not efficiency, automation, or scale. It is intent.

Search is explicit. A user typing a keyword in the App Store is already close to a decision. “You are working with direct search intent,” Orlov said. “That gives you the highest purchase intent across the whole marketing mix.”

“You are working with direct search intent. That gives you the highest purchase intent across the whole marketing mix.”

That intent creates stability. Prices fluctuate less violently than in social channels. Volumes do not collapse overnight. Creative fatigue exists, but it is not the dominant constraint. Apple Search Ads rewards correctness more than novelty.

At the same time, the channel has not evolved in ways that make it easier to operate. There are still no native post-install integrations. There is no meaningful automation. Everything that matters is manual.

Which is precisely why discipline matters.

Control is not optional

Rather than starting with bids or budgets, Orlov began with structure.

The default way many teams set up Apple Search Ads accounts bundles dozens or hundreds of keywords into a single ad group, tied to a single bid. It feels efficient. It is not.

The alternative, single-keyword ad groups, feels tedious until the logic becomes clear. Control is not an abstract benefit. It is the only way to understand what is actually happening inside the auction.

“The more control you have, the better it works,” Orlov said. “Control is the biggest success factor in Apple Search Ads.”

“The more control you have, the better it works. Control is the biggest success factor in Apple Search Ads.”

Control enables relevance. Relevance determines whether Apple allocates impressions at all.

Apple Ads strategy for stable growth and higher ROI

Source: Business of Apps via YouTube

The competitor most teams misunderstand

When Orlov asked the room who their biggest competitor was in Apple Search Ads, the answers followed a familiar pattern. Other advertisers. Big brands. Aggressive bidders.

They were all wrong.

“The most important competitor is the number one organic result,” he said.

“The most important competitor is the number one organic result.”

Apple Search Ads auctions are not fought in isolation. Paid ads compete directly against organic listings for attention, credibility, and clicks. If the top organic result is more relevant than your ad, Apple has little incentive to show you frequently, regardless of bid.

This framing shifts the optimisation question. The task is not to outbid other advertisers, but to outperform the best organic alternative for that query.

Why IPM quietly governs scale

The centrepiece of Orlov’s talk was not CPI, ROAS, or any familiar executive dashboard metric. It was IPM: installs per thousand impressions.

IPM is not something Apple exposes as a first-class metric, but it sits at the heart of the auction. It combines tap-through rate and conversion rate into a single signal of relevance.

“Apple doesn’t use IPM directly,” Orlov explained. “But it uses the components of it to decide who deserves impressions.”

“Apple doesn’t use IPM directly. But it uses the components of it to decide who deserves impressions.”

Below a certain threshold, scale stalls. Impression share caps out. Campaigns feel artificially constrained. Cross that threshold, and something changes. Impression allocation accelerates. Volumes grow faster than spend. CPIs begin to fall instead of rise.

Internally, Orlov described this threshold as an “invisible wall.” The auction does not reward incremental improvement evenly. It rewards relevance disproportionately.

Custom Product Pages as relevance engines

Improving IPM requires changing what users see, not what teams bid. This is where Orlov was most direct.

“Custom Product Pages are the most powerful tool in Apple Search Ads,” he said. “And almost nobody uses them properly.”

“Custom Product Pages are the most powerful tool in Apple Search Ads. And almost nobody uses them properly.”

Despite Apple expanding the limit to 70 Custom Product Pages per app, most teams treat them as a nice-to-have. Design resources are scarce. Roadmaps are full. Gains are assumed to be marginal.

In practice, CPPs are where relevance becomes tangible. Matching messaging, visuals, and onboarding flows to specific keyword intent changes how the auction evaluates your ad. Conversion improves. IPM rises. Impression share follows.

Deep links extend this logic. Sending users directly to the feature, offer, or flow they searched for shortens the path to value. It also signals alignment to Apple’s system.

“You’re not just improving conversion,” Orlov noted. “You’re improving how Apple understands your relevance.”

“You’re not just improving conversion. You’re improving how Apple understands your relevance.”

Pricing is an outcome, not a lever

On auction pricing, Orlov pushed back against the idea that competition alone drives CPI.

For apps with in-app purchases, Apple has near-perfect visibility into revenue. Keyword prices reflect average profitability across the category. For apps without IAPs, Apple estimates. And it estimates well.

“If a keyword is not profitable for you,” Orlov said, “it means it’s profitable for someone else.”

“If a keyword is not profitable for you, it means it’s profitable for someone else.”

Apple Search Ads functions as a performance benchmark. When campaigns fail, the explanation is rarely that the channel is broken. More often, the execution does not yet match the economics of the category.

Knowledge as the missing layer

Tools, Orlov argued, have converged. Most platforms offer similar features, similar dashboards, similar promises. What does not converge easily is understanding.

“The knowledge is the most important factor,” he said. “Understanding how the auction works is what creates results.”

“The knowledge is the most important factor. Understanding how the auction works is what creates results.”

Across the case studies he shared, spanning fintech, utilities, mobility, and subscriptions, the pattern was consistent. When relevance improved, prices fell. When IPM rose, scale followed. Category mattered far less than execution.

Apple Search Ads is not forgiving, but it is predictable. Teams that learn how its incentives align are rewarded not with tricks, but with compounding performance.

A channel that rewards seriousness

Apple Search Ads does not reward haste. It does not reward shortcuts. It rewards teams willing to treat it as a system worth understanding.

Orlov’s argument was not that Apple Search Ads is easy. It was that it is honest. If results disappoint, the feedback is already there, embedded in impression share, relevance, and conversion.

The work is not glamorous. It is also difficult to fake. And for teams willing to engage with it properly, that may be its greatest advantage.

The post Apple Ads strategy for stable growth and higher ROI appeared first on Business of Apps.

In the most recent episode of Branch’s How I Grew This podcast, Mick Rigby, Founder and CEO of Yodel Mobile, reflected on nearly two decades of building a specialist app growth agency in a market that rarely rewards restraint.

His account was not framed around hypergrowth or tactical advantage, but around a quieter set of decisions that compound over time: when to refuse work, how to grow without overextending, and why human judgment becomes more valuable as automation accelerates.

Refusal as an operating principle

Rigby treats the ability to say “no” as a structural capability rather than a personality trait. At Yodel Mobile, refusal is used to protect clarity of purpose. The agency does not work with gambling or gaming businesses, not as a moral stance, but as a boundary around what the company exists to support. That boundary removes ambiguity, both internally and externally, and prevents the gradual erosion that often follows opportunistic client acquisition.

The same discipline applies to service expansion. Yodel regularly turns down requests to deliver work outside mobile and app growth. Rigby acknowledges that the agency could take on adjacent digital services, but argues that doing so would weaken the specialist expertise that defines the business. Saying “no”, in this context, preserves quality rather than limiting opportunity.

Growth at a speed values can tolerate

Refusal only works when a business is not structurally dependent on every marginal deal. Rigby is explicit that the early years were financially constrained, and that the temptation to chase revenue was constant. His response was to grow slowly, prioritising cash flow stability over aggressive scale. That pacing reduced the number of moments where principles had to compete with payroll.

This approach also shaped how Yodel Mobile invested internally. Specialist skills were developed in-house and retained over long periods, allowing knowledge to compound rather than reset. Rigby frames retention as a strategic decision rather than a cultural aspiration. In a specialist agency, experience that leaves is value that disappears.

Learning before the market demands it

Several of Yodel Mobile’s inflection points came from investing ahead of demand. Rigby points to App Store Optimisation as an example. Long before ASO was formalised as a discipline, the agency allocated time to learning and experimentation, often at the expense of short-term utilisation. When the market caught up, Yodel Mobile was already positioned to deliver.

A similar pattern emerged during the first year of the pandemic. Rather than reducing headcount, the agency retained staff and used the disruption to reassess its service offering. That period became an opportunity to improve delivery and prepare for the next phase of growth, rather than a retreat into defensive cost-cutting.

Scaling through alignment, not exit

Yodel Mobile’s acquisition by NP Digital was driven less by personal exit considerations and more by organisational constraints. Rigby describes a point where internal leaders were approaching a ceiling, not because of performance, but because the structure could no longer support their progression.

The search for a buyer focused on alignment rather than valuation alone. Rigby wanted a partner with global reach, shared values, and a willingness to preserve what made Yodel Mobile distinct. Post-acquisition, he describes greater access to expertise and perspective, particularly across international markets, without a loss of identity.

AI and the rising value of judgment

Rigby is pragmatic about AI. He sees it as inevitable and already useful, particularly for data-heavy tasks and analysis. He is equally clear about its limits. In his view, strategic judgment, relationship-building, and contextual understanding remain human advantages, especially in service-driven businesses.

As a result, Yodel Mobile is investing deliberately in skills that do not compress easily into automation. Client communication, empathy, and the ability to interpret nuance are treated as assets that will appreciate rather than depreciate as AI adoption increases. The machines may handle execution more efficiently, but the responsibility for meaning, prioritisation, and trust remains human.

A quieter definition of success

Rigby’s account offers a counterpoint to the dominant growth narrative in app marketing. Success, as he defines it, is not measured solely in scale or speed, but in durability. A business that can retain its people, refuse work that does not fit, and evolve without abandoning its principles may grow more slowly, but it also becomes much harder to destabilise.

After nearly twenty years in the industry, Rigby’s pride does not come from a single metric or milestone. It comes from having built an environment where others could grow, make decisions with confidence, and carry those skills forward, whether at Yodel Mobile or beyond.

Tune in below to listen to the episode, or check out all the episodes here.

The post Why saying “no” is the key to long-term success appeared first on Business of Apps.

After a full day of talking growth loops, creative strategy, and distribution, the conversation at Business of Apps Berlin 2025 snapped to the moment that ultimately decides whether any of it matters: the paywall.

Peggy Anne Salz sat down with Lanre Akinyemi, VP of Growth at Superwall, to unpack what’s changed in paywall optimisation and why the teams winning in 2026 won’t treat monetisation as a static screen, but as a living system they can iterate daily.

Paywalls used to be slow, expensive, and ignored

Lanre’s starting point was blunt. App teams spend months building value, acquiring users, and driving engagement, then underinvest in the highest-stakes touchpoint in the entire journey.

The reason is operational, not strategic. Traditionally, changing a paywall means design work, development work, App Store submission, waiting through review, and then waiting again for users to update. If the change fails, the process resets. That friction creates a hidden tax: experimentation becomes rare, slow, and politically difficult.

Superwall’s pitch is to remove that constraint. Once the SDK is installed, paywall design changes, segmentation, and flow logic can be configured remotely without shipping a new app release. That shifts paywalls from “something we rebuild every few weeks” to “something we can test continuously”.

Treat the paywall like a landing page

Lanre described a simple pattern he sees across top performers: they don’t treat paywalls as generic UI. They treat them like high-intent landing pages.

That means relevance matters. If a sports app knows a user is engaging with soccer content, showing baseball creative on the paywall is a self-inflicted conversion penalty. The goal is to align the paywall to the “magic moment” as closely as possible, aka the specific context that made the user care.

Entry points matter, and the debate is messy

Peggy pushed on a tension every subscription app recognises, namely that showing a paywall early can feel aggressive, but moving it later can miss the peak conversion moment.

Lanre acknowledged the trade-off. Many apps still see their highest conversion rate immediately after onboarding, because intent is fresh and the user is primed to say yes. He also pointed to a behavioural dynamic teams often ignore. People react more strongly to losing access than gaining it. Give users premium features first, then take them away, and renewal likelihood climbs.

But the more important point wasn’t “early vs late”. It was optionality. Different apps, user cohorts, and markets behave differently, which is why the only durable strategy is testing.

Why your paywall is your most important growth level

Source: Business of Apps via YouTube

Localisation isn’t translation, it’s persuasion

One of the more concrete sections of the conversation focused on localisation, not just in language but in what motivates trust.

Lanre cited Japan as a market where social proof consistently outperforms feature-heavy pitches or short-term discounts. Many successful paywalls there are deliberately long, built around testimonials and reviews to establish legitimacy. In the US, the same app may find that a strong sale framing lifts conversions more reliably.

The implication is uncomfortable but useful: there isn’t a universal “best paywall”. There are only hypotheses that survive contact with a specific audience.

Packaging and choice can lift conversion

Lanre shared an experiment pattern that keeps showing up: giving users structured choice can materially improve conversion.

One example was “choose your trial style” using introductory offers — such as paying a small amount for 30 days of access versus taking a shorter free trial that rolls into an annual plan. Even when the choice is partly psychological, the feeling of control reduces commitment anxiety, particularly for younger users who aren’t paying but hesitate at subscription lock-in.

The underlying principle is less about clever pricing and more about lowering the emotional barrier to saying yes.

Show the plan you think fits and recover if they decline

Another tactic Lanre highlighted was sequencing, aka leading with the plan most likely to convert for a given user, then falling back to a softer option if they refuse.

For example, show an annual offer first to high-propensity segments, then present a monthly plan as a recovery path. What matters here is not the exact plan order, but the ability to implement the logic quickly and iterate without a two-week dev cycle.

Retention and winback are part of the paywall system

The conversation then moved beyond first conversion. Lanre described winback as a natural extension of paywall infrastructure.

If a user is still reachable, teams can re-open a monetisation flow through push notifications that deep-link into a specific recovery paywall. More advanced programs use lifecycle email to deliver an offer that opens directly into the app at the right paywall screen, avoiding “random place” journeys that break intent.

The point is that paywalls aren’t only for new users. They also become a control surface for reactivation.

Web payments are becoming the 2026 monetisation story

Looking ahead, Lanre tied 2026 to a major industry shift: payments outside the app stores are becoming more viable, but only if the UX stays native.

He described Superwall’s recent work to reduce the friction of off-store payments, moving from sending users out to Safari, to an in-app browser, to a drawer-style experience that feels like a native purchase flow and supports Apple Pay.

In internal testing shared on stage, he said the Stripe flow showed near-flat conversion compared with in-app purchase (reported as roughly a -1% change) while avoiding platform fees — an outcome that, if it holds across apps, will reshape how teams think about margin.

Small changes compound

Lanre closed with a reminder that most paywall optimisation is incremental, not miraculous.

Microcopy changes — “Continue” versus “Try for free” versus “Get started for $0” — can move conversion a point or two at a time. That sounds small until you repeat it, month after month, without rewriting the product. The compounding effect becomes the strategy.

Watch the full App Talks episode to discover all of Lanre’s insights. You can also watch all episodes of App Talks here.

The post Why your paywall is your most important growth lever appeared first on Business of Apps.

Source: Business of Apps via YouTube

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As ad platforms become increasingly automated, many of the levers performance marketers once relied on are disappearing. Targeting options are narrowing, optimization controls are abstracted away, and algorithms are doing more of the decision-making behind the scenes. In that environment, one question is becoming critical: what signals are we actually feeding those algorithms?

At Business of Apps Berlin 2025, Shumel Lais, Co-Founder at Day30, unpacked why signal optimization is emerging as one of the last meaningful growth levers for app marketers and how getting it right can materially lower CAC and improve ROAS.

When optimization moves into a black box

Shumel opened by acknowledging the pressure most growth teams are under. Competition is rising, CPMs are climbing, and AI-generated apps are flooding the stores. At the same time, platforms like Meta and Google are becoming more algorithmic and opaque.

Where marketers once tweaked bids, audiences, and structures, today there are far fewer controls. Creative remains important but even that may soon be partially automated. That leaves one major lever still firmly in the hands of marketers: the signal.

What signals really mean to ad platforms

At its core, a signal is the goal event you ask an ad platform to optimize towards — installs, trials, purchases, subscriptions, or renewals. These events teach algorithms what “good” looks like so they can find similar users at scale.

Shumel compared this to a sports scout. Give the scout a clear definition of success — say, players who score ten goals in a season — and they can identify the traits that matter. Feed vague or incomplete goals, and results suffer.

The problem is that on mobile — especially on iOS — signals often stop at day one or day seven. What marketers actually care about, such as renewals, lifetime value, or ROAS, typically happens much later.

The signal visibility gap

Because platforms can’t reliably see beyond the attribution window, many subscription apps end up optimizing to cost per trial rather than cost per subscriber. This creates a disconnect between what platforms optimize for and what businesses actually value.

Signal optimization, Shumel argued, is about closing that gap. The goal is to predict meaningful downstream outcomes — like a day-8 renewal or day-30 ROAS — using behaviors that occur within the first 24 hours.

Signal optimization playbook

Source: Business of Apps via YouTube

A framework for signal optimization

To make this practical, Shumel shared a three-part framework for evaluating signals before spending a dollar of media budget.

The first dimension is precision, or the conversion rate of users who trigger a signal. Higher precision means stronger correlation with the outcome you care about.

The second is signal volume. Algorithms need enough data to learn patterns. A highly predictive signal is useless if it fires too infrequently to sustain optimization.

The third — and often overlooked — factor is recall. A signal with very high precision can still be harmful if it excludes a large share of users who would have converted anyway. In that case, you’re effectively telling the algorithm that good users are bad.

The challenge is finding the right balance between all three.

Why correlation isn’t enough

Shumel illustrated the risks with a real-world example from earlier in his career: a restaurant booking app that optimized campaigns based on early behaviors that appeared correlated with bookings. Despite promising-looking data, performance didn’t improve.

What this shows is that correlation alone isn’t sufficient. Signals must be evaluated within clearly defined cohorts, time windows, and prediction statements — something data science is particularly good at enforcing.

From theory to results

The session moved from theory to practice with examples from real apps.

In one case, a photo storage app attempted to optimize toward users who started a trial and uploaded thousands of photos. While precision improved slightly, recall dropped sharply, excluding over a third of users who would have converted.

By applying machine learning to identify more nuanced early behaviors, Day30 helped the app increase precision by around 40% while maintaining roughly 80% recall, a far more effective signal.

In another experiment across multiple campaigns on Meta, three optimization strategies were compared: installs, trials, and a predictive signal. Each did exactly what the platform was told — cheapest installs, cheapest trials, and, ultimately, the strongest CAC and ROAS performance when optimizing toward the predictive signal.

The takeaway for growth teams

Signal optimization doesn’t replace creativity or instinct. It puts them under scrutiny before budget turns into exposure.

Shumel’s top pieces of advice:

• Start with a clear prediction statement
• Use early behaviors to predict what really matters
• Balance precision, volume, and recall
• Test incrementally before going live
• Iterate continuously as user behavior evolves

As automation increases, the quality of inputs matters more than ever. In a world where platforms optimize perfectly for whatever you ask of them, the real competitive advantage lies in asking the right question.

The post Signal optimization playbook appeared first on Business of Apps.

For years, app monetisation discussions have focused on fill rates, eCPMs, and demand sources. But one critical variable has often been treated as a necessary evil: ad quality. As user expectations rise and competition intensifies, that trade-off is becoming increasingly dangerous.

In the latest edition of App Talks, David Murphy spoke with Alex Yerukhimovich, GM at AppHarbr, about why ad quality is no longer a secondary concern and how bad ads quietly erode retention, revenue, and trust across the app ecosystem.

Why ad quality is becoming a growth problem

Alex describes ad quality as a user experience issue first, not a monetisation one. Users don’t distinguish between app content and ad content. To them, everything that appears on screen belongs to the app.

That perception creates a fundamental risk for publishers. When ads feel deceptive, disruptive, or malicious, users don’t blame ad networks, they blame the app itself.

As Alex explains, this problem has traditionally been most visible in gaming, where high-paying formats like rewarded video have historically justified aggressive ad behaviour. But the challenge is now spreading rapidly into non-gaming apps, where user lifecycles are longer and retention is far more valuable.

The three types of bad ads

AppHarbr categorises ad quality issues into three core buckets:

The first is unwanted content — ads or landing pages that are violent, inappropriate, or simply off-brand for the app.

The second is bad ad behaviour. These ads may look fine on the surface but disrupt the user experience by playing loud audio, triggering vibrations, or hijacking the screen with unskippable full-screen formats.

The third, and most dangerous, category is malvertising. These ads are intentionally deceptive, designed to manipulate users into handing over personal or financial information. Common examples include fake virus alerts, false system warnings, scam investment promotions, and AI-generated deepfake endorsements.

According to AppHarbr’s recent data, one in every 77 ads served in gaming apps is malicious. In non-gaming apps, it’s roughly one in 120 — a gap that is narrowing fast.

Ad quality is hurting retention and app revenue

Source: Business of Apps via YouTube

The hidden cost of “letting it slide”

One of the biggest misconceptions Alex highlighted is the belief that bad ads are tolerable because they pay well. In reality, publishers rarely know whether that’s true.

Without full visibility into their ad inventory, publishers can’t see what happens when a bad ad is blocked. In most cases, Alex says, the second-highest bidder fills the slot at nearly the same price, without damaging the user experience.

The real costs of bad ads show up elsewhere:

• Shortened sessions when users close apps early
• Higher churn and uninstall rates
• One-star reviews that hurt ASO and future installs
• Long-term damage to brand trust and lifetime value

For apps with long-term usage — weather, finance, live scores, utilities — losing even a single loyal user due to ads can outweigh short-term monetisation gains.

From blind spots to full transparency

AppHarbr’s core mission is to give publishers full visibility and control over the ads shown in their apps.

Alex points out that while some ad networks and mediations provide limited reporting tools, most developers still can’t confidently answer basic questions like which ad verticals dominate their inventory or where quality issues originate.

The first step is transparency, or knowing exactly what ads are being served, by which networks, and how users are exposed to them. The second step is enforcement, or the ability to block or reject problematic ads in real time, without relying on slow or incomplete third-party responses.

A smarter ad ops workflow

Beyond protection, ad quality tools unlock new operational and optimisation opportunities.

With granular control, publishers can:

• A/B test blocking specific ads or advertisers
• Segment enforcement by OS, country, or user type
• Balance UX improvements against real revenue impact
• Customise ad experiences for different user cohorts

This allows teams to move away from one-size-fits-all monetisation strategies and toward a more nuanced balance between revenue performance and user satisfaction.

Why good ads actually help monetisation

Alex is clear that ads themselves are not the enemy. Good ads — relevant, clearly dismissible, well-timed, and non-deceptive — enable free content and sustainable business models.

Users are willing to engage with advertising when it respects their time and attention. The goal isn’t to remove ads, but to make them safe, transparent, and predictable.

The bigger picture

As ad quality continues to deteriorate across the ecosystem, Alex argues that passive acceptance is no longer an option. Solving the problem requires both better tooling and a more vocal industry response.

Publishers, platforms, and ad tech providers all share responsibility. If bad actors are consistently blocked and exposed, the incentive structure shifts, improving outcomes even for smaller developers who lack leverage on their own.

The takeaway

Ad quality isn’t a monetisation edge case anymore. It’s a core growth variable.

From retention and reviews to revenue stability and brand trust, the ads inside an app are inseparable from the app itself. As Alex succinctly put it, if the ads in your app are bad, your app is bad.

The post Ad quality is hurting retention and app revenue appeared first on Business of Apps.

For much of the last decade, app growth has been driven by signals marketers could easily see: clicks, installs, sessions, and conversions. But beneath the surface, a far richer layer of data has always existed, one that lives entirely on the device itself. Now, in a privacy-first world shaped by the loss of IDFA, that overlooked layer is becoming one of the most valuable assets in the app economy.

At Business of Apps Berlin 2025, Peggy Anne Salz sat down with Dieter Rappold, CEO and Co-Founder of ContextSDK, to discuss how on-device contextual data is redefining growth, monetisation, and user engagement. The conversation explored why timing may be the most underutilised lever in app growth and how motion and sensor data can unlock intent without compromising privacy.

Why context is having its moment

Dieter describes the current environment as a long-overdue correction. As the industry adjusted to stricter privacy standards, many marketers focused on what had been lost. ContextSDK, by contrast, focused on what was always there.

Smartphones are packed with highly sensitive sensors, generating streams of contextual data around movement, orientation, charging state, battery level, and time of day. This data is freely available on-device, privacy-safe, and incredibly revealing when interpreted correctly.

For Dieter, timing is the key insight. Knowing when a user is most receptive can matter more than knowing who they are.

From device assumptions to real-world behaviour

In the early days, device data was used crudely. A high-end phone might signal affluence. A specific Android model might imply a demographic segment. Today, those assumptions are obsolete.

Instead, ContextSDK looks at users in their natural habitat — how they physically interact with their device throughout the day. By analysing accelerometer and gyroscope data, the ContextSDK platform can identify more than 24 distinct real-world activities with up to 96% accuracy.

That includes whether a user is walking, sitting, lying in bed, relaxing on the sofa, or even whether they are travelling in a combustion-engine vehicle versus an electric one. Each context carries a different level of intent and a different likelihood of engagement or conversion.

Why intent beats identity

One of the most powerful outcomes of contextual data is intent prediction. Dieter shared how simple signals like battery level or charging status can dramatically affect conversion probability though the impact varies by vertical and audience.

In one surprising case, a Gen Z dating app saw no drop in conversion even when battery levels fell below 3%. That insight only surfaced because the data was collected, analysed, and tested.

The lesson is clear: context is not universal. It must be learned, not assumed.

Source: Business of Apps via YouTube

From monetisation to data infrastructure

ContextSDK originally launched with off-the-shelf products like Context Decision and Context Push, designed to help apps optimise monetisation and engagement. Increasingly, however, the company is evolving into a broader data infrastructure provider. Large app businesses are now building their own use cases on top of ContextSDK’s data layer.

One of the most compelling applications is fraud detection. Phones that trigger activity without any corresponding physical movement are often a strong indicator of fraud. By combining motion data with machine learning models, ContextSDK can identify fraudulent behaviour in the very first session, potentially saving millions in wasted user acquisition spend.

A new edge for ad networks

Looking ahead, Dieter outlined how contextual signals could reshape programmatic advertising itself. By surfacing a curated set of “golden signals” from hundreds of available data points, ad networks could dramatically improve bidding performance and targeting accuracy.

In a hyper-competitive and transparent market, even small performance gains can compound across massive spend volumes, creating a meaningful competitive advantage.

Engagement is about timing, not volume

Context doesn’t just inform what to show users, it informs when to connect with them.

Through Context Push, ContextSDK integrates with platforms like Firebase, OneSignal, CleverTap, and Customer.io to deliver notifications based on real-world readiness rather than fixed timestamps.

A phone face-down on a table signals low engagement potential. A user relaxed on the couch, phone unlocked and scrolling, signals the opposite. Delivering messages at the right moment can significantly improve engagement without increasing message volume.

The same logic applies to churn prediction. Shorter sessions, increased usage while in transit, and changes in physical context can all indicate declining intent, long before a user actually cancels.

From curiosity to category-defining layer

When ContextSDK first launched, even its founders weren’t sure how valuable this data would be. As Dieter admitted, it could have quickly proven to be a dead-end or something much bigger.

Today, it is increasingly clear that on-device context represents a missing layer in mobile growth. In a world of rising acquisition costs and fierce competition for retention, ignoring an entire category of first-party data is no longer an option.

Looking ahead

As apps compete not just for installs but for moments of attention, understanding real-world context is becoming essential. The future of app growth won’t be defined solely by who users are, but by what they are doing and when.

Watch the full video to discover all of Dieter’s insights. You can also watch all episodes of App Talks here.

The post The missing data layer in app growth appeared first on Business of Apps.

Wondering what fast-growing GEO to tap into next with your eCommerce project? Studies and our experience point towards Vietnam (VN). This high-reward social commerce market stands out with its mobile-first audience, strong content-driven shopping on TikTok and Facebook.

Need more? Social commerce should soar up to $10.21 billion by 2030, while TikTok Shop is the second-largest growth market in VN, with fashion, beauty, FMCG (Fast-Moving Consumer Goods), and home as the leading verticals.

More below, PropellerAds’s Senior Account Strategist Karlina Berzina‘s scalable TikTok Shop case study – a performing funnel based on on-click and Spark campaigns, wide-test, and zone-level optimization.

What is a TikTok Shop?

TikTok Shop is TikTok’s native eCommerce solution, which makes it easy for users to find and buy products straight from the app. Shoppable videos, live streams, and the Shop tab take users to an in-app product page with prices, product details, and easy checkout.

From content to store

Source: TikTok Shop

The result: less friction and more conversion potential.

Campaign objective and offer strategy

Our advertiser wanted to check which TikTok Shop traffic sources generated the highest-quality buyers. So, he chose several verticals based on the shop’s local demand:

• Beauty (skincare, makeup, tools)
• Clothing (fashion and accessories)
• Gadgets (small electronics and smart devices)

How to: evaluate zones, devices, and browsers first, then narrow into high-performing product clusters.

Phase 1: Wide test

The team launched a broad Onclick campaign in Vietnam with:

• Mobile-only traffic (Android and iOS)
• Full GEO coverage, including all provinces and districts
• In depth filtering across browsers, zones, and OS combinations

Key metrics: CTR and engagement, conversion rate (CR), checkout completion, cost per checkout (CPCO), refund/return risk, and day-to-day ROI stability.

The result: ~80% of traffic came from Chrome and Safari, just as Vietnam’s mostly mobile usage trends indicate (Android Chrome and iOS Safari).

Phase 2: Zone-level optimization

Analyzing the CTR, CVR, CPCO, refund risk, and ROI stability over a 7–14 days period the team managed to identify all the high-sale zones of VN, instead of seeing it as a single GEO.

The outcome: Deprioritizing high-risk or volatile zones and whitelisting profitable ones with custom bids. All of which leads to 20-40% lower spend, more stable CPA, and more focused scaling  over time.

Phase 3: Browser and device segmentation

• Chrome (Android) delivered volume and conversions
• Safari (iOS) showed higher AOV and stronger long-term quality
• Social in-app browsers (e.g., Facebook) drove engagement, but with a bit higher volatility

The solution: separate campaigns for Chrome and Safari traffic, plus a controlled test campaign for Facebook browser traffic, pinpointing performance-drivers.

Bonus: Expanding the funnel

A more in-depth view, with added social traffic from Facebook, Instagram, and other platforms, using SmartCPM, filtered by mobile OS, high-performance browsers, and zone-based data, showed profitable micro-pockets (specific city + browser combinations).

The effect: at first, a clean cost per zone and strong micro-pockets, followed by a safer and more traffic-type-based scaling.

TikTok Shop vs other social commerce funnels

TikTok Shop	Instagram Shop	Pinterest Shopping	YouTube Shopping	Amazon Social-Style Feed
Straight from content to Shop	Product tags or Reels to Shop	From pin/board to product page	Shopping Button or sticker to Store	Amazon’s Inspire was a bust

PropellerAds VN TikTok Shop

Source: PropellerAds

Key takeaways

• Identify micro-markets within large GEOs
• Test wide and refinebased on zone-level data
• Segment by browsers and OS as different audiences
• Add external social traffic to your TikTok Shop

Take this approach to turn TikTok Shop into a predictable and controllable performance funnel, and move through your funnel from testing and optimization to sustainable scaling.

And if you need more help…

Go to PropellerAds.

The post How data-led advertisers turn views into cash appeared first on Business of Apps.

In today’s saturated and fast-moving software landscape, the strength of a company’s value proposition often determines whether it thrives or disappears into the noise. While features, pricing, and technology stacks matter, they’re no longer enough on their own. Buyers are seeking more than tools; they’re seeking outcomes. A clearly defined value proposition is how companies communicate the outcomes they deliver.

A value proposition goes beyond product specs or pricing tiers. It answers the core question: Why should someone choose this app over the countless alternatives? For software companies, particularly SaaS platforms, it clearly shows how a user’s problem connects to a real, measurable fix.

Consider Slack’s positioning: “The collaboration hub that replaces email.” In one sentence, it highlights: The pain (email overload), the solution (centralized communication), and the outcome (greater team efficiency).

A strong value proposition serves as a foundation for every area of the business:

• It aligns product development with customer needs
• It guides marketing and sales narratives
• It helps internal teams prioritize features and roadmap decisions
• Most importantly, it builds trust with users who understand what to expect

What makes a strong software value proposition

In a market flooded with feature-rich software, the companies that stand out are those that shift the conversation from what their app does to what it enables. The most effective value propositions don’t simply showcase capabilities; they promise results that matter.

This distinction is subtle but powerful. A project management tool that lists “task tracking, notifications, and dashboards” offers functionality. But one that positions itself as “a single source of truth for fast-moving product teams” speaks to outcomes. It frames the same features in a way that highlights their real-world impact.

Jira is a prime example. Rather than compete with general task tools, Jira carved out its niche by aligning tightly with software development workflows. Its value proposition is not “we manage tasks”, it’s “we power agile teams”. That clarity attracts the right users and fosters loyalty through relevance.

Similarly, Amazon Web Services didn’t grow by promoting the largest list of cloud services. Its dominance stems from positioning itself as the most scalable, secure, developer-first platform in terms that speak directly to pain points in infrastructure, speed, and flexibility.

Tesla takes this even further. Its vehicles are electric, yes, but its proposition isn’t just about avoiding gas. It’s about “future-ready mobility”, autonomous driving, over-the-air updates, and a vertically integrated user experience. The car is just part of the value.

For software companies, this shift means anchoring messaging in:

• Efficiency gains
• Cost savings
• User empowerment
• Innovation enablement
• Scalability and future-readiness

Bridging product and service: A dual approach to delivering value

As businesses evolve, their technology also needs to. Some require tailor-made software to fit unique workflows. Others benefit from flexible, pre-built tools that efficiently address common challenges. Increasingly, the most forward-thinking software companies are combining both approaches, custom development and proprietary products to maximise client value.

This dual-track strategy allows companies to meet customers where they are, offering tailored solutions when necessary and proven products when speed or cost is a priority. It’s not about selling more; it’s about delivering the right solution to the right problem at the right time.

For example, a company might engage in full-cycle custom development to help an enterprise digitise outdated operations or build a new AI-powered analytics engine from scratch. In parallel, the same company may develop a proprietary product, born out of repeatable client pain points that offer rapid deployment for everyday use cases, like process automation or internal reporting.

This hybrid model strengthens the value proposition in three key ways:

• It enables flexibility: Not every challenge requires custom code to be solved. A modular approach to app offerings allows for scalable, cost-effective solutions.
• It accelerates delivery: In-house tools and frameworks built through past engagements reduce development time for future projects without compromising quality.
• It reflects deep understanding: Companies that can both build and productize are often those that understand their market intimately. They’ve seen the patterns and designed tools to match.

Rather than treating services and products as separate business lines, this integrated approach positions them as complementary levers of value.

Key pillars of a value-centric software development model

Delivering real value through software isn’t just about writing clean code; it’s about how teams are structured, how they collaborate, and how they prioritise impact over process. Software companies that consistently create high-performing, user-focused apps typically share several key principles in how they work.

Below are four pillars that define a value-centric software development model:

Specialisation Over generalisation

Teams structured around specialised roles, such as product strategy, UX design, front-end engineering, and back-end development, tend to deliver higher-quality results. When experts focus deeply within their domains, they can move faster, catch edge cases earlier, and share learnings across projects.

Rather than relying on full-stack generalists for every task, high-performing teams assign work to those with relevant expertise, which leads to better architectural decisions, stronger user experiences, and more maintainable codebases.

Co-creation with clients

Modern software development is no longer a handoff; it’s a partnership. Successful teams don’t just gather requirements and disappear for weeks. They build with clients, not just for them.

This means frequent feedback loops, collaborative working sessions, and openness to change. Co-creation helps ensure that what gets built aligns with business needs and that problems are solved, not just features delivered.

Pragmatic agility

Agile is a mindset, not a mandate. Value-driven teams don’t follow processes for the sake of process; they adapt frameworks like Scrum or Kanban to fit the problem at hand.

Being agile doesn’t mean chasing every trend or refactoring endlessly. It means iterating quickly, validating assumptions early, and focusing on delivering the most valuable outcome, not just the most polished sprint demo.

For a deeper look at the best framework for your project’s development, follow this guide.

Dedicated focus

Context switching is one of the biggest productivity killers in software. Teams that dedicate developers to a single project at a time see higher velocity, fewer mistakes, and better collaboration.

Deep focus enables developers to understand product context, own technical decisions, and contribute proactively, not just execute tickets. This also boosts morale, clarity, and overall team performance.

Understanding and validating client needs

A strong value proposition isn’t static, it evolves through continuous alignment with real customer needs. That alignment starts long before code is written and continues well after the first release. In high-performing software teams, the journey follows a clear pattern: discovery, delivery, and iteration.

Discovery: Build the right thing

Effective discovery is more than requirements gathering. It’s about deeply understanding the problem space business goals, user behaviors, and pain points before offering solutions. Teams use stakeholder interviews, collaborative workshops, and user research to build shared context and avoid assumption-driven development.

This phase is critical for shaping a value proposition that’s meaningful. It ensures you’re solving the right problems and delivering outcomes customers care about not just requested features.

Delivery: Solve real problems with precision

Once teams have clarity, delivery becomes execution with purpose. Agile methodologies enable incremental progress and allow for faster validation of ideas.. Velocity, however, means little without alignment. Each sprint should reinforce the value hypothesis uncovered during discovery.

Tight collaboration between developers, designers, and clients helps translate requirements into solutions that not only meet functional goals but also drive business impact. The more focused and feedback-aware the team, the more effective the output.

Iteration: Use feedback as fuel

Client feedback isn’t a post-launch exercise; it’s an embedded part of app development. Structured feedback loops such as user testing, beta groups, and in-app surveys provide immediate insight into what’s working and what’s not.

Analytics platforms, heatmaps, and AI-based behaviour tracking add another layer of validation, helping teams make data-informed decisions about product evolution. Feedback boards and upvoting systems ensure that customers feel heard and contribute to the app’s direction.

Here’s a complete guide to using feedback loops to drive effective MVP development.

Building for scalability: The real test of long-term value

In software, delivering initial value is only half the equation, sustaining that value as customer needs evolve is the real challenge. Scalability is what separates short-term utility from long-term impact. It ensures that the software you ship today can grow alongside the business without breaking under pressure.

Architectural choices that enable growth

Modern software teams prioritise scalable foundations from the outset. This includes:

• Modular architecture: By organising systems into loosely coupled components, teams make it easier to add, remove, or upgrade features without disrupting the whole.
• Cloud-native infrastructure: Leveraging cloud platforms allows for on-demand resource scaling, geographic distribution, and infrastructure-as-code practices that support agility.
• Microservices: Breaking complex systems into independent services makes it easier to deploy updates, isolate failures, and scale components based on usage patterns.

Together, these patterns reduce downtime, increase deployment velocity, and support future use cases that might not even be on the roadmap yet.

Scalability reinforces the value proposition

A scalable app delivers more than just technical robustness; it preserves the integrity of your value proposition over time. When usage spikes, performance doesn’t suffer. When new customers are onboarded, the experience remains seamless. When business needs shift, new capabilities can be introduced without architectural overhauls.

For software companies, scalability means staying relevant. It protects customer satisfaction, keeps costs predictable, and allows for continuous innovation, three key ingredients of a long-term competitive advantage.

Ultimately, building for scale is not only a technical decision, it’s a business one. And in a market where client expectations grow just as fast as user bases, scalable solutions aren’t a bonus, they’re a necessity.

Sustaining the value: Ongoing partnerships and improvement

Shipping software is just the beginning. The true test of long-term value lies in how well a product evolves post-launch, adapting to new needs, staying secure, and continuing to deliver impact.

Modern software partnerships extend beyond delivery. Businesses expect their platforms to scale, adapt, and improve over time, which requires ongoing collaboration, not just maintenance.

Beyond maintenance: Iteration as strategy

Rather than treating support as a passive service, leading teams embed continuous improvement into their workflows. Regular updates, feedback-driven enhancements, and performance tuning ensure that products remain aligned with business goals and user expectations.

This iterative mindset helps avoid technical debt, ensures compliance with new standards, and makes it easier to evolve the product as markets change.

Delivering value is a continuous practice

​​In an increasingly competitive software landscape, a strong value proposition is more than a positioning tool, it’s the foundation for product strategy, development processes, and long-term client success.

Whether delivered through custom-built platforms or scalable, pre-packaged solutions, true value emerges when teams understand real customer needs, design with intent, and build with adaptability in mind.

From discovery to delivery, and through every iteration beyond, software teams must align technical execution with business outcomes. That means staying close to the user, treating feedback as a strategic asset, and making architecture decisions that enable not limit growth.

Ultimately, value-driven development isn’t about features or frameworks; it’s about solving the right problems, at the right time, with clarity and purpose.

The post Why a strong core value proposition is your app’s greatest advantage appeared first on Business of Apps.

For years, app discovery and user acquisition have been dominated by app stores, search rankings, and paid media. That model is now being challenged. As large language models become daily interfaces for hundreds of millions of users, a new distribution layer is emerging — one where apps are discovered, used, and even built entirely inside AI ecosystems.

In the latest editions of App Talks, David Murphy spoke with Dan Shabtay, Co-Founder at Z2A Digital, about how the rise of ChatGPT as an operating system is reshaping app creation, discovery, and growth strategy. The conversation explored what this shift means for developers, marketers, and growth teams heading into 2025 and beyond.

From app stores to AI ecosystems

Dan describes the current moment as a structural break from the past. Instead of building solely for Apple’s App Store or Google Play, developers can now create applications directly on top of ChatGPT.

This new environment removes many of the traditional barriers to entry. There is no app review queue, no complex compliance process, and, crucially, no requirement to write code. Anyone with an idea and a well-structured prompt can create and publish an app to an audience that already numbers in the hundreds of millions.

For Dan, that changes the competitive landscape entirely. Solo founders and large enterprises now start from the same baseline, with access to the same infrastructure and distribution.

The rise of the ChatGPT App Store

At the centre of this shift is the growing app ecosystem inside ChatGPT itself. Dan highlights the scale of the opportunity: with more than 800 million active users, ChatGPT has effectively become a global platform where utility apps, productivity tools, and even games can be launched instantly.

Dan points to research from Goldman Sachs, estimating that the ChatGPT app ecosystem could generate between $15 billion and $25 billion annually by 2026-2027.

For entrepreneurs and growth teams, the implication is clear. Even capturing a fraction of that audience can support a meaningful business.

ChatGPT and the future of app discovery

Source: Business of Apps via YouTube

No-code creation and instant scale

One of the most disruptive elements of this new ecosystem is no-code app creation. Dan explains that tasks which previously required engineering, product, and project management teams can now be handled by individuals using AI-assisted workflows.

The result is speed. Ideas can be tested, launched, and iterated in days rather than months. Distribution is built in, because users never have to leave ChatGPT to discover or use these apps.

Dan calls the current moment a veritable gold rush. Low regulation, low friction, and massive demand are converging at the same time.

User acquisition in an LLM-first world

Despite the disruption, Dan does not believe traditional user acquisition will disappear overnight. Paid media, programmatic buying, and App Store Optimisation still matter. However, he expects a parallel acquisition channel to emerge directly inside large language models.

As LLMs evolve, Dan anticipates new forms of in-context promotion, recommendation, and possibly ad exchanges embedded within chat interfaces. Payments are already being integrated, turning ChatGPT into something closer to a super app.

Why major platforms are moving early

Large platforms are not waiting on the sidelines. Dan points to examples such as Booking.com, Canva, and Figma, all of which are beginning to establish a presence inside ChatGPT’s app layer.

By integrating functionality and payments — often through providers like Stripe — these companies can reach both existing customers and entirely new audiences without forcing users to switch contexts.

For Dan, this reinforces a key idea: the future battle is not going to be about acquiring new users, but about keeping them engaged inside your ecosystem rather than losing them to competing AI platforms.

Adaptability as the new growth skill

When asked how growth teams should respond, Dan framed the challenge in human terms. In the past, success favoured intelligence and expertise. Today, it favours adaptability.

Growth teams need to move quickly, experiment aggressively, and combine their existing skill sets with AI-native tools. The teams that win will be those that embrace LLM platforms early, test distribution inside them, and learn how discovery works when search results are replaced by conversational answers.

In Dan’s view, the risk is not moving too fast. The real risk is standing still while the ecosystem forms around you.

Looking ahead

As ChatGPT and competing LLMs evolve into full operating systems, app creation, discovery, and monetisation are being fundamentally redefined. For developers, marketers, and founders, the opportunity is vast but time-sensitive.

The takeaway for 2026 and beyond is simple: app growth will no longer live in a single store or channel. It will live wherever users spend their time. Increasingly, that place is inside AI chatbots.

Watch the full video to discover all of Dan’s insights. You can also watch all episodes of App Talks here.

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In-app customer support has evolved into a characteristic feature of the mobile app experience. According to recent research, the integration of AI agents in customer support is projected to handle 95% of all customer interactions. Users are now willing to get immediate responses without even leaving the app, particularly with payments, logins, subscriptions, or feature usage. Live support is no longer an added value; it is a prerequisite, conditioned by 24/7 online services.

Conventional support models find it difficult to satisfy these demands on scale. Frustration and churn are common subjects of high ticket volumes, slow response times, and low availability. U.S. consumers estimate they are transferred at least once during 87% of their customer service interactions. Thus, with the continued evolution of mobile ecosystems, AI in customer support is becoming a global industry trend, and it provides app teams with a scalable alternative to address the increasing demands without sacrificing quality.

The evolution of AI in customer support for mobile apps

Customer support AI has been shaped to develop considerably in the last ten years, specifically in mobile contexts. The initial applications were mostly rule-based applications that were meant to respond to predefined questions. These systems were based on hard-decision-trees and matching keywords, and their use was limited when the query by the users deviated out of the patterns.

The evolution of AI in customer support for mobile apps

Source: GPTBots.ai

Customer support in mobile applications has been developed in stages due to rise in user expectations and complexity of applications. At first, assistance was based on simple automation to decrease workloads.

The important milestones in this development would be:

• Rule-based chatbots: The early systems performed fixed operations including answering of FAQs, password reset, or responding to straightforward login problems with pre-created scripts.
• NLP-based assistants: NLP opened up the possibility of the AI to comprehend user intent and thus respond better to subscription queries, billing schedules, and simple in-app navigation.
• Smart AI agents: New AI agents are capable of handling multi-step processes with machine learning and contextual information, e.g., troubleshooting multiple failed attempts to enter the account or assisting the user with an upgrade or unsubscribe.

It demonstrates how AI in customer support has evolved through solitary, scripted responses to dynamic systems that can assist in the intricate interactions of mobile applications.

The new stage of adoption is AI agents today. These systems are capable of interpreting user intentions, preserving support conversation details and executing actions in support workflows. This is a feature in mobile apps, whether it is the recovery of a password, an upgrade of subscriptions, or the request of refunds, or even the explanation of features in a single interaction.

Instead of responding, AI agents have the ability to take users through the process step by step and respond accordingly based on history and behavior.

This development is more of a larger change towards proactive assistance that is contextual and built right into the app experience.

Customer support automation with AI agents in mobile apps

The use of intelligent systems to handle and resolve support requests without human intervention all the time is referred to as customer support automation in mobile apps. In contrast to traditional help desks, automation, which is driven by AI agents, will work dynamically in the application environment.

Customer support is an autonomous customer support agent, which can comprehend intent, retrieve pertinent data, and perform predetermined actions. These agents are also constructed to manage large volumes and repetitive interactions and still have the continuity of conversation.

Benefits of in-app customer support automation:

• Scalable problem-solving: AI-based agents will be able to handle thousands of concurrent in-app interactions, automatically solving frequent problems (accessing an account, onboarding, subscription, and more).
• Context-sensitive support: Automated agents can store session and user state, so that they can give the right response based on the current screen, actions or the status of the user account.
• Quick response time: In-app automation removes time to wait so that it offers real-time support when users require it.
• Reliable support: Knowledge-based responses are standardized and result in correct and consistent support to all users.
• Elastic support capacity: Automation has an immediate scaling ability that happens when the usage is high without any new human personnel.
• Operational efficiency: Human support agents gain liberation to work on high-value cases, complex, and sensitive cases demanding empathy and judgment.
• Reduced and foreseeable costs: Automated routine queries will cut down on the variable support cost as more users are added.

With automation of customer support being directly embedded in mobile applications, teams can achieve a high level of responsiveness, control the expenses, and provide the users with a seamless user experience without losing the quality of service..

Placing customer support automation within mobile apps allows the respective teams to stay responsive without compromising user experience.

Best practices for implementing AI in in-app customer support

When introducing AI-powered in-app customer support, it is necessary to design it in a strategic and user-focused manner to make it efficient, credible, and high-quality of the support experience. The best practices are useful in assisting teams design, deployment and continuous improvement of AI support systems in mobile or web applications.

Isolate high frequency user requirements

When creating the analysis, begin by examining support, chat logs and in-app behavior to identify high frequency and repetitive problems such as login issues, onboarding support, account updates and billing questions. Automation of these use cases will provide an instant effect and less work to human agents.

Provide context-sensitive support

Make the AI agent aware of the context of the user, such as what he is on, what he has been doing, account status, and user-journey progress. Context-sensitive responses enable the AI to provide accurate, timely and pertinent assistance without compelling the user to repeat information.

Support reliable knowledge sources

The AI should be trained using authoritative descriptions of the product like manuals, frequently asked questions, policies, and internal sources of knowledge. Knowledge-response is more accurate, fosters trust in the user and ensures the same quality of support throughout all interactions.

Engineer smooth failure over to human agents

Establish explicit escape points which can be used to move to human assistance when the matter is complex, sensitive or unsolved. The information about context, conversation history, and user data should be passed through to human agents without creating friction and redundancy.

Show AI engagement

Be obvious about when an automated agent is engaged with and what to expect of this agent. Openness enhances user friendly behavior and evades frustrations should automation be pushed to the extremes.

Train and optimize on real user feedback

Train AI continuously on real user inputs (e.g. anonymized conversation logs, queries not answered, edge cases, etc) and continually improve the AI. This repeated training process assists in bridging the gap in knowledge and enhancing the level of accuracy in resolving problems with time.

Develop a scheduled feedback system

Provide an option of user rating, wrong-answer marking, and suggestions input in the app to inform improvements and related adjustments to the AI behavior and content coverage.

Measure the performance

Keep an eye on the key indicators such as the number of resolves, response time, escalation rate, and the satisfaction rate of the users to determine the effectiveness. The process of continuous improvement of AI processes and the overall support strategy is possible, as data-based assessment allows improving it.

Continuous data analysis and refining

Conduct regular interaction data analysis in order to detect emerging user needs, optimize conversations flow, and improve contextual knowledge and make sure that the AI is developed in the same manner as the product.

Select an advanced no-code or low-code AI agent platform

Select a platform that is capable of scaling and enabling teams to create, configure, and deploy in-app AI support without intensive engineering. No/low-code tools make it possible to quickly iterate, maintain easily and scale efficiently to meet user demand.

The combination of all these practices is a responsible adoption of AI, a quality in-app experience, and a flexible support model with a balance between automation and human knowledge.

The practices contribute to the responsible integration of AI and the maintenance of the quality in-app experience.

How GPTBots.ai enables AI-powered customer support automation

GPTBots.ai enables companies to provide smart and smooth customer care in real time on web and mobile applications. Through AI-based automation, context-responsive support and no-code deployment, it assists staff to react faster, cost less and deliver reliable and uniform support, without interfering with the user experience.

Enterprise AI agents

Source: GPTBots.ai

GPTBots.ai in-app customer support key features:

• Seamless in-app integration: GPTBots.ai are installed right into your applications providing immediate support without any necessity of users to leave the product. The support is seen as a natural interactive experience.
• Context-aware assistance: The AI is also aware of what the user is doing and what stage of the journey they are in, so any response given is timely and applicable depending on the action of the user.
• Knowledge-driven answers: GPTBots.ai uses product documentation, FAQs, and internal knowledge depositories to provide reliable and correct responses to all interactions in a similar manner.
• Automation of support workflows: Automation is done to repetitive tasks including guided trouble shooting, form entries, and creation of tickets, enabling the support teams to concentrate on more valuable problems.
• Human-in-the-loop escalation: In case of a case being complex or sensitive, GPTBots.ai scales gracefully to human agents with context of conversations and thus resolves the problem better and informedly.
• Actionable insights and continuous improvement: Analysis of AI interactions is used to determine common problems, user pain points, and inefficiencies in the workflow, and helps teams to improve the product and the support experience over time.
• No-code deployment and customisation: The teams that are not technical can easily deploy and configure the agents of AI with no engineering in a short period of time, developing and optimising in-app support workflows with the minimum effort.
• Multitasking and human-like interaction: GPTBots.ai has multilingualism and can adjust the tones and styles to give natural and human-like conversations, which will improve the user experience across the world.

With no-code deployment, context-aware assistance, and seamless human handoff, GPTBots.ai can be up and running in minutes—without heavy engineering effort. Companies are able to provide quicker solutions, lessen operation expenditures, and constantly enhance the customer experience, and still maintain human regulation in the process where necessary.

Start your free trial today and see how GPTBots.ai helps you automate in-app support, reduce support workload, and deliver smarter, more human customer experiences at scale.

The post How AI agents are reshaping in-app customer support for mobile apps appeared first on Business of Apps.

2024 was the year of bright-eyed pitches, speculative innovation, and narratives about industry transformation. 2025 filtered the excitement through budget expectations and regulatory inquiry. At the outset of 2026, the mission statement for performance marketers seems to be benchmarking the performance of everything that has made it through the filter and fully integrating it into the wider adtech ecosystem.

Adtech giants commit to AI

The clearest indication of AI-led transformation of adtech, even to the uninitiated, is leading social and commerce platforms taking risks with the technology. Meta’s initiative to use data from AI chatbot interactions for tuning ad and content personalization across Facebook and Instagram shows the extent of AI’s current influence on media systems.

How committed is Meta to making AI part of its ad platforms’ infrastructure?  The decision to apply this update broadly across the platforms’ user bases with limited opt-out mechanisms should be an obvious sign.

Amazon keeps investing in AI-driven media buying inside its DSP, rolling out tools like Performance+ and Brand+ that leverage machine learning to automate audience selection, bidding, and optimization across the company’s inventory. These features lift operational load from media buyers and help campaigns optimize toward specific performance goals, combining Amazon’s first-party data with real-time behavioral patterns to predict conversion likelihood and adjust delivery accordingly.

Rolling out and incentivizing advertisers to use AI tools will likely remain the game plan for major advertising platforms in 2026. The good news is that the features will likely remain optional, and marketers’ job will remain the same: identifying which ones actually drive value and taking manual control where needed. The challenge is to split-test new capabilities in pace with this year’s tightly packed release schedule.

Programmatic still not automatic

Programmatic advertising enters 2026 mostly intact, but clearly more streamlined. Many advertisers have cut intermediaries out of their supply paths in 2025, valuing cost transparency and lead accountability above immediate volume.

Major platforms keep moving further into automation. For example, Google expanded its AI-driven bidding and keyword recommendations across search in 2025, while TikTok released predictive optimisation tools that reduce manual campaign management. Advertisers who have tested it report that results still depend heavily on data quality and campaign strategy.

What became clear in 2025 and stays pertinent in 2026 is that automation likely won’t ever eliminate the need for judgment. Machine-learning optimisation tools have streamlined execution since before the generative AI boom, but performance still depends on the marketer’s ability to interpret opportunities and act on them. The tools’ capabilities are still far from removing the responsibility for decision-making.

CTV gains more ground

Connected TV crossed a signpost threshold last year: it’s no longer perceived mainly as an experimental extension of linear TV. Lower-funnel use cases have become much more practical with improved household-level targeting, better attribution modeling, and tighter programmatic integrations.

At the same time, some of the format’s unresolved issues still carry over into 2026. Measurement is still fragmented, frequency management across publishers is inconsistent, and inventory quality varies widely. These shouldn’t be perceived as a barrier to investing, as major progress in these areas is bound to happen in 2026.

Cookies with context

Google’s impending third-party cookie ban finally stopped impending in April last year, to the collective sigh of relief from the performance marketing industry. However, “indefinitely postponed” doesn’t inspire complete confidence in the stability of the status quo, especially in the face of unremitting pressure from regulatory bodies and the average internet user’s ever-increasing privacy expectations.

The search for alternative tracking and attribution methods that started when the sky was falling continues as it stalls uncomfortably close to the ground.

Contextual and content-based methods are gaining traction, if not to replace cookie-based attribution, then to supplement it by giving marketers a clearer picture of what works and what doesn’t on the supply path. Emerging tools like Classify use generative AI to analyze and classify page content instead of logging device IDs and locations.

Optimising based on content and engagement instead of user identity is a potential way to avoid privacy challenges entirely. It also makes sense as a natural evolution of keyword-based models, leveraging one of the strongest points of the modern generative AI models: language processing.

Wrapping up the wrap-up

2026 offers more stable footholds for marketers than the previous few years. There are fewer hypotheticals to navigate and more defined options to test. While the field still carries more risk than it did before 2022, the shape of a new framework is becoming visible.

There is room both to help develop it further and to find a place within its existing sections. That constraint, paradoxical as it may be, creates more opportunity than the endless possibilities of the past few years.

If you’re looking to gain more insights and start earning with ClickDealer, register here.

The post Adtech wrap-up 2025: Experimentation to execution appeared first on Business of Apps.

There are some classic pairings where each half makes the other better: peanut butter and jelly, bread and butter, cookies and milk. The same applies to App Store Optimization (ASO) and paid marketing. When you only have one, you are missing its other half that makes it even better.

App Store Optimization covers every element that contributes to your organic search rankings, visibility, downloads, and conversion rate. ASO, when done correctly, can help your app rank in search results, where 70% of an app’s installs originate from. This includes testing and making changes to:

• App title
• Subtitle (iOS only)
• Keywords (iOS only)
• Short description (Google Play only)
• Full description
• App icon
• Screenshots
• Preview video
• Poster frame (iOS only)
• Feature graphic (Google Play only)

Let’s consider two different app promotion strategies.

Focus only on App Store Optimization without any paid marketing. Our organic listing will be optimized fully, and we have targeted our core terms correctly, but the algorithms are taking longer to index our updates for keyword rankings, and it is difficult to break into high-ranking positions.

Focus only on paid marketing without any App Store Optimization. Our paid campaigns are fully optimized and drive traffic. However, the Apple Ads campaign has minimal impressions for some terms after increasing bids, and the conversion rate to install for other terms is low compared to industry standards, which is increasing our cost per install.

Both of these strategies are viable and can be effective in their own ways, but what happens when we combine them?

Like cookies and milk, App Store Optimization and paid marketing are good on their own, but they are better together.

A unified App Store Optimization and paid marketing strategy will improve performance for not just installs, but also organic keyword targeting, conversion across channels, and campaign efficiency.

How do Apple Ads work?

Apple Ads is a paid channel where you can bid on key placements within the iOS App Store. Apple will take your bid into account along with other factors such as historical bids, relevance, tap-through-rate, conversion rate, and competitor bids in order to determine who wins the auction and placement.

One advantage of Apple Ads is the users. You already know the users intend to download an app by nature of exploring the store, so Apple Ads just helps you get in front of the right user.

There are four placement types which each requires a unique strategy and approach to maximize performance:

• Search Results let you reach users at the top of relevant search results when they search for something specific.
• Today Tab lets you reach people on the front page of the App Store, where users start their visit.
• Search Tab lets you reach users at the top of the suggested apps list on the Search tab before they search for something specific.
• While browsing, Product Page lets you reach people as they browse apps on pages across the App Store.

The Search Results campaign has the most direct impact on ASO, as this traffic can be directly tied back to a search query. Apple will take the clicks and installs from Apple Ads into account for organic keyword rankings. This means that additional volume from Apple Ads Search Results campaigns can help ASO updates index faster for keywords and improve ranking potential for targeted terms.

How do Google Ads work?

Google Ads is an algorithmic pay-per-click (PPC) acquisition channel where you can run app campaigns across Google’s inventory of placements, including:

• Within the Google Play Store
• Google Search
• Display Network (Gmail, within other apps, or on mobile websites of news sites, blogs, and other sites across the Internet)
• YouTube

The Play Store placement has the most direct impact on ASO, similar to the Apple Ads Search Result campaign, as it can be directly related to a user’s search query. However, Google Ads differs from Apple Ads because you cannot select where the ads are placed. This is automatically determined by the algorithm based on factors such as bid, assets, historical performance, campaign goals, etc.

How paid marketing helps ASO

Although paid marketing helps with ASO, each channel and placement has a different impact and can vary based on the app’s vertical and size.

Apple Ads

Search result placements drive additional clicks and installs, which helps signal Apple to support organic rankings and keyword indexation. The impact of this will vary depending on the app size. If a new app starts running Search Results campaigns, they will likely see larger increases and improvements in rankings compared to an established app with organic rankings running at the same ad spend.

Auto placements, including the Today tab, product page, and search tab, will help support overall volume and awareness, but will not have keyword ranking support because they cannot be tied back to a search query.

Google Ads

Play Store placements will also support keyword indexation and organic rankings by driving clicks and installs on specific search queries.

Other placements, including Google Search, Display Network, and YouTube, will support install volume and awareness outside of the Play Store but will not have keyword ranking support because they cannot be tied back to a search.

How ASO helps paid marketing

Paid drives users to the store listing/product page, meaning your ASO assets will impact their final decision to download or not. Higher converting ASO assets and screenshots will not only help your organic performance, but will also decrease paid costs and make your campaign more cost-efficient.

Apple Ads directly pulls ASO assets, including the icon, title, and subtitle, into all ad placements. Search result campaigns will also utilize the first three screenshots or the preview video and the first two screenshots as default creatives. Apple Ads also determines paid relevance from the organic metadata, meaning your ASO will impact paid impressions.

Google Ads utilizes ASO metadata and creative assets alongside other ad elements to generate various ad combinations.

Overall, any changes made to organic assets can have a direct impact on paid campaign performance, and the strategy should be united across both.

How app store custom page work

Imagine you are working at a food stand and different customers order a hot dog, hamburger, and ice cream. Would you show them the salad bar? You could show them the salad bar, but they will likely leave and buy food somewhere else.

If you drive all paid traffic to the same main store listing, you may end up with a salad bar situation, losing customers. iOS custom product pages and Google Play custom store listings are your solution to ensure users see and get what they want from your listing.

iOS custom product pages are additional versions of your product page where you can present unique versions of your screenshots, promotional text and/or app preview videos without impacting the main evergreen page. Google Play custom store listings have a similar functionality, with the additional ability to customize the app name, icon, and descriptions.

These separate landing pages help optimize your app’s last touchpoint before the user decides to install. Custom pages can be displayed for specific keywords, ad campaigns, or external marketing efforts, directing users to a designated URL.

Custom pages can be created for Apple Ads and Google Ads to match the theme of the ad group. For example, a retail app could set up a custom page with shoe-related screenshots and run it in the Apple Ads shoe ad group. This would change the ad placement creatives for any keywords in the shoe ad group. Consistent visuals and messaging between the paid campaigns and custom pages can support conversion performance.

The custom page by search term is most useful for ASO because different pages can be created for each theme of terms. But this is not a free-for-all where developers can add in any term they wish:

• Apple will only allow targeting for terms and phrases in the keyword bank.
• Google will allow targeting for terms listed in the console.

It is essential to have a unified ASO and paid strategy because elements overlap and impact each other. If the iOS keyword bank were only focused on terms for Apple Ads, for example, this might not provide the terms necessary for the ASO custom page by search term targeting.

Final thoughts

There are many options when it comes to promoting your app. Should you focus on App Store Optimization, paid social channels, ad networks, influencer marketing, or another form of marketing?

As a foundation, you should start with ASO because it impacts the listing where all users will land, and having an effective page will support all future marketing. Then give your ASO and organic rankings a boost with Apple Ads or Google Ads campaigns. Over time, both your ASO and paid ads will see better results from each other – just like milk and cookies.

Let’s chat

If you’re ready to explore how ASO and paid can fit into your growth strategy, consider leveraging expert guidance and ASO services. Together, we can help your app maximize performance for both organic and paid marketing with a unified strategy catered to your app.

The post Maximizing the impact of App Store Optimization and paid marketing appeared first on Business of Apps.

Sports streaming monetization depends on a platform’s niche and audience geography, with revenue typically driven by advertising and affiliate models. This case study from Adsterra outlines practical strategies for building an effective monetization framework showing how a sports streaming site can generate revenue without live event broadcasting.

Case study overview

• Publisher: Mohammed Ali, sports blogger
• Vertical: sports, mainly football 
• Top traffic: Saudi Arabia, Egypt, France, Germany, United States
• Ad formats: Display banners, Popunder ads
• Earnings: Up to $2,000 per month

Websites operated by the publisher

Source: Adsterra

As seen, the publisher operates English- and Arabic-language sports websites monetized through a limited ad setup, using a single 300×250 Banner and Popunder ads via Adsterra. Content is updated twice daily with current sports events and links to official streaming platforms, without hosting live broadcasts.

This model delivers average monthly revenue of $1,900–$2,000, with peak periods generating up to $650 within five to seven days. The strategy centres on a focused niche, soccer, and non-intrusive ad formats, ensuring minimal disruption for video-oriented users.

Average monthly revenue for the app

Source: Adsterra

Geography matters

Audience selection plays a critical role in sports streaming monetization. While tier-one markets such as the US, UK, and Canada deliver premium ad rates, Mohammed expanded focus to Arab-speaking audiences, where demand for sports content is particularly strong.

Audiences selected for the app

Source: Adsterra

This strategy significantly increased traffic, with Morocco, Saudi Arabia, and Egypt among the top sources of impressions as the screenshot from Adsterra stats shows. European markets, including Germany, France, Spain, and the UK, represent an additional priority, supported by localized, SEO-driven blog content that serves as the primary traffic and monetization channel.

Best practices to build traffic and revenue in sports streaming 

Mohammed emphasizes that successful sports streaming websites are built on a strong technical and content foundation, particularly for football-focused platforms. Here are his main tips for success:

• The mighty UX

Before investing in SEO, make the website lightweight, fast, and intuitive, allowing users to reach key information within one or two clicks. Shallow click depth (the closer other pages are to the main screen) also signals content importance to search engines, supporting higher rankings.

• Keywords and trend alignment

Effective monetization depends on targeting high-intent keywords alongside audience-specific topics. Football coverage typically spans match results, predictions, fixtures, and player profiles. High-volume search terms in Arabic sports markets include phrases such as Koora Live and Yalla Shoot, reflecting demand for real-time match-related content.

• Audience-specific content

To reach diverse markets, Mohammed maintains separate Arabic- and English-language platforms. However, he avoids direct translation; instead, he localizes content to reflect the interests and discussions of each audience. Popular competitions include the UEFA Champions League, English Premier League, CAF Champions League, and leading regional leagues.

Given licensing restrictions on live broadcasts, the recommended approach is to publish in-depth match coverage and direct users to official streaming platforms. Outbound links should use no-follow attributes to preserve SEO value.

Pro tip: if the streaming platform for the event isn’t a big or well-known one, there’s an opportunity for mutual promotion and win-win collaboration. Reach out to them, and try to agree on some exclusive conditions, because technically, linking to them on your website is a promotion. They may be open to linking you back, too!

• Search-optimized content structure

Organic traffic serves as the primary revenue driver, making structured, SEO-friendly articles essential. High-performing match previews typically include a keyword-focused headline, analysis of both teams and expected lineups, and clear event details such as timing and broadcast availability. Where permitted, use embedded streams; otherwise, provide links to licensed broadcasters.

SEO tools supporting traffic growth

Mohammed relies on a combination of established SEO platforms to support traffic acquisition and search visibility, with each tool serving a distinct role in execution.

1. Moz is primarily used for foundational SEO tasks, including domain strength assessment, keyword discovery, on-page optimization checks, and high-level competitor analysis. Its crawling and rank-tracking capabilities help identify technical issues and monitor performance trends over time.
2. Ahrefs is positioned as a more advanced solution for backlink intelligence and competitive research. The platform is used to analyze link profiles, uncover keyword and content gaps, audit site health, and track rankings across markets. Its link-focused features, such as competitor backlink comparison, are particularly effective for identifying growth opportunities.

Together, these tools enable a balanced SEO workflow, combining technical optimization, content planning, and off-page strategy to support sustainable traffic growth.

A publisher’s guide to sports streaming infrastructure

Key tech for streaming 

Launching a sports streaming website requires: 

1. Selecting the right blogging platform that is suitable for handling high traffic and transmitting videos without lag, particularly during peak moments such as live goals. 
2. Integrating a video player to deliver videos and server infrastructure to save them. 
3. Attention to legal compliance – streaming must either be licensed or link to official platforms.

Using Blogger for streaming

Blogger offers a cost-effective option for sports websites, with free templates, ad-friendly designs, and minimal technical issues. Key benefits include ease of use, security, and compatibility with embedded video players. To safeguard content, publishers should back up both posts and themes regularly.

Guide to embedding live streams

Live videos can be integrated using M3U8 links or iframe codes, which most players, including JW Player, support. M3U8 files act as playlists, organizing video segments for efficient streaming. Professional players may require additional setup, but tutorials are widely available.

Server considerations

Self-hosted servers provide control but are expensive. Many publishers who don’t directly produce streams use third-party servers to only host streams in a legal way, embedding the M3U8 links on their websites. Regular monitoring ensures that expired streams are promptly updated.

Licensing and content access

Legal compliance is essential. Here’s what to take into consideration:

1. Global sports events often require expensive licenses nearly impossible to afford by an average publisher.
2. Local events may be accessible via direct permission from teams or associations.
3. Linking to official streaming platforms remains the most practical approach for beginner publishers. Marketplaces such as Envato offer pre-built templates with licensing considerations for streamlined deployment.

Leveraging Telegram as a source of organic traffic for sports streaming sites

While Google remains a primary traffic source for many publishers including Mohammed, he also hints that Telegram offers a growing opportunity to reach engaged sports audiences. Telegram is a messaging platform with flexible features that allow users to join niche-specific channels and track trending content.

Discovering relevant Telegram groups:

1. Use Telegram’s search with keywords such as “sports” or “football” and explore the channels associated with these words.
2. Switch to global search or “Show more” mode for broader results.
3. Use Google to find groups with the format: t.me/joinchat + [keyword] (e.g., t.me/joinchat football).
4. Explore aggregator websites like Telegramchannels.me to identify active communities.
5. Leverage bots such as Teletagbot for searchable lists of channels.

Utilizing Telegram traffic

Telegram channels can serve as a promotional tool to direct users to a sports streaming website. Short teasers, such as upcoming match streams, news, or sports predictions, encourage users to visit the site for full content. To maintain engagement, it is critical to provide accurate, high-quality information and media.

Key takeaways for monetization

Effective sports traffic monetization requires ongoing attention to trending teams, popular players, and high-demand events. Publishers should simplify operations by selecting a focused niche, implementing SEO-driven content, and using cost-effective platforms such as Blogger. Embedding high-demand streams and integrating ad networks like Adsterra (Popunders, Push Ads, or Banners) can then generate revenue as shown in this peculiar case presented by Mohammed. 

Join and earn as Adsterra Publisher

The post How non-live sports streams can generate $1,900+ monthly appeared first on Business of Apps.

With global financial digitalization accelerating, competition in the financial app market is intensifying. Marketing strategies are evolving rapidly and have become essential for user acquisition and market expansion.

To help industry professionals stay ahead, SocialPeta analyzed 2025 global financial app marketing data and released the report Finance app marketing trends, providing detailed insights into emerging market patterns and strategic, data-backed recommendations to support more effective decision-making.

Overview of global financial app advertising

In 2025, the overall trend for global financial app advertising shows a slight decrease in the number of advertisers but an increase in the volume of advertising creatives.  Marketing strategies vary significantly across different regions and dimensions, reflecting the diverse needs of the global financial market.

Slight decrease in the number of advertisers, increased investment in advertising creatives

In 2025, the average monthly number of active financial app advertisers worldwide exceeded 13,000, down 3.81% YoY, indicating a more rational market as rising compliance costs and tighter budgets pushed some small and mid-sized advertisers to scale back.

In contrast, the average number of creatives per advertiser rose 23.83% YoY, peaking in August, as leading platforms increased creative investment to strengthen brand visibility and defend market share.

Meanwhile, rapid product evolution—driven by AI features and deeper scenario integration—has further increased demand for diverse creatives to clearly communicate new value propositions and attract target users.

Monthly advertisers and creatives trends for global finance apps

Source: SocialPeta

Regional advertising differences: North America has the highest concentration of advertisers, while Southeast Asia leads in the volume of creatives.

From a regional distribution perspective, global financial app advertising shows distinct characteristics:

Number of advertisers

North America is the core market with the highest number of active advertisers, attracting payment and brokerage apps due to mature consumer behavior and a well-regulated financial environment. In contrast, Hong Kong, Macau, and Taiwan have the fewest advertisers, reflecting market saturation and intense local competition.

Average number of creatives

Southeast Asia leads globally in average creative volume, signaling the strongest growth potential as rising adoption of mobile financial services drives high-frequency creative deployment. Europe and Africa follow as complementary markets, supported by Europe’s stable user base and Africa’s expanding opportunities in emerging markets.

Finance app advertising trends in top countries/regions

Source: SocialPeta

Copywriting strategy: English as the mainstream, core message focused on “free” and “convenient”

In 2025, global financial app copywriting shows clear language and content preferences:

Language selection

English remains the dominant language, accounting for 46.3% of creatives and covering major markets such as the United States and Europe. It is followed by Spanish (19.6%), French (7.7%), and German (4.7%), primarily serving audiences in Latin America and Europe. Minority languages—including Hindi, Japanese, and Vietnamese—each represent less than 2%, reflecting targeted investment in emerging markets.

Core message

High-frequency copy highlights free transfers, real-time exchange rates, and operational convenience, directly addressing users’ demand for low-cost, efficient financial services. In terms of tone, emojis (23%), numbers (22%), and exclamations (20%) are most commonly used, boosting click-through rates with more vivid and impactful messaging.

Analysis of popular ad copies

Source: SocialPeta

Popular financial app marketing cases

Across the three core sectors—payments, brokerage, and consumer finance—leading financial apps have adopted differentiated marketing strategies tailored to their positioning and regional needs, forming practical and replicable benchmarks.

Payment apps: AI-pwered upgrades and ecosystem co-operation to expand boundaries

Payment apps position convenience and global reach as core strengths. In 2025, they further reinforced these advantages through deeper AI integration and expanded ecosystem partnerships:

PayPal: As a leading global digital wallet, PayPal serves over 400 million active accounts, supports payments across 200+ countries and regions, and processes US$1.68 trillion in annual payment volume. In 2025, its marketing centers on the “AI + payments” ecosystem, highlighted by a collaboration with OpenAI that will enable ChatGPT users to make payments via PayPal starting in 2026, seamlessly connecting user intent with transactions.

PayPal’s advertising focuses on Europe and North America, with English-language creatives accounting for over 85% of campaigns. Messaging revolves around three pillars—Cash Back, Pay Later, and Social Payments—while creative execution follows two main styles: minimalist product animations with concise copy, and celebrity-led campaigns that use humor to highlight key benefits. PayPal consistently ranks among the top three free finance apps on the U.S. App Store.

PayPal: Partner with an AI giant, changing the digital wallet market

Source: SocialPeta

Brokerage apps: Highlighting zero commission fees, with professional creatives designed to build user trust

Brokerage apps focus on low cost and high precision, establishing user trust through clear value propositions and professional marketing.

Brokerage apps: Advertising competition vs. zero-commission

Source: SocialPeta

Webull

Webull launched zero-commission US stock trading and went public on Nasdaq via SPAC in April 2025 (BULL), with first-day trading up 10% and a market cap of ~$6.09B. The platform has 50M+ global downloads, 23M registered users, and 98% quarterly retention. Marketing focuses on image-based creatives (54%), highlighting zero fees, low commissions, and deposit bonuses.

Its style is professional and stable, using stock charts and feature-focused videos. Webull ranks consistently in the top 20 of the US App Store finance category.

Consumer finance apps: Focusing on emerging markets, scene integration enhances user loyalty

Consumer finance apps focus on “fast credit approval” and “localized services,” leveraging scene-based ecosystems to drive user conversion:

DiDi Finanzas offers revolving credit and credit cards, leveraging its ride-hailing ecosystem—11.96M daily rides—to promote vehicle loans to drivers and consumer credit to passengers. In Q2 2025, international GTV reached RMB 27.1B (+27.7% YoY). Advertising targets Latin America, especially Mexico (10M+ users), with 71% of placements on Android.

Creatives use micro-dramas depicting real financial scenarios and KOC influencer recommendations, with copy compliant with Mexico’s Consumer Protection Law.

DiDi Finanzas’s excellent active ad creatives

Source: SocialPeta

This 24-page report offers a comprehensive overview of the 2025 global financial app marketing landscape, featuring case studies, data analyses, and future trend predictions, including scenario integration and deeper AI adoption.

It provides practical guidance on strategy, acquisition, and retention, helping marketers and industry professionals identify new growth opportunities.

Download the full report for detailed insights.

The post Finance app marketing trends appeared first on Business of Apps.

Subscription apps are no longer the scrappy sibling of mobile gaming. They now represent one of the fastest-growing segments in the app economy — but they’re also facing a far more complex growth environment than even a few years ago. Between iOS privacy changes, fragmented measurement, rising acquisition costs, and increasingly algorithmic ad platforms, sustainable growth now depends less on hacks and more on fundamentals.

That was the core theme of a recent episode of Branch’s How I Grew This podcast, featuring Shumel Lais, founder of Day30 and longtime advisor in mobile growth and analytics. Drawing on more than a decade of experience across agencies, analytics platforms, and subscription businesses, Lais offered a grounded look at what actually works for subscription apps today — and where many still go wrong.

Why subscription apps lag behind gaming

One of Lais’s central observations is that the app ecosystem has long been split into two dominant verticals: gaming and non-gaming subscription apps. While gaming companies are typically well equipped with business intelligence, data science teams, and sophisticated tooling, subscription apps often operate with far less analytical depth.

That imbalance matters more than ever. As user acquisition becomes more algorithm-driven and privacy-constrained, growth teams are increasingly reliant on the quality of the signals they feed into ad platforms. Without strong data foundations, subscription apps struggle to compete — not because demand isn’t there, but because decision-making lacks precision.

This gap is precisely what motivated Lais’s work over the years, from building analytics platforms to advising subscription businesses directly. The opportunity, he argues, isn’t about chasing growth shortcuts, but about bringing the same analytical rigor to subscriptions that gaming has had for years.

From spreadsheets to real-time decisions

Earlier in his career, Lais experienced firsthand how fragmented mobile data used to be. Performance data lived in spreadsheets, revenue data sat elsewhere, and stitching the two together was a manual, time-consuming process. The result? Teams reviewed performance weekly — sometimes even less — making it nearly impossible to react quickly.

Today, while tooling has improved, the underlying lesson remains the same: growth suffers when data is delayed or incomplete. Marketers don’t need to become SQL experts, but they do need access to timely, trustworthy data that reflects the full customer journey.

For subscription apps, that means understanding not just installs or trials, but how different users behave across plans, time horizons, and engagement levels. Weekly subscribers behave very differently from annual ones — and without event-level visibility, those differences are invisible.

The real challenge of iOS measurement

Despite years passing since Apple’s major privacy changes, Lais notes that many large apps are still struggling to adapt. SKAdNetwork remains widely used, but it provides limited, delayed feedback — especially problematic for subscription models where value accrues over time.

What’s emerging instead is a shift toward probabilistic and privacy-preserving measurement approaches offered by major platforms. These methods don’t restore perfect visibility, but they do provide something just as important: confidence.

Confidence in longer-term decision-making is what allows teams to invest, test, and scale without overreacting to short-term noise. In a world where ad platforms optimize on seven-day windows but businesses care about twelve-month lifetime value, bridging that gap becomes critical.

Early-stage mistakes that slow growth

For early-stage subscription apps, Lais sees a familiar pattern repeat itself. Founders want results immediately — often because cash is tight — and expect user acquisition to pay back almost instantly. While understandable, this mindset can quietly cap growth.

Small budgets tend to capture the lowest-hanging fruit: users most likely to convert quickly. As spend increases, acquisition inevitably widens to less certain audiences, extending payback periods. Teams that insist on immediate ROI often stall right at this inflection point.

Instead, Lais encourages founders to model growth realistically. Larger apps scale faster precisely because they accept longer payback windows — sometimes six to twelve months — knowing that strong retention ultimately justifies the investment.

Data before perfection

Another common trap is waiting for the “perfect” funnel before investing in growth. In reality, funnels are refined through data, not designed in isolation. Without sufficient traffic, benchmarks never form, and optimization remains theoretical.

Paid acquisition, while expensive, provides fast feedback. It reveals which messages resonate, which features matter, and where users drop off. That insight doesn’t just improve ads — it improves onboarding, positioning, and even product direction.

The key is intentionality. Paid traffic isn’t about scaling prematurely, but about learning efficiently.

AI, creative volume, and paywalls

On the creative side, Lais sees AI accelerating experimentation rather than replacing strategy. Platforms like Meta increasingly reward volume and variety, making it easier for AI-generated creative to fill the pipeline. Whether AI consistently produces true “winners” remains an open question — but its ability to speed up testing is undeniable.

Paywalls, meanwhile, remain one of the most critical — and time-sensitive — elements of subscription success. Data consistently shows that most users who subscribe do so within minutes of opening the app. That makes paywall design, placement, and testing non-negotiable.

While paywall tools are becoming more standardized, Lais believes optimization here is now table stakes. The question isn’t whether to test paywalls, but how systematically teams do it.

Scaling requires patience, not shortcuts

Ultimately, the episode reinforces a simple but often overlooked truth: sustainable subscription growth is built on patience, data discipline, and realistic expectations. The “crappier” the product, the faster payback must be. The better the product, the longer teams can afford to wait.

As subscription apps continue to grow — fueled in part by lower barriers to entry and AI-driven development — competition will only intensify. Those that win won’t be the ones chasing quick returns, but the ones building strong signals, understanding their unit economics, and making confident decisions in an imperfect data world.

In that sense, the future of subscription growth looks less like clever hacks — and more like grown-up marketing.

Tune in to listen to the episode below.

The post Subscription App Growth: Insights from the Frontlines appeared first on Business of Apps.

For app marketers, the narrative of the last few years has been consistent — and consistently challenging. The easy wins on traditional social and search platforms are gone. The cost of acquiring users on the duopoly continues to climb, while performance slowly erodes. Users are tuning out saturated ad formats, and standing out on Google Play or the App Store feels increasingly like a lottery.

The result? Even top-tier brands are paying more for less.

As we move into 2026, diversification is not just a buzzword but a survival strategy. While social and search remain critical, a “third pillar” of app growth has emerged that offers the scale, retention, and efficiency UA managers are desperate for: mobile OEM advertising.

To help marketers navigate this ecosystem, AVOW has released the Mobile OEM Advertising Guide 2026. It offers a blueprint for accessing the untapped potential of the world’s leading smartphone manufacturers, including Samsung, Xiaomi, Huawei, OPPO, Vivo, Transsion, and Honor.

The scale of the opportunity

There is a common misconception that OEM advertising is a niche channel or limited to specific emerging markets. The data tells a different story.

By partnering directly with mobile manufacturers, advertisers gain access to over 1.85 billion daily active users. These partnerships cover approximately 86% of the global Android market, representing the vast majority of users outside of iOS.

This isn’t just about volume, but about granular, device-level reach.

• Samsung remains the dominant force in Europe and Latin America.
• Transsion (Tecno, Infinix, itel) commands over 50% of the African market.
• Xiaomi and Vivo are powerhouses in Southeast Asia and India.

For global apps, ignoring this inventory means ignoring the native ecosystem where these billions of users live.

Beyond the install: Innovation in ad formats

The Mobile OEM Advertising Guide 2026 highlights that this channel has evolved far beyond simple factory preloads. While dynamic preloads remain a powerful way to be on a device from day one, the modern OEM ecosystem offers high-impact, brand-safe inventory that spans the entire user journey.

The guide details formats such as:

• The “minus-one” screen: Native placements located on the dashboard immediately to the left of the home screen, targeting users in a “lean-in” mindset.
• Lock screen magazine: High-resolution visuals that act as a personal billboard, generating impressions before the device is even unlocked.
• Native app store placements: Banners and search ads within the manufacturer’s own app stores (like Galaxy Store or GetApps), capturing high-intent users actively searching for new apps.

Efficiency and ROAS as the new benchmarks

Perhaps the most compelling argument for the “third pillar” is cost efficiency. As competition on Meta and Google drives bidding prices to unsustainable levels, OEM inventory remains a comparative blue ocean.

Our data shows that apps running campaigns via OEM channels consistently see approximately 30% lower cost per acquisition (CPA) compared to traditional app store marketing.

But the metrics that matter most in 2026 are return on ad spend (ROAS) and lifetime value (LTV). Because OEM placements are native to the device experience, they attract users who exhibit stronger long-term retention.

• Exness, a fintech trading platform, achieved a 1000% ROAS by leveraging OEM inventory in emerging markets.
• Joom, a leading e-commerce platform, secured a 150% ROAS by positioning itself as a default shopping utility during device setup.
• Funvent Studios exceeded retention goals with a Day 7 retention rate of 8.5%.

The roadmap for 2026

The mobile landscape is shifting. To escape the rising-cost, shrinking-returns trap, marketers need to look where the competition isn’t.

The Mobile OEM Advertising Guide 2026 is designed to be a practical manual for this transition. It covers everything from in-depth analyses of regional market shares to technical specifications for the latest ad formats. It moves beyond theory to provide actionable strategies for building a sustainable, fraud-free growth engine.

Access the full Mobile OEM Advertising Guide 2026 here.

The post Beyond the duopoly: Why mobile OEM advertising is the ‘third pillar’ of 2026 growth strategy appeared first on Business of Apps.

Mobile apps are now at the core of shopping, communication, finance, and content consumption, and it is becoming more and more challenging to maintain engagement. The higher the number of push notifications a user receives in the first 90 days of using an app, the greater the likelihood that they will stick to a particular app.

However, the retention is a problem: the gap between the first and the second usage is almost 25%, and merely a third of users come back within 90 days.

Meanwhile, the cost of installs of new apps has grown as well, and it is quite expensive to replace the churned users. Consequently, user-interest has become a key measure of app success. In order to maintain interaction and create long term value, application teams are turning to permission-oriented re-engagement vehicles – most prominently, mobile push notifications.

Research findings back this method, with just a single push notification in the first 90 days raising 90-day retention by 147% and multiple messages per day increasing retention by up to 285%.

So let’s see how to achieve success with right and strategic use of mobile app push notifications.

What effective app engagement means?

Authentic engagement indicates long-term interest, contentment, and a definite relationship between user action and business worth.

Apps that do well have high levels of engagement, meaning they can meet specific criteria on major metrics: users are returning (high Day 30 and Day 90 retention), the frequency of interaction is sufficient to make the app a part of their life (good DAU/MAU ratio), and users make value-based decisions, such as purchasing something, subscribing, or adopting a feature.

These metrics can only show excellence when they are shifting together, and users are not just present, but interested in the matter.

Interaction and personalisation lead to quality engagement

Quality of interaction is hosted by interactive experiences that are considered to have relevance to every user, particularly via push notifications. The effective notifications are customized according to user behavior, preferences, or situation, and it results in content that can directly fulfill user needs.

They do not automatically open the app, but promote purposeful behaviour, such as exploring the features, accomplishing tasks, continuity of unfinished tasks, or in-app promotions.

When users regularly communicate once they open the app, it is an indication that there is intentional engagement and not an accidental one. It is the difference between mere usage and meaningful interaction, and the distinguishing factor between an app being habit-forming or not.

The reason strong engagement has long-term business impact

Long-term quality interaction directly affects the long-term business results. Frequently and deliberately interacting consumers are better off staying loyal, converting at better rates, and becoming lifetime value generating. In the long run, this forms a cycle of growth, with engagement enhancing retention, retention enhancing revenue, and revenue enhancing further innovation.

Interaction is a key factor to long-term app value. The studies which have been published in Harvard Business Review reveal that a 5% higher retention can increase profits by 25% to 95% based on industry. Heavy users also tend to leave feedback, refer their friends to the app, and stay loyal even in case of competitor products.

In this respect, engagement does not merely look like a performance indicator, but it is a strategic tool that affects lifetime value, revenue maintainability, and growth sustainability.

Unlock higher app engagement with mobile push notifications

Push notifications are also beneficial to enhance engagement as it will allow the app experience to be prolonged outside of active usage sessions. Rather than waiting until users come back on their own, users are given a chance to re-engage with users using time-sensitive, relevant notifications.

Timely re-engagement outside the app

Immediacy is one of the main benefits of push notifications. Notifications are displayed on the devices of the users, which enables apps to provide updated data, reminders, or alerts at the time when users can pay attention or not.

• JibJab (digital entertainment studio) grew the Click-Through Rate (CTR) by 30% through automated, personalized and timely (push notifications) to promote content creation.

Behavioral and contextual triggers

Interaction will be better when notifications are activated by certain actions or circumstances. As an example, informing the user about the abandoned cart or the steps of the onboarding process, or new content helps provide a sense of continuity between the past activity and the next one. Leanplum says that behaviour-based messaging performs better than generic alerts in the open rates and downstream messages.

• KFC India applied re-engagement promotion to reach out to customers that had not redeemed rewards in the recent past. The campaign led to the increase of daily orders by 22% growth in revenue.

Personalisation, segmentation, and relevance

To avoid notification fatigue, personalisation is needed. A message that is specific to the user preferences, location, or stage of the lifecycle is more helpful than intrusive. An example of this is an app that sends an alert about the topic being reported or an app that confirms a transaction, which will offer perceived value immediately.

• Beyond the Rack online retailer increased repeat visits by 50% within three months by using timely, personalized push notifications for short-term offers.

Such strategies can be used to position the mobile phone push notifications as service-oriented messages and not promotional interruptions.

Supporting multiple engagement scenarios

Push notifications are accommodative to many engagement processes, such as onboarding direction, transactional updates, promotional announcements, and requests.

•  Omada achieved a 67.4% conversion rate by using a push-driven onboarding campaign for multiple engagement purposes.

They can be leveraged to enhance the usefulness of the app in its whole customer experience.

Choosing the right mobile push notifications platform

Flexibility and reliability do not have the same degree in all push notification solutions. When selecting the appropriate platform, it is necessary to consider a number of technical and strategic issues, and choose the right service for mobile web push notifications for iOS or Android.

Delivery reliability across platforms

Dependable delivery of messages is at the core. It should be based on both mobile web push notifications iOS and mobile web push notifications Android, to ensure that it reaches everyone irrespective of the device or operating system. Failure to deliver detracts the involvement and trust in the user.

Segmentation and personalisation capabilities

High-level segmentation will allow it to target messages according to the behaviour, demographics, or real-time events. Social networks with limited adaptable viewer definitions may compel organisations to use unspecific marketing messages, making them less relevant and encouraging more opt-outs.

Workflow management and automation

With automation, teams can react to user actions immediately without having to be involved. Trigger campaign, lifecycle campaign, and scheduled notification assists in sustaining the continued engagement with minimal operational effort.

Continuous optimisation and analytics

Strong analytics help the teams monitor the delivery rates, open, conversions, and the overall effect on the engagement over time. Such insights would be essential in the optimisation of strategies and enhancement of results in the long-term. Analytics depth must be a consideration when analysing a mobile push notifications service.

Supporting app engagement at scale with EngageLab AppPush

As applications scale, maintaining effective engagement becomes increasingly difficult due to inefficient communication workflows, limited automation, fragmented delivery infrastructure, and rising expectations for personalisation across user segments. Without centralised systems, teams often struggle to deliver timely and relevant notifications, leading to delayed campaigns, inconsistent reach, and weaker retention signals.

For global apps, additional challenges include platform dependencies, OEM limitations, and the need to ensure notifications feel local rather than generic. Capabilities such as multilingual messaging, automation, omni-channel delivery, and time-zone optimisation help ensure messages arrive at the right moment, feel regionally relevant, and drive higher response rates.

Intelligent and accurate AppPush notifications

Source: EngageLab

Centralising campaign management and performance measurement is equally important, allowing teams to align execution, test variations, and understand the role of push notifications in overall engagement and retention outcomes.

When these challenges are addressed together, push notifications become predictable, context-aware touch-points rather than isolated messages, improving response rates and supporting consistent engagement throughout the user lifecycle.

Within this framework, web and mobile app push solutions like EngageLab AppPush can be applied to this framework as it allows doing structured automation, centralized control, and consistent delivery at scale.

EngageLab helps teams overcome fragmented communication, limited automation, and unreliable delivery, enabling global apps to send timely, localised notifications through time-zone optimisation and multilingual support. Sign up for free and experience it firsthand today.

The post How to increase app engagement with mobile push notifications appeared first on Business of Apps.

Mobile app advertising, monetization, and insights company Liftoff is bringing together inspirational women from across the mobile industry to share their stories. In this series, we’ll explore their achievements, the challenges they’ve overcome, and their vision for the future — offering insights from the experts shaping the world of mobile apps and technology.

This month’s article focuses on Ulyana Guseva, Head of Advertising Sales at Citymapper.

Can you start by walking us through your career path — how did you get to where you are today?

I actually started my career in hospitality and consulting before I moved into the tech industry, joining Google to work on advertising sales for small and medium-sized businesses in Ukraine, Belarus, and Kazakhstan. That was a very interesting role focused on helping SMBs in those markets grow with the help of Google Ads.

From there, I moved on to Waze (a Google-owned company), which was my first foray into the mobile space. I was still working in advertising sales, but shifted my focus to the UK market and began working with enterprise-level clients. I stayed there for five years in various roles.

Two years ago, I moved to Citymapper, so I have stayed within not just mobile apps, but specifically mobility apps, which has become my area of focus. My current role revolves around growing our advertising revenue across global markets and various channels. This involves overseeing all aspects of the ads business, from product development and go-to-market strategy to sales, covering direct brand and agency partnerships, resellers, and the auction side of the business.

When you were going into your first role in consulting, did you have any idea that your career would move into the tech side of things?

If I’m honest? Not at all! When I was starting out, the tech world just wasn’t the massive, central force it is today. I don’t think we fully realized how big it was going to become.

I actually spoke with some students at an alumni event recently and asked them about their career aspirations. Some of them said, “I don’t know, I’m very open.” I think that’s the best possible approach. Being open to opportunities is crucial because you truly never know how things will change tomorrow. My career moving into tech is a great example of that.

Are there any particular skills that you have found really important as your career has developed? What skills do you feel have helped you progress in your career?

I believe the most important ones are openness to change and eagerness to learn. Things in this industry, especially in tech, evolve so rapidly. Accepting change makes everything easier, not just in terms of finding the next opportunity but also in everyday life, as new things emerge all the time. If you resist rather than adapt & learn, that creates its own problems.

What’s the thing about your job that you most enjoy?

There are lots of things! I really enjoy working with clients and helping them achieve their goals. It sounds cheesy, but because my first job was in hospitality, client service is a big thing for me. Delivering a good service and bringing value to my advertising clients is very important to me. When they’re happy with the results, it makes me happy too.

Another thing I really enjoy, which can be challenging sometimes, is working with different teams across the company. From day one at Citymapper, I’ve been involved in conversations with various teams, which have given me the incredible opportunity to learn how different teams think and what they care about. One common factor, of course, is that the whole Citymapper team cares deeply about the app’s users and strives to do everything possible to make their lives as easy as possible.

Have you encountered any challenges or barriers in your career?

For sure. Working across so many different international markets has definitely been a big challenge, but also a huge opportunity.

I had to learn everything about each market I’ve worked with (Ukraine, Belarus, Kazakhstan, and even the UK), totally from scratch. I hadn’t lived in any of those countries before I started, so I had to learn really quickly. This meant diving deep into local business conduct, market ecosystems, key players, and sometimes even learning a new language just to understand the nuances. That learning process was intense!

When I look back, I think “oh, that was fine,” but at the time, I was often overwhelmed by the complexity. For instance, working with clients in the UK media industry, I quickly understood that performance and merit are essential, but success is also heavily reliant on building strong relationships. That was a big shift for me.

More recently, joining Citymapper presented a whole new challenge: getting to grips with the programmatic advertising ecosystem. Honestly, two years in, I feel like I know it better; however, the ecosystem is a massive, constantly evolving beast – I don’t think anyone can claim to understand every single corner of it.

Do you have any ideas about where you want to take your career next?

We have been building this business for just over two years, and it has been a very interesting journey. We’re not near the finish line. For me, evolving this and developing different markets aside from the UK, such as France and others, is my next step. I want to explore how we can effectively establish and build the Citymapper Ads brand. The app is already well-known and has millions of active users every month. As with everything, though, there’s always room to grow and improve.

How are you differentiating what you do at Citymapper?

Citymapper is quite unique in the app ecosystem. We function as the daily co-pilot for millions, used multiple days a week for reliable, up-to-the-minute planning. We are also the most reliable app during major strikes or disruptions, because we provide the most reliable information instantly. This is why people trust us and immediately turn to us during challenging moments.

Beyond its utility, the app is very playful and quirky, which users love. This unique approach creates a special rapport.

For advertisers, this brings a dual appeal: some brands lean into the quirkiness with custom executions and tailored messaging, while others value our foundational role as a trusted utility and the ability to connect with users in high-attention, relevant moments.

Does your work go out directly to brands, their agencies, or both?

Both! Especially in the UK, the media industry is quite complex. Sometimes you work directly with brands, but more often than not, you work with the brands and their media and creative agencies.

The brands we work with are very different from one another. For example, we recently launched a campaign with Universal Pictures for Wicked: For Good. We also work with quick-service restaurants such as McDonald’s, pubs like Greene King, retail stores like Boots, and finance companies like Chase Bank and Barclays. It really depends on the brand when it comes to how we work with them, but we help them tap into the different moments of the user journey and see where it can be helpful for them to be present. That way, they can bring value to our users and step in at the right moment.

How is AI impacting what you do? Is it influencing the ad tech platforms and services you work with, and is it being integrated into the Citymapper app?

It definitely is influencing all parts. For me, it’s great to see how it’s simplifying a lot of things and shifts your work to more strategic tasks where possible. In my team, it helps us be more productive. AI speeds up many of the processes, from brainstorms to execution, and it helps us identify where else we can focus our time.

We need to learn from AI and see how we can utilise it most effectively to make our lives a bit easier. It assists us in what we do and what we focus on.

What advice would you give to other women to ensure they receive the recognition they deserve for their work?

Sometimes we can get lucky and be championed by other people, but I’d say being your own cheerleader is key. We often put a lot of pressure on ourselves and have a feeling that we’re not good enough, or have imposter syndrome. So I’d say step one is getting on your own side, being kind to yourself, and cheering yourself on.

From there, the path to recognition is about starting small and asking for what you need. Take chances to present your work by sharing updates with just your manager or a peer first. The more you do it, the more comfortable it gets. I’m often surprised by how true the “don’t ask, don’t get” saying is.

While sometimes it might be terrifying to ask for things, including recognition, no one will care about you more than you will. Also, don’t underestimate how willing people (our managers, mentors and peers) are to help. If you’re nervous about presenting, for example, ask a colleague to pass the conversation on to you deliberately in a meeting. It’s often the quickest way to break through a barrier.

The post A journey to Citymapper: Ulyana Guseva on embracing change in tech appeared first on Business of Apps.

Nearly every SaaS product is either integrating AI or planning to do so. However, the term “AI” has become so broad that it’s often misunderstood, especially by non-technical founders. Words like “chatbot,” “AI assistant,” and “agent” get used interchangeably, even though they represent very different capabilities.

That confusion can lead to misaligned features, wasted development time, and missed opportunities for automation or personalization.

This guide explores the key distinctions between chatbots and AI agents, two of the most prevalent “AI-powered” tools in SaaS products today. You’ll learn:

• What each one actually does
• When to use one (or both)
• And how to avoid common implementation mistakes

If you’re building a product and want to use AI effectively, this article will help you choose the right tool for the job and design more innovative user experiences from the start.

What is a chatbot? (And what it’s best at)

A chatbot is a conversational tool designed to follow rules, scripts, or decision trees. It doesn’t understand the user in a meaningful way; it simply matches inputs to predefined intents and responds accordingly.

Think of it as a guided flow: The user selects or types something → the bot matches it to a rule → the bot responds with a scripted answer.

Because of their simplicity, chatbots are ideal for:

• Answering common questions (e.g., pricing, login issues)
• Routing users to the correct department or support tier
• Walking users through predictable flows, like onboarding or tutorials

They’re fast to implement, easy to update, and often require no-code tools to deploy. That makes them especially useful for early-stage SaaS teams looking to automate without a heavy investment in infrastructure or AI training.

If your use case is narrow, predictable, and doesn’t require real-time data, chatbots are often the smartest (and safest) place to start.

Example: A chatbot on a login screen might ask, “Need help signing in?” If the user clicks yes, the bot can walk them through password reset instructions, no human required.

What is an AI agent? (And why it’s different)

An AI agent goes beyond pre-written responses. It’s an autonomous system that can understand context, make decisions, and take action based on goals rather than scripts.

Where chatbots follow predefined paths, AI agents are goal-oriented. You define what they should achieve, and they figure out how to do it using memory, logic, tools, and data access.

Many AI agents use large language models (LLMs) and frameworks like AutoGPT, LangChain, or BabyAGI, which allow them to:

• Break down multi-step tasks
• Pull and interact with live data
• Adapt based on user history, behavior, or preferences

Example: A user asks to reschedule a meeting. An AI agent might:

• Check calendars for availability
• Suggest time slots
• Send updates to all attendees
• Notify participants all without human input

If your product needs to do things, not just answer things, you’re likely in AI agent territory.

When should you use a chatbot vs. an AI agent?

This isn’t a question of which is more advanced; it’s about which tool fits the job. Both chatbots and AI agents have distinct strengths depending on your product’s goals, stage, and complexity.

Here’s a breakdown:

Use a chatbot when:

• You want to automate simple, repetitive flows (e.g., FAQs, onboarding)
• The goal is to guide users, not complete tasks
• You’re in an early stage and need lightweight automation
• There’s no need for real-time data, personalization, or memory
• You want to test assumptions before investing in complex logic

Use an AI agent when:

• The system needs to take action, not just respond
• Your product involves multi-step flows or decision-making (e.g., scheduling, document generation)
• You need context awareness, like remembering user preferences or history
• Your value proposition depends on scalable personalization or automation

Start with what your users actually need. If they just need answers, start with a chatbot. If they need tasks completed, it’s time to explore agents.

Can you combine chatbots and AI agents?

Yes, and this is where things get interesting. Many SaaS teams are combining both tools into a hybrid architecture: the chatbot handles conversation and flow control, while the AI agent executes the task behind the scenes.

It’s a “best-of-both-worlds” setup:

• The chatbot provides structure: greeting users, clarifying intent, gathering input
• The AI agent delivers outcomes: pulling data, taking action, triggering workflows

Example: A user says, “Can you move my 3 PM meeting to Thursday?”

• The chatbot engages the user, confirms details, and ensures clarity
• The agent checks the calendar, reschedules the event, sends notifications, and logs the change all automatically

In this model, the user never leaves the conversation. But behind the scenes, a lot is happening to fulfill their request. This layered approach is quickly becoming a go-to pattern for SaaS products looking to deliver human-like UX with real utility, without over-engineering the surface interaction.

Chatbot vs. AI agent: Side-by-side comparison table

Common mistakes non-technical founders make

Understanding the difference between chatbots and AI agents is one thing. Building with that difference in mind is another. Here are the most common mistakes non-technical SaaS founders encounter and how to avoid them:

Assuming every chatbot is “AI”

Many chatbot tools are labeled as AI-powered, but most operate using decision trees or keyword matching. That’s not inherently bad, but it’s important to understand what’s behind the label. Not every conversational interface is intelligent.

Overloading the chatbot

Trying to make a basic chatbot handle complex logic, user memory, or multistep actions creates friction for both users and developers. If your flow starts to include decisions or dynamic data, it’s likely time to graduate to an agent.

Skipping user context

Whether you’re using a chatbot or an agent, skipping user-specific inputs like account data, behavior, or preferences leads to generic, flat interactions. Personalization doesn’t need to be advanced to be effective, but it does need to be intentional.

Deploying an agent without a defined goal

AI agents need clear objectives to be effective. Without a defined outcome (“schedule meetings,” “summarize notes,” etc.), agents can become unfocused, difficult to test, and hard to maintain. Start with a single, measurable task.

Ignoring privacy and data risk

AI agents often interact with sensitive data: calendars, documents, user records. Guardrails are essential. Authentication, permission scopes, and audit logs need to be part of the conversation early, not after deployment.

Read our article about app scalability to learn more about ideal growth for your business. 

Strategy tips for SaaS founders navigating AI

If you’re not technical, but you’re leading product strategy, your job isn’t to write code; it’s to ask the right questions, define outcomes, and scale what works. Here’s how to approach chatbot and AI agent decisions with clarity:

Start small, then scale with intent

You don’t need to launch with full autonomy on day one. Use chatbots to test assumptions: what do users ask? Where do they get stuck? Which flows repeat? Let patterns emerge before investing in more complex builds.

Match tools to outcomes

Don’t think in terms of “AI features,” think in terms of what your users need. A chatbot might be the right answer for one flow and an agent the right fit for another. Focus on utility, not labels.

Design for evolution

Plan ahead by asking: Can this chatbot become an agent later? Tools and frameworks that support growth (e.g., plugin-ready systems, modular flows) give you flexibility as the product matures.

Use behavior, not assumptions, to guide decisions

If you’re considering an agent, start with the task you want to automate and work backwards. Behavior-based development is clearer, cheaper, and more measurable than building based on feature hype.

Develop a goal-oriented roadmap with this guide to ensure AI additions provide value. 

Real-world use cases and takeaways

Real examples offer insight into how chatbots and AI agents function in SaaS products and where they each add the most value. Below are two use cases that illustrate key principles for building AI-powered experiences.

Use case 1: Hybrid chatbot + agent for scheduling

A SaaS tool needed to streamline meeting rescheduling for its users. A chatbot collected the initial request (“Move my 3 PM to Thursday”), verified intent, and passed it to an AI agent.

The agent checked the calendar, suggested available time slots, updated invites, and notified attendees without manual intervention.

Key takeaway: Use the chatbot for structure and UX, and hand off task execution to the agent. This hybrid model improves user flow and keeps systems modular.

Use case 2: Smarter support routing with context

A platform launched a chatbot to field FAQ-style questions but found users needed more complex support, involving account history and dynamic routing.

Rather than expanding the chatbot’s logic endlessly, they implemented an AI agent capable of assessing the query, retrieving user data, and routing it based on urgency and context.

Key takeaway: When support logic requires memory or real-time data, introducing an agent improves flexibility and keeps the chatbot focused on its strengths.

Clarity over hype

As AI becomes more accessible, the real differentiator isn’t how much of it you use it’s how wisely you apply it.

Chatbots are excellent for structured guidance and repeatable flows. AI agents unlock autonomy, context, and intelligent action. The strongest SaaS experiences often combine both, with clear roles and intentional design.

For founders, especially those without a technical background, the goal isn’t to “add AI,” but to solve real problems with the right level of complexity. Start small, validate user behavior, and scale intentionally.

In the end, the smartest products aren’t the ones with the most AI they’re the ones that remove the most friction.

The post Chatbot vs. AI agent: What’s the difference and how to make the right choice appeared first on Business of Apps.

When users browse the app stores, every visual, color, and word on your app listing tells a story. Whether it is a bold product page, metadata crafted with strategic keyword usage, or screenshots highlighting your app’s strongest value propositions, every element influences how potential users experience your brand before they ever download. This experience, and the intentional way your app is presented to inspire action, is merchandising.

In the world of mobile apps, merchandising is both an art and a science. App stores’ algorithms pick what is relevant to users, then display those apps in the app stores. Balancing app store’s algorithms with implementation of ASO best practices is what keeps your app relevant, and in return, creates opportunities for your app to be more visible to the right audience.

Merchandising is the practice of shaping how users perceive value, encouraging engagement during key seasonal moments, and influencing whether visibility leads to conversions. Strong merchandising transforms your app listing into a dynamic storefront that adapts to user behavior, market trends, and creative experimentation.

The success of merchandising depends on data-driven App Store Optimization (ASO). ASO increases your app’s visibility in search and browse, while merchandising ensures that visibility converts. Together, these strategies create a comprehensive growth framework that places your app in front of the right audience at the right time, then compels them to take action.

Driving first-time app installs through merchandising

The first goal of merchandising is clear: attract new users through first-time installs. Every element of an app listing should be designed to convert impressions into downloads, guided by measurable data.

Tracking first-time installs as a core performance metric and comparing those results year-over-year can provide valuable insights into growth trends. Analyzing performance during key seasonal windows such as fall, Halloween, or major shopping events reveals how user behavior shifts and which creative strategies deliver the strongest returns.

For example, a 10 percent increase in installs compared to the previous year during the same period could validate a new visual direction, a refined keyword strategy, or updated messaging. Merchandising connects creative presentation with measurable business outcomes, allowing data to guide each iteration.

The most effective merchandising strategies are built on consistent KPI tracking and continual refinement. Each campaign becomes an opportunity to learn, improving decision-making and performance season after season.

Optimizing your app store front with A/B testing and custom product pages

A/B testing is the foundation of a strong merchandising strategy. It allows developers to validate creative and messaging decisions through data rather than assumptions, turning every update into a measurable opportunity for growth. By systematically testing variations of product page assets, teams can identify which visual sequences, icons, taglines, or calls to action most effectively drive conversion.

Custom Product Pages (CPP’s) take this optimization further. These pages allow developers to create multiple, campaign-specific storefronts that target audience segments, acquisition channels, or seasonal campaigns. For example, a grocery delivery app might design one product page emphasizing fresh produce and another focused on household essentials. Each experience aligns with a user’s intent and reinforces the campaign message that brought them to the listing.

When refining CPPs, the first three screenshots carry the most influence. These visuals appear directly in search results and must communicate the app’s core value proposition within seconds. Merchandising at this level is about precision. Every creative decision should be supported by analytics that identify which assets drive the strongest engagement and conversion. Over time, this structured testing builds a cycle of continuous improvement that directly impacts growth.

A/B testing and CPP optimization together create a self-sustaining system of learning and refinement. Each iteration provides insight into user preferences, allowing teams to refine creative direction, sharpen messaging, and maintain an app store presence that evolves with audience behavior.

Merchandising tools: Search Ads, Google Ads, and app keyword alignment

Merchandising performs best when supported by targeted acquisition channels that amplify reach and connect with users at the moment of intent. Apple Ads and Google Ads are two of the most effective tools for this purpose.

Apple Ads help developers reach high-intent users within the iOS ecosystem by matching user search queries with the most relevant metadata and creative assets. When paired with strong merchandising, these ads do more than increase visibility—they drive conversion. A product page that reflects the same messaging and visual themes as an ad campaign creates a seamless user experience, improving cost efficiency and strengthening conversion rates.

Google Ads extends these same principles to Android audiences. Developers can test creative variations across different audience groups and optimize for conversion signals that improve both paid and organic performance. Aligning Google Ads creative assets with store listing visuals and metadata ensures message consistency and reinforces user trust at the point of decision.

Both Apple and Google platforms provide feedback loops that enhance ASO. High-performing ad keywords highlight terms with strong user intent, which can inform future metadata strategies. Similarly, engagement data from custom product pages can signal to store algorithms that your app is highly relevant to certain searches, potentially improving organic rankings over time.

Apple Ads, Google Ads, and merchandising together create a connected growth system. Paid campaigns attract qualified traffic, merchandising converts that traffic with relevant creative, and ASO integrates those learnings into long-term visibility. The result is a sustainable, performance-driven marketplace presence.

Crafting effective app keyword strategies

A developer’s keyword strategy is just as critical as their creative strategy when it comes to merchandising success. Selecting high-volume, high-intent terms that accurately reflect your app’s purpose and audience ensures your efforts are focused on meaningful discovery rather than vanity metrics.

Keyword volume reflects real user behavior and cannot be artificially influenced. Developers should prioritize relevant, intent-driven terms that accurately describe the app’s functions and appeal to its target audience. A strong keyword plan balances competitiveness with specificity, capturing the users most likely to convert.

Competitive analysis plays a central role in this process. Examining how competitors rank for key terms, identifying gaps, and targeting overlooked but valuable queries can yield significant results. For example, a budget travel app might find “cheap flights” to be highly competitive, but “last-minute hotel deals” could provide stronger visibility and engagement.

When integrated with merchandising, keywords act as the entry point to your app’s storefront. Discovery begins with search, but the decision to install depends on how well your listing communicates value once the user arrives.

App store seasonal planning and mobile app A/B testing

Merchandising thrives on preparation. Apps that plan creative updates and metadata changes well in advance of high-traffic seasons are best positioned to capitalise on shifting consumer interest.

Whether the goal is to align with holiday promotions, summer travel trends, or cultural moments, early planning ensures sufficient time for creative development, internal approvals, and A/B testing. Seasonal optimization does not always require a full redesign. Subtle updates, such as limited-time offers, festive color accents, or refreshed metadata, can have a meaningful impact on performance.

Other brands may choose a more dramatic seasonal approach, launching fully themed visuals for major events. In both cases, consistent testing determines which creative directions resonate most with target audiences.

Even small performance gains can have a major impact. A 1-2 percent lift in conversion during a major holiday week can result in thousands of new installs. Over time, these incremental gains build strong seasonal benchmarks and compound into long-term growth.

Seasonal merchandising also helps keep your app’s presentation fresh. Users notice when listings evolve with current trends and events. This reinforces brand relevance, communicates that the app is actively maintained, and encourages continued user trust and engagement.

Common misconceptions in merchandising

One of the most common challenges in merchandising comes from within: overly rigid creative guidelines. Many teams hesitate to experiment with new visuals or messaging, fearing they will break brand consistency. While brand integrity is important, flexibility often leads to discovery.

Strict creative alignment can limit experimentation and reduce opportunities to connect with new audiences. A willingness to test new color palettes, visual layouts, or messaging tones can uncover valuable insights that drive engagement.

For example, a productivity app might discover that brighter, more optimistic visuals improve engagement among younger audiences compared to a traditional or muted design. Without A/B testing, those insights would remain hidden.

Merchandising is a balance between creativity and control. The goal is not to abandon brand identity, but to let data guide how and when to evolve it. Brands that grow consistently are those that measure, iterate, and make evidence-based creative decisions rather than relying solely on assumptions.

Merging merchandising and user acquisition for full funnel optimisation

Merchandising becomes even more powerful when aligned with user acquisition. Together, these disciplines create a full-funnel strategy that strengthens both downloads and long-term growth.

The connection begins with message consistency. If an ad promotes “exclusive holiday deals,” the screenshots and messaging on the app store page should reinforce that exact promise. When users encounter consistent messaging from ad to store, the result is higher trust and improved conversion performance.

This alignment also improves cost efficiency. When paid traffic converts more effectively due to data-informed merchandising and optimized ASO, cost per acquisition declines. Over time, this performance loop compounds, allowing teams to scale paid campaigns without inflating budgets.

Paid acquisition and merchandising ultimately reinforce one another. Paid ads drive high-intent traffic, while optimized app store listings improve conversion rates and organic visibility. This synergy builds a sustainable growth cycle based on visibility, credibility, and performance.

Final thoughts

Merchandising represents the intersection of creativity, strategy, and data. It transforms app listings from static product pages into dynamic storefronts that evolve in response to audience behavior, seasonal trends, and performance insights.

The strongest merchandising strategies are grounded in testing, analysis, and adaptability. Every asset, from metadata to screenshots, should be designed to communicate value and drive measurable results.

When integrated with ASO, merchandising becomes more than an aesthetic exercise. It becomes a strategic growth engine that powers visibility, conversion, and retention across the app’s lifecycle. In a competitive marketplace where attention is fleeting and user decisions are data-driven, authoritative merchandising is what separates leading apps from the rest.

Let’s chat

Each season, app update, and campaign offers new opportunities to refine creative direction and improve visibility. For many teams, the challenge lies in connecting all these moving parts into a single, data-driven strategy.

That is where expert guidance can make a measurable difference. Our ASO services can help developers identify optimization opportunities, test what matters, and translate insights into performance. Whether your goal is to strengthen conversion, align your app with seasonal trends, or refine your long-term growth strategy, ASO support can provide the framework you need to scale with confidence.

If you are ready to explore how merchandising and ASO can work together to maximize your app’s potential, our ASO experts are here to help you take the next step.

The post The power of merchandising and ASO appeared first on Business of Apps.

For years, app growth strategies have prioritized acquisition. Retention, engagement, and lifetime value were often treated as downstream effects. That balance is shifting. As competition intensifies and paid acquisition becomes less efficient, app teams are increasingly focused on keeping users active, connected, and invested.

In an App Talks video interview recorded at Business of Apps Berlin 2025, Peggy Anne Salz spoke with Luigi Manrique, Account Executive at Stream, about how in-app communication and community features are evolving from optional enhancements into core product infrastructure. The discussion explored what it takes to architect communities that feel native, trustworthy, and measurable across various categories such as fintech or fitness.

From features to foundations

According to Manrique, one of the biggest challenges app teams face today is not deciding whether to build community features but how to start. Many teams have ideas inspired by competitors or market trends but struggle to translate them into scalable, performant experiences.

The risk is execution. A well-intentioned social feature that feels slow or disconnected can do more harm than good. Community, Manrique argues, has to be architected into the product and not just layered on top of it.

That requires thinking beyond individual features and toward systems that support user-to-user interaction, two-way communication between the app and its audience, and feedback loops that inform product decisions.

Trust and tone in regulated categories

Community-building looks very different depending on category. Fintech is a clear example.

While social interaction can drive engagement, trust remains the defining constraint. Users expect seriousness, clarity, and control when money is involved. Manrique pointed to Robinhood as an example of how social functionality can be introduced without undermining credibility.

Robinhood lets users to follow others, observe activity, and discover context around trends without forcing interaction. The experience mirrors familiar social mechanics such as feeds, follows, and likes, but within boundaries appropriate to finance.

The result is not entertainment for its own sake, but informed participation. Users learn from one another, discover what is gaining attention, and contribute signals that the platform itself can analyze to understand sentiment and interests.

How in app communities are becoming a growth engine

Source: Business of Apps via YouTube

Community as a data signal

One recurring theme in the conversation was feedback. In-app communities generate more than engagement metrics. They produce qualitative and behavioral data that product teams rarely access through traditional analytics alone.

Likes, follows, comments, and dwell time indicate not just what users do, but what they care about. When analyzed correctly, these signals help teams identify emerging interests and refine their content strategies.

Manrique emphasized that community works best as a loop. User interaction generates insight. Insight informs content and feature decisions. Those decisions then shape future interaction. Over time, this creates a self-reinforcing system that increases relevance and stickiness.

Performance, moderation, and rollout discipline

While community features promise many upsides, they also introduce complexity. Manrique stressed three areas where teams often underestimate the work involved.

First is performance. Social features are highly visible and heavily used. Latency, instability, or security issues quickly erode trust.

Second is moderation. Keeping users safe and conversations aligned with brand values requires clear rules and tooling. Moderation strategy cannot be an afterthought.

Third is rollout. Launching community features too early, without internal alignment or user education, risks low adoption. Manrique noted that sometimes the hardest advice to give partners is to slow down until the product, onboarding, and communication plan are ready.

Retention economics and monetization

From a business perspective, the case for community ties directly to retention economics. Time spent in-app correlates with monetization opportunities, whether through advertising or subscriptions.

Manrique outlined several common models, including freemium tiers, access to exclusive content, and social enrichment that increases overall platform value. The key is alignment. Monetization should emerge naturally from participation, not disrupt it.

Crucially, these strategies are measurable. Engagement depth, session frequency, and content interaction provide concrete signals that CFOs and product leaders can evaluate against investment.

Communities do not migrate easily

One of the strongest retention effects of community is inertia. Users may switch apps easily. Communities do not.

Once social graphs, shared history, and norms are established, switching costs increase significantly. This dynamic helps explain why being early matters, but also why it is rarely too late. Apps without meaningful community risk widening the gap with competitors that already have it.

As Manrique noted, the risk is not entering a crowded space. The risk is opting out entirely.

Looking ahead to 2026

As acquisition slows and differentiation becomes harder, in-app communities are moving from experimental features to strategic assets. They influence retention, monetization, product direction, and brand trust.

For app teams planning ahead to 2026, the takeaway is clear. Communication is no longer just messaging. It is infrastructure. And community, when architected with intent, is one of the most durable growth levers available.

Watch the full video, embedded above, to discover all of Luigi’s insights. You can also watch all episodes of App Talks here.

The post How in app communities are becoming a growth engine appeared first on Business of Apps.

Top 40 publishers by footprint in Top 250

Source: ConsultMyApp

Top 20 publishers by monthly download volume

Source: ConsultMyApp

If visibility is repeatable, growth is learnable

Voodoo’s mobile game portfolio by US downloads

Source: ConsultMyApp

Creating demand in a world of interchangeable products

The post Demystifying the App Store appeared first on Business of Apps.

As 2026 unfolds, the era of manual campaign optimization is effectively over. The next phase of app growth is defined by precision, automation, and direct integration into the device itself. Advertisers are no longer just looking for volume; they demand predictable, incremental results. The global digital advertising spend is forecast to reach $753 billion in 2026, with mobile in-app revenue accounting for 56% of this total.

Reflecting on recent market analysis and insights from my conversations with major industry players like Samsung and Xiaomi, here are the 10 mobile advertising trends that define the landscape for the year ahead.

Automation becomes the norm

Campaign optimization used to require hours of manual work. That dynamic has changed completely. Media platforms are now rolling out tools that handle quality controls and generate creative variations much more quickly. This shift allows UA managers to stop focusing on repetitive spreadsheets and start focusing on high-level strategy, creative direction, and market expansion. Automation is no longer a luxury but a baseline for staying competitive, where rapid iteration is becoming imperative.

AI and Gen AI transform mobile marketing

Artificial Intelligence (AI) is no longer just a buzzword; it is the engine of mobile growth. We are seeing AI power the entire lifecycle of an advert, from drafting the initial creative concept to predicting which users will churn. Generative AI tools like OpenAI and Gemini have become standard for advertisers and agencies, allowing them to produce high-quality assets at a speed that was previously impossible. This reduces the risk of creative fatigue and ensures that campaigns remain fresh without blowing up the budget.

Performance and ROAS take centre stage

Advertisers want predictable results. In 2026, Return on Ad Spend (ROAS) is the main driver for budget decisions. The focus has shifted from “how many installs can we get?” to “how efficient and profitable is this channel?” Advertisers are becoming ruthless about cutting sources that deliver volume without value. Quality and efficiency now far outweigh raw channel volume, forcing every partner in the ecosystem to prove their worth in hard revenue numbers.

Partner consolidation accelerates

Managing dozens of different ad networks is inefficient and resource-heavy. As a result, brands are working with fewer, more effective partners. Instead of spreading budgets thin across twenty different sources, advertisers are concentrating spend on top performers who can deliver scale. This consolidation means that new traffic sources face a higher barrier to entry; they must prove they can add value quickly, or they risk being cut from the media plan entirely.

Conversational search and AEO rise

The way users search for apps and answers is fundamentally changing. Instead of typing keywords into a search bar, users are having natural conversations with AI assistants. This behavior gives rise to Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) as new pillars alongside traditional ASO and SEO. Brands must adapt their content to be “read” and recommended by AI engines, ensuring they are the “answer” provided when a user asks for a recommendation.

Industry spotlight: Samsung

Domingo Mendez (Mobile Ecosystem Partnerships at Samsung) highlights the growing role of AI and personalization.

On the continued impact of AI in 2026:

“AI will revolutionize mobile advertising by enabling predictive analytics, dynamic ad optimization, and real-time decision-making. Advertisers will use AI to analyze vast datasets, predict user behavior, and deliver highly targeted ads. Additionally, AI-powered creative tools will streamline ad production, allowing for faster experimentation and iteration.”

On new behaviors by mobile users in 2026:

“Mobile users are increasingly seeking seamless, multidevice experiences and value-driven interactions. There’s a growing demand for privacy ‘conscious’ apps and personalized content.”

To visualize your potential reach across key OEMs like Samsung and strategically plan your global campaigns, you can access the AVOW User Reach Calculator tool for free.

OEM expansion and on-device advertising

This trend is accelerating rapidly within the hardware ecosystem. Phone makers (OEMs) are introducing new native ad placements directly on the device, integrating promotions into the launcher, the -1 screen, and system apps. Soon, on-device AI assistants will even serve ads based on user intent. For advertisers, this represents a massive opportunity to reach users before they even visit an app store, engaging them the moment they unlock their screens.

A shift in publisher inventory sales

We are seeing a shift in which major global publishers are selling their inventory directly to buyers through trusted partners rather than open third-party exchanges, consolidating their premium inventory via these trusted partners. This move increases transparency in the ecosystem and allows for higher CPMs for the publishers. For advertisers, it means fewer intermediaries, less budget wastage, and a clearer view of where their ads are actually running.

Creative production at scale

Speed is everything in 2026. Creative iteration is becoming faster and more data-driven than ever before. Brands are now generating higher volumes of assets with smaller teams, using performance data to make real-time changes to colors, copy, and CTAs. The days of running the same static banner for a month are over; creative strategy is now as dynamic as the bidding strategy itself.

Industry spotlight: Xiaomi 

Bono Wu, Head of Sales for the US, EU, & LATAM at Xiaomi, shares their vision for shortening conversion paths and deepening native experiences.

On new opportunities for mobile advertisers in 2026:

“I believe a major opportunity lies in precausal audience targeting and shortened conversion paths. At Xiaomi, we make it easier for app developers and advertisers to reach new users who are interested in similar apps, but haven’t found theirs yet. Furthermore, formats like the Mini-Card in our GetApps store enable automatic app downloads upon click, eliminating the redirect to an app marketplace. This significantly reduces user drop-off and improves conversion efficiency.”

On innovations they are excited about in 2026:

“We are excited about deepening native and system-level ad experiences. Our integrations within system apps like the Lock Screen, App Vault, Mi Browser, and GetApps allow ads to feel like a natural part of the user interface.”

To see how apps are successfully executing their mobile advertising with Xiaomi and other OEMs, you can explore AVOW’s success stories here.

Incrementality with the right channels

The era of chasing vanity metrics is over. Advertisers now demand proof that a channel is delivering net-new users who wouldn’t have been acquired through standard search or social campaigns. This shift is driving a surge of interest in alternative app stores, where the ability to measure unique reach is possible with OEM-specific metrics.

Human relationships remain a competitive advantage

Despite all the AI, automation, and algorithms, business is still about people. Strong collaboration and personal connections drive trust and lead to better performance outcomes. When issues arise or strategies need to pivot, having a direct relationship with a partner matters. Even as machines do the heavy lifting of optimization, the human element remains a critical competitive advantage in building long-term success.

Final thoughts

This tenth trend rings strongly with me personally. While technology drives our industry forward, I still believe that business requires a human touch. Real partnerships are built face-to-face. That is why at AVOW we make it a priority to meet our clients and partners in person, ensuring our strategies are aligned with their real-world goals.

However, strong relationships need powerful tools and partners to deliver results. To capture the efficiency and incrementality of the current market, advertisers must look where the user lives: on the device itself. Adding mobile OEMs to your marketing mix, with a specialized partner like AVOW, unlocks untapped audiences, bypassing app store noise to secure measurable success in the year ahead and beyond.

These mobile advertising trends are defining the 2026 roadmap. Don’t wait to capture the incremental growth available on the device itself. Schedule a session with the AVOW team today to start planning your 2026 OEM advertising strategy. Ready to align your strategy with the future of mobile?

The post 10 Key mobile advertising trends for 2026 success appeared first on Business of Apps.

Connected TV has been part of the advertising conversation for more than a decade, but the industry is entering a very different chapter. For years, CTV was seen as a premium reach tool: great for awareness, excellent for storytelling, but disconnected from the precision and attribution that performance marketers depend on. Most advertisers treated it as a CPM-based branding line item: something adjacent to performance, but not part of it.

That has changed dramatically.

Today, CTV is no longer an unmeasurable awareness channel. It’s becoming a performance-compatible medium with real attribution, real optimization levers, and real synergy with mobile. And in many cases, the smartest programmatic partners running mobile campaigns are now also running CTV, giving advertisers unified control over two channels that increasingly belong together.

Across campaigns and categories, we’re seeing a clear pattern: when CTV and mobile are planned jointly, brands unlock reach, frequency control, and measurable lift that neither channel can achieve alone. The challenge for marketers is learning how to balance both effectively.

CTV used to be CPM-only branding, but that era is ending

For a long time, CTV carried a major limitation: it couldn’t prove performance.
Marketers could measure impressions and general reach, but they couldn’t connect those impressions to real downstream outcomes. Install? Purchase? Subscription? Reactivation? None of it was trackable at the device level in a reliable way.

Because of that, performance teams viewed CTV as a “nice to have,” not a revenue-driving channel. Many advertisers explicitly passed on CTV because it didn’t meet their core requirement: measurability.

In many ways, CTV in 2017 was where mobile display was in 2010: high potential, limited attribution.

But today, we’re looking at a completely different landscape.

The evolution of CTV into a performance-ready channel

The shift began when data availability expanded across the CTV ecosystem. Smart TV manufacturers, streaming platforms, cable providers, and OEMs started enabling more deterministic identifiers and household-level signals. Combined with cross-device graphs, this made it possible to connect:

A CTV ad exposure → to a mobile device → to a conversion.

If the model sounds familiar, it’s because major retailers have used this type of cross-device matching for years. What used to be a proprietary capability is now accessible to a wide range of advertisers through the right programmatic partners.

This single development changed the trajectory of CTV.
It moved the channel from “trust us, it works” to “here are the users who saw your CTV ad and converted on mobile within the last 30 minutes.”

And that opened the floodgates for performance budgets.

Why programmatic partners who run mobile are now activating CTV

Mobile-first performance partners have a structural advantage when it comes to CTV:

• They already specialize in measurable, data-driven optimization
• They operate with mobile IDs, probabilistic models, and attribution logic daily
• They understand user funnels, conversion windows, and incrementality testing
• They are built on platforms designed for precision and scale

So when CTV inventory expanded and became more addressable, mobile programmatic partners naturally became CTV partners as well. This created a major benefit for advertisers:
One partner, one data layer, one optimization logic across two channels.

In the network space, this works seamlessly. In OEM environments, it’s straightforward to buy.
And even with mid-tier or smaller cable providers, the right programmatic stack can unify supply and reporting. For marketers, the takeaway is simple: If a partner can execute high-performing mobile campaigns, they can likely execute high-performing CTV campaigns and integrate both into one strategy.

The surge in CTV inventory across networks and platforms

Another factor accelerating CTV’s performance adoption is the sheer expansion of supply. What used to be limited to a few premium streaming apps is now a wide, diverse inventory landscape, including:

• Smart TV manufacturers (OEMs)
• Major streaming services and FAST channels
• Large and mid-sized cable providers with digital supply
• Gaming consoles
• Set-top boxes and home entertainment devices
• Niche and emerging streaming apps with loyal audiences

This diversity matters because performance campaigns need both precision and volume. Premium inventory brings quality, while mid-tier supply brings scale. Today, advertisers can reach households across income tiers, content preferences, and device types, all in one programmatic ecosystem.

Data is enabling CTV attribution at the performance level

What truly propelled CTV into the performance stack is data, specifically, cross-device attribution.

Performance advertisers can now answer questions that were previously impossible:

• Did this CTV impression lead to a mobile app install?
• Did it increase the likelihood of purchase?
• Did it reactivate a previously dormant user?
• Is CTV driving incremental conversions or just assisting mobile?

Attribution blends several data types:

• Deterministic identifiers (OEM IDs, logins, authenticated devices)
• Household-level identifiers
• Device graphs mapping CTVs to mobile phones
• Probabilistic exposure modeling
• Time-based impression-to-conversion windows

For performance marketers, this means CTV impressions can be connected to real revenue events on mobile, making the channel actionable, optimizable, and accountable.

Best practices for performance-focused CTV

As CTV becomes part of the performance toolkit, advertisers need a playbook that maximizes impact without inflating costs. Here are the best practices we consistently see delivering measurable lift.

Use creative designed for trackability

Performance on CTV improves when the ad invites action.
Some effective approaches include:

• Dynamic QR codes
• On-screen CTAs that match mobile messages
• Creative designed for quick recall when users switch screens
• Sequential storytelling (CTV → mobile)

Even when users don’t directly scan QR codes, they strengthen attribution by linking CTV exposure to device-level behavior.

Lean on modern attribution tools

Attribution is what makes CTV performance-compatible.
Teams should combine:

• Deterministic identifiers
• Probabilistic modeling
• Cross-device graphs
• Brand lift + incrementality tests
• Geo-testing or holdout regions

This ensures conversions are correctly assigned and CTV impact is not inflated or undercounted.

Sequence CTV with mobile

CTV shines brightest when paired with mobile programmatic.
We’ve seen strong results with strategies like:

• CTV for broad household reach, followed by
• Mobile retargeting to drive action
• Mobile user behavior informing CTV audience segments
• Consistent creative messaging across screens

This creates a full-funnel flow: awareness → intent → action.

Buy smart across inventory tiers

Not all CTV supply performs equally.
A balanced mix is essential:

• Premium streaming apps for high engagement
• OEM placements for deterministic IDs
• Mid-tier cable and FAST channels for efficient scale

The goal is to blend quality with reach while maintaining control over cost and frequency.

Maintain frequency discipline

Without frequency management, CTV CPMs climb quickly and cannibalization risks increase.

Performance teams should set:

• Frequency caps by household
• Incrementality tests to monitor saturation
• Unified reporting with mobile to avoid overlap

This is where mobile + CTV consolidation under one programmatic partner becomes extremely valuable.

How to balance programmatic mobile and CTV without cannibalizing either

Balancing these channels requires treating them as complementary, not competitive.

Here’s what works:

• Define roles: use CTV for creating intent and mobile for capturing it
• Use distinct KPIs: CTV for uplift and exposure, mobile for final conversion
• Limit overlap: avoid showing heavy CTV impressions to existing high-frequency mobile users
• Test incrementality: ensure CTV is truly additive, not replacing mobile performance
• Use combined insights: let mobile data refine CTV audiences and vice versa

In the strongest campaigns, CTV expands the funnel and mobile converts it. When balanced correctly, mobile performance improves, not declines, with the addition of CTV.

If you’re exploring how to blend CTV with your programmatic mobile strategy, let’s talk.

The post Connected TV’s performance moment and how to balance it with programmatic mobile appeared first on Business of Apps.

For years, user acquisition strategies have been built around channels. Marketers optimized mobile campaigns in isolation, treated CTV as branding, and measured success within neatly defined silos. But user behavior has moved on — and the industry is now being forced to catch up.

In an App Talks video interview recorded during Business of Apps Berlin 2025, Maria Dimitropoulou, Head of Partnerships at SpinX, explains why performance marketing is no longer about optimizing individual channels, but about understanding and orchestrating the entire user journey across screens. 

Drawing on her experience building SpinX and working with app marketers globally, Maria outlines how fragmented measurement, channel-first thinking, and weak internal strategy continue to hold teams back — and why cross-platform “journey architecture” is quickly becoming a competitive advantage.

From programmatic black boxes to unified visibility

The idea for SpinX was born out of frustration. While working closely with user acquisition teams across regions, Maria noticed a recurring problem: programmatic marketing delivered results, but marketers rarely understood why.

Campaigns felt opaque. Teams didn’t know which creatives, formats, or placements actually drove performance — only that money was being spent. This lack of transparency made it difficult to scale with confidence.

SpinX was built to address that gap by creating a unified ecosystem: owned apps, owned inventory, an ad exchange, and full visibility into how users move from impression to install to subscription. The goal was simple — replace guesswork with clarity.

Users don’t live on one screen anymore

A defining insight behind SpinX’s approach is that users no longer follow linear, single-device journeys.

Maria describes a familiar scenario: a user sees an ad on a streaming platform, searches for the brand on their phone, compares options on the web later that evening, and installs the app the next day. Each interaction happens on a different screen, in a different mindset, at a different moment.

Treating these touchpoints as separate channels misses the bigger picture. Performance marketing, Maria argues, should follow behavior, not platforms. Success comes from understanding how screens connect — not optimizing them in isolation.

Measurement has finally caught up

For a long time, cross-screen strategies were constrained by measurement limitations. Attribution across CTV, web, and mobile was unreliable, which pushed marketers back toward mobile-only campaigns.

That has now changed.

Thanks to improvements in mobile measurement partner integrations, marketers can track performance from upper-funnel impressions all the way to in-app purchases and subscriptions. This makes it possible to evaluate CTV and cross-platform campaigns on performance metrics — not just awareness. 

As Maria puts it, CTV is no longer “just branding.” With the right strategy and measurement in place, it has become a legitimate performance driver — one piece of a broader cross-platform ecosystem.

Strategy before scale

One of the most common issues Maria encounters is not tooling — it’s preparation.

Many teams know their high-level targets but lack a clearly defined internal strategy. They don’t fully understand their conversion flow, their drop-off points, or the percentage of users they expect to move from install to registration to purchase.

Without this foundation, scaling acquisition simply amplifies inefficiency.

SpinX doesn’t replace UA teams or act as an agency. Instead, it supports teams that already have a strategy by providing the tools and inventory to execute cross-platform campaigns effectively. But that only works when KPIs, attribution, and expectations are clearly defined upfront.

Cross-platform performance beats mobile-only scale

A key pattern emerging from SpinX’s data is that unified, cross-screen campaigns outperform mobile-only strategies on long-term metrics.

By reaching users in different moments — relaxed on a TV, researching on web, active on mobile — marketers create a more natural and less aggressive acquisition experience. According to Maria, these campaigns tend to deliver better retention, stronger LTV, and healthier ROAS than static, single-channel approaches.

The goal isn’t to be everywhere for the sake of it. It’s to match creative, format, and message to the user’s mindset on each screen.

Creative and context still matter

Not all screens — or creatives — perform the same way.

Maria stresses that effective cross-platform marketing requires experimentation. Playables may work well for gaming apps, while finance or wellness apps require very different creative approaches. Each screen captures a different mood, and creative formats must reflect that.

SpinX runs creative testing across screens to identify what works where — but the real value comes from combining those insights into a coherent journey, rather than treating them as isolated A/B tests.

The industry’s blind spot: chasing volume without quality

Perhaps the most striking observation Maria shares is how often teams prioritize volume over clarity.

Many UA teams focus on installs without a clear understanding of quality, incrementality, or long-term value. They continue to pour spend into increasingly expensive search and social campaigns without asking whether new channels are actually adding incremental growth.

Adding channels only makes sense if they improve outcomes — whether that’s lower CPAs, stronger retention, or better LTV. Otherwise, more spend simply means more inefficiency.

Laying foundations that scale

Maria closes the conversation with a lesson drawn from both business and personal life: foundations matter.

In a volatile environment — whether in marketing, startups, or life — sustainable growth depends on having a strategy that remains stable as conditions change. From there, everything else can scale.

For app marketers heading into 2026, that foundation is clear: understand the full user journey, define success before spending, and stop thinking in channels.

Watch the full interview below.

The post Cross-platform user journeys and the future of app growth appeared first on Business of Apps.

Driving users to an app store page is only half the battle. For many app teams, the real leakage happens after the click — when users land on a generic, poorly optimized product page that fails to reflect their intent, context, or expectations.

In a recent Business of Apps podcast episode, Vivian Dang, Senior Director of Accounts & Client Services at Gummicube, unpacked why app store conversion optimization remains one of the most overlooked growth levers in mobile marketing — and how teams can unlock quick, compounding wins by treating the App Store and Google Play as dynamic performance channels rather than static storefronts.

This article distills the key themes from the conversation, covering why aggregate conversion rates are misleading, how traffic source context should shape store experiences, and where app teams can find fast, practical improvements without overhauling their entire growth strategy.

The hidden problem: app store pages are treated as static

One of the first issues Vivian highlights is surprisingly simple: many app teams ignore their app store pages altogether.

In practice, this shows up in several ways:

• little to no A/B testing
• underused screenshots, videos, and metadata
• reliance on a single top-line conversion rate
• minimal strategic thinking about user intent

Too often, teams monitor one aggregated conversion metric in App Store Connect or Google Play Console and treat it as a proxy for performance. According to Vivian, this is one of the least effective ways to understand how an app store presence is actually performing.

The core question shouldn’t just be “How do we get more downloads?” — it should be “How do we get the right users to convert, and continue converting down the funnel?”

Why traffic source context matters more than most teams realize

A central theme of the conversation is that not all app store traffic behaves the same way.

Users arrive at app store pages from many different entry points:

• paid user acquisition channels like Apple Search Ads or social
•  websites and landing pages
• email and newsletter campaigns
• organic App Store and Google Play discovery

Each of these users arrives with a different mental model and expectation. A user coming from a playable ad expects to see gameplay mechanics reflected immediately in screenshots. A user searching organically expects relevance to their query. When that context is missing, bounce rates rise — and marketing spend is effectively wasted.

As Vivian puts it, if you’re not tailoring the app store experience to each funnel, you’re often paying to drive users to a page that pushes them straight to a competitor.

Custom pages: the most underused conversion lever

The good news is that both Apple and Google already provide the tools to fix this.

On iOS, Custom Product Pages (CPPs) allow teams to create multiple versions of their App Store page, each tailored to a specific traffic source or audience. On Google Play, the equivalent is Custom Store Listings.

These tools allow teams to customize:

• screenshots
• preview videos
• icons
• metadata and messaging

The result is a store experience that mirrors the user’s original motivation. A sports fan searching for baseball content can land on a page highlighting baseball features rather than generic functionality — dramatically increasing the likelihood of conversion.

Once implemented, these pages can be monitored independently, allowing teams to see which sources and messages are actually driving installs — and iterate as campaigns evolve. 

Using app events to re-engage and reactivate users

Beyond acquisition, Vivian also highlights the importance of in-app events (iOS) and promotional content (Google Play) as tools for engagement and reactivation.

These features allow developers to promote:

• new features
• limited-time challenges
• seasonal content
• special offers

Crucially, these events aren’t limited to new users. They can be surfaced to:

• existing users who’ve gone inactive
• users who previously uninstalled the app
• lapsed segments with high reactivation potential

While many teams run events for just a few days, Vivian recommends 7–14 day durations to allow store algorithms enough time to index and properly distribute the content. 

The setup effort is relatively low, but the visibility gains — especially during high-interest moments — can be significant.

A/B testing: frequent, structured, and realistic

Another recurring theme in the discussion is how A/B testing is consistently undervalued in app store optimization.

Vivian emphasizes that effective testing needs structure:

• clear hypotheses
• defined success metrics
• consistent testing cadence

Rather than testing sporadically, she recommends running experiments every 2–3 weeks, depending on traffic volume. The goal isn’t to find a “home run” every time — it’s to accumulate learnings.

Most tests won’t produce dramatic winners, and that’s expected. But each test helps teams understand what resonates with users, which messages fail, and which creative elements influence conversion behavior.

Google Play offers particularly broad testing capabilities — allowing nearly every store asset (except the title) to be tested. iOS is more limited, but icons, screenshots, and preview videos are still fair game.

If users can see it, Vivian argues, it should be tested.

Why A/B testing often falls apart

So why don’t more teams do this consistently?

According to Vivian, the blockers are usually a mix of:

• limited resources
• the day-to-day tedium of monitoring test performance
• rapidly shifting trends that make results harder to interpret

A/B testing requires sustained attention. Conversion trends can swing daily, and without dedicated ownership, insights are easily lost. But skipping testing altogether guarantees stagnation — especially in competitive categories.

Measurement: breaking free from aggregate metrics

Measurement brings the conversation full circle.

Vivian strongly cautions against relying on aggregate conversion rates. Instead, performance should be analyzed per channel and per page:

• did a custom page improve Apple Search Ads performance
•  did it lift organic conversion?
• which traffic source is dragging down overall averages?

On iOS, she recommends focusing on install-to-impression metrics rather than product-page-view-to-install, as this better reflects top-of-funnel performance. On Google Play, analysis should be split across the three traffic categories the platform provides.

Only by breaking performance down this way can teams understand what’s truly driving — or blocking — growth.

Quick wins: where teams should start

For teams feeling overwhelmed, Vivian’s advice is pragmatic.

The fastest path to impact isn’t launching more campaigns — it’s auditing existing traffic and setting up custom pages to support it. Many teams already have valuable traffic sources but fail to prepare store experiences that convert that demand into installs.

By taking stock of each channel and aligning the app store experience accordingly, teams can improve conversion rates without increasing spend — creating a more sustainable foundation for long-term growth.

Final takeaway

Improving app store conversion rates isn’t about chasing one metric or one feature. It’s about recognizing that the app store is an active part of the acquisition funnel — not a passive endpoint.

By tailoring store pages to traffic sources, running structured A/B tests, leveraging in-app events, and measuring performance at a granular level, app teams can turn existing demand into higher-quality installs and stronger downstream performance.

As Vivian Dang makes clear, the biggest opportunity isn’t driving more users to the store — it’s making sure the users you already have actually convert.

You can watch the full interview below.

The post Fixing app store funnel drop-offs appeared first on Business of Apps.

SocialPeta has partnered with Reforged Labs to release the report “2025 Global Mobile Game Marketing Insights & Creative Breakdown”, offering an in-depth look into the latest creative and marketing trends across the mobile gaming industry.

For mobile game professionals asking questions like “How many creatives are enough?” or “Which strategies work best in different regions?”, this report provides data-backed answers. By analyzing over 1.7 billion ad creatives and tracking millions of daily updates, it uncovers the evolving strategies that top advertisers use to capture user attention and maximize ROI.

Global Creative Landscape: Volume and Speed Are Key

The 2025 mobile gaming market is more competitive than ever. On average, advertisers globally deploy 123 creatives per month, up 19.4% year-on-year, with peaks reaching 132. More importantly, 58% of monthly creatives are new, reflecting the fast-changing preferences of users. High-frequency iteration is no longer optional—developers who can quickly optimize and refresh creatives will gain a significant edge.

Regional Deployment Strategies

Creative strategies differ sharply across regions, highlighting the importance of localized marketing:

• North America & HK/TW: The highest creative volume at 117 per month. Success requires broad coverage and attention to multiple audience segments to avoid fatigue.
• Europe: Fastest iteration at 46.4% new creatives monthly. Users demand novelty, so rapid iteration and innovative concepts are essential.
• Other regions: Oceania and Southeast Asia rank third and fourth for creative volume, while Africa has slower updates, suggesting targeted, lower-frequency strategies for emerging markets.
  

Category Insights

Creative deployment also varies by game genre:

• Strategy games: Lead with 325 creatives per month, reflecting the need to showcase gameplay depth, world-building, and social systems.
• RPGs and board games: Moderate creative needs, relying on frequent updates to maintain user engagement.
• Casual or sports games: Lower creative iteration is sufficient, given simpler gameplay and stable user engagement.
  

Platforms and Creative Types

• Android dominates (77.6% of creatives), with iOS favored in heavy games and mature markets like HK/TW and Europe.
• Video creatives lead the market, particularly for puzzle and fast-paced games. High-impact visuals and short demonstrations capture attention effectively.

The Life Cycle of a Hook: Balancing Experimentation and Exploitation

One of the report’s most valuable insights is how top advertisers manage creative hooks—balancing experimentation (discovering new hooks) with exploitation (leveraging proven hooks).

Every hook follows a predictable life cycle:

1. Discovery: New hooks deliver exceptional results for early adopters.
2. Fast-Follow Duplication: Competitors copy the hook, performance normalizes but remains effective.
3. Oversaturation: Hook effectiveness declines, requiring iteration or new concepts.

Examples from leading advertisers:

• Royal Match: Refreshes the classic “King in Peril” hook with dynamic danger and immersive sound.
• MONOPOLY GO!: Moves beyond board game visuals to universally appealing mini-games.
• Kingshot: Adapts puzzle game hooks to other genres with strong results.

Understanding and managing hook lifecycles allows advertisers to maintain a competitive edge by continually innovating while maximizing proven strategies.

The 28-page report provides detailed insights on global creative trends, formulas for top-performing ads, and hook analysis—essential for any developer looking to break creative homogenization and maximize ROI.

Download the full report here.

The post Latest creative trends report: ad volume surges 20%. Complete insights on regional, genre, and platform strategies appeared first on Business of Apps.

On December 4th, Business of Apps ran a webinar in partnership with Verve that featured speakers from Verve, Dataseat, and Kochava. Moderated by Nicole Weiss, the session focused on how Connected TV (CTV) is becoming a viable and measurable channel for user acquisition.

The discussion brought together the perspectives of a DSP, an SSP, and an MMP to give marketers a clearer understanding of what is now possible when inking CTV exposure to mobile installs.

Speakers included:

• Nikunj Sureka, Sr Director Product Management, CTV at Verve
• Alexei Moltchan, VP of Product Management at Dataseat (Now part of Verve)
• Julia Kan, Managing Director, Advertising Solutions at Kochava

Host:

• Nicole Weiss, Founder at Brass Finch

CTV is shifting from a branding medium to a measurable performance channel

The speakers agreed that CTV has undergone meaningful changes in the past few years. Increased ad supported inventory has created more liquidity and publishers have become more familiar with programmatic monetization. This shift allows buyers to transact at an impression level instead of purchasing large upfront packages.

The result is a channel that is more accessible for performance-driven teams. Alexei Moltchan noted that measurement and optimization were previously constrained by the lack of granular signals, but the ecosystem has matured enough for DSPs to model outcomes with a high degree of confidence.

Julia Kan also explained that this shift accelerated after the Covid pandemic when streaming viewership spiked and advertisers demanded more accountability from CTV platforms.

Making CTV work for UA: From exposure to install

Source: Business of Apps via YouTube

Measurement has become more practical due to advances in household-level attribution

A central point in the discussion was the progress made in connecting CTV impressions to mobile installs. CTV inventory is not clickable, so all attribution flows through probabilistic matching using the household IP.

Julia Kan pointed out that IP based methods are more reliable for CTV because televisions remain stationary while mobile devices can move across networks. To address multi-day conversion delays and fragmented identifiers, Kochava has built a household graph known as DeviceLink. It maps devices within the same home to allow attribution to succeed even when the CTV impression and the mobile install occur on different networks. This gives marketers a more accurate view of CTV’s contribution to both direct installs and downstream channel uplift in areas such as search and social.

Contextual and environmental signals are becoming the foundation of effective CTV buying

The panel emphasized the importance of contextual intelligence as a performance lever. Since CTV is typically a one-to-many environment, identifying the individual viewer is challenging.

DSPs must rely on signals such as content type, viewing format, time of day, device characteristics and household patterns to estimate the likelihood of an install. Alexei Moltchan explained how Dataseat’s models use a wide set of environmental attributes to interpret user intent.

Nikunj Sureka, on the other hand, expanded on the types of signals actually available in the bidstream. These include IP address, user agent, device identifiers when permitted, and content level metadata. The presence or absence of these signals affects how accurately a DSP can optimize and how clearly an MMP can attribute.

Final thoughts

The post Making CTV work for UA: From exposure to install appeared first on Business of Apps.

In our latest episode of App Talks, David Murphy spoke with David Quinn, VP of Strategy and Partnerships at Gummicube, to discuss the role of paid campaigns in boosting ASO performance.

[Q] A lot of people have different ideas of what ASO actually is. Maybe we could start with your take on it, your definition of it.

[A] When we think about ASO at Gummicube, it’s different from how most people see it. A lot of people think it’s just keyword updates, A/B tests, or even paid campaigns to boost downloads, but none of those are ASO on their own.

ASO is really about top-of-funnel acquisition and conversion in the store. You need keywords to get discovered, A/B testing to improve conversion, and paid campaigns to support organic performance. It’s not one thing in isolation; it’s combining all of them and learning from each to make the whole strategy work.

Source: Business of Apps vis YouTube

[Q] What are you tips for structuring Apple Ads in a way that can best support organic?

[A] We see a wide range of Apple Ads setups when we start working with new partners. Sometimes it’s just one campaign with everything thrown into it, which is better than nothing, but not ideal.

The basic structure should include a discovery campaign using search match and broad match, a competitor group, a brand defense group, and feature-based keywords.

From there, depending on your budget and app, you may want multiple feature or competitor groups to get more clarity on performance. That structure lets you see, at a high level, how different areas perform.

The next level would be breaking features out even further, like separate ad groups. Apple’s Custom Product Pages let you match creatives to intent, which improves conversion. The biggest opportunity is building enough structure and differentiation to measure performance clearly and optimize creatives and keywords more effectively.

[Q] What about cannibalisation? Because that’s a subject that always comes up when you talk about combining paid and organic. What are your thoughts on that?

[A] Cannibalisation depends on how much paid you’ve run in the past. If you’re starting fresh, it’s easy to compare before and after by reconciling Apple Ads data with what you see in App Store Connect.

For brand campaigns, you can see how much organic drops once paid is turned on and estimate from there. If you already have campaigns live, you can test by turning them off and on, or even use another market as a proxy.

But the best indicator is share of voice reporting in Apple Ads. If you’re consistently at 90–100% on your brand terms, you’re likely cannibalising and can safely pull back. The 80–90% range is usually a safe zone, and even 70% can work depending on competition.

Share of voice also matters for feature keywords. If you’re only getting 10%, you’re not winning enough taps to move organic ranking. To use Apple Ads to boost ASO, you need strong share of voice, tap-through rates, and conversion to truly compete.

[Q] Can you give us some dos and don’ts for using Apple Search Ads?

This episode of App Talks was created by Business of Apps and David Murphy for Gummicube. Find out more about the company on their website.

Watch the full video, embedded above, to discover all of David’s insights. You can also watch all episodes of App Talks here.

David’s responses have been shortened and slightly edited for clarity.

The post How paid campaigns boost your ASO appeared first on Business of Apps.

Gone are the days of app growth at any cost. Gone are the days of unlimited budgets. Gone are the days when simply getting more users was enough to succeed. That was all yesterday.

In our latest episode of App Talks, David Murphy sat — in the unparalleled comforts of virtual interview room — with Eoin Hallahan, Chief Revenue Officer at SplitMetrics, to discuss the world after yesterday.

The state of play today

“Conditions today are more volatile and unpredictable than ever, with trends shifting all the time,” began Eoin after David asked him to quickly sketch what it is like to be growing an app today.

Eoin continued. From changing privacy regulations and signal loss to channel and tool fragmentation as well as data silos, UA managers today face a veritable juggernaut of problems, a full course of obstacle they need to surmount to “correctly and efficiently connect the dots across paid and organic and ultimately drive performance.”

Furthermore, while AI has blown open the industry and created many new opportunities, it has also created confusion and uncertainty, according to Eoin. As he put it, “many tools simply are not designed to facilitate mobile app growth, and they give you generic responses that can distract you from what’s actually happening. Ultimately, they can be disconnected from real world marketing needs and workflows.”

Finally, UA teams often find themselves under a lot of pressure to deliver, to improve, to grow, with new requirements and expectations coming their way by the hour — a very sour cherry on a decidedly undelectable cake.

Solving app growth complexity with AI

Source: Business of Apps vis YouTube

Iris: SplitMetrics’s solution to UA team’s plight

To address the above problems and simplify the complexity of app growth in the AI era, SplitMetrics created Iris — the very first AI agent purpose-built for Apple Ads and ASO.

What is Iris?

As Eoin explained, “we would like everybody to think of Iris as a strategist in your Slack workspace. You can ask a question in whatever language you speak, and Iris will give you recommendations in seconds. How this differs from how people would have worked with insight tools in the past is that there is no longer a need to check six or ten different tools, or download reports, or wait for a weekly sync with your data team.

“Iris lives where you already work — in the majority of cases, Slack — and gives you instant clarity on your most important questions. This could be competitor-related. This could be market-related. This could be campaign-related. Iris separates what actually matters from the noise. She takes complex market data and turns it into structured, simple, and clear actions in seconds.”

What are the types of questions people ask Iris?

“For the most part, the types of questions fall into three main buckets. First and by far the most, everything about their competitors. People are competitor-obsessed, and this is a no brainer. It’s highly competitive industry, especially when we think of paid user acquisition channels. What are my competitors are doing in this market? What are my competitors doing overall? How much are their spending? Which keywords are they bidding on?

“The second bucket is all about growth opportunities. For example, where should I invest next? Which GEOs have untapped potential? How can I scale my campaigns more efficiently? And what are my competitors doing in different markets?

“Finally, I would say the third most common is about execution-level support, aka more hands-on, in-the-weeds questions. For example, what keywords should I use for my campaign in Germany? Or, can you help me rewrite this app store listing?”

What does Iris deliver for users?

“The first would be speed. AI slashes the time it takes to go from question to action. Instead of hours spent in dashboards or spreadsheets or meetings with your data and analytics and insights teams, our customers and our Iris users get answers in seconds and right inside Slack.

“The second would be results. Iris helps teams to increase return on ad spend without increasing spend.

“The third big thing is productivity. Manual work doesn’t scale. Iris automates all of this research. It flags issues and changes and trends in real time, and continuously adapts so that your team can focus on strategy and hopefully not spend too much time buried in spreadsheets or kicking through reports.”

Why does Iris perform so well?

“I would say what sets Iris apart is quite simple: it’s deep specialisation, trusted data, and over a decade of domain expertise. AI is only as good as the data and the context it’s built upon, and that’s where the most generic tools fall short. As I mentioned before, on a daily basis, we’re comparing the outputs and the quality of answers from Iris to all of the standard LLMs. These LLMs were not trained on mobile growth signals. They don’t understand the nuances of app marketing or of Apple Ads as a channel.

“Iris is completely different. It’s purpose-built for mobile marketers and trained specifically on SplitMetrics data, which includes over 40 million keywords and 2 million apps in almost 100 storefronts. Iris will be useless if you ask her to help you to plan your holiday or give you a recipe for Russian salad Iris is, however, bloody good when it comes to giving you clear directions on what you should be doing to scale and grow your your app or apps in the stores and paid channels, especially Apple Ads.”

Why is Iris a game changer?

“There’s a few reasons for this. The first is, I would argue, Iris doesn’t just change what you do, it changes how you work. For well over a decade now, we have seen mobile marketers who have been forced into reactive workflows. They’ve been juggling disconnected dashboards. They’ve been analсzing fragmented signals and running manual competitor research.

“Iris changes all of this. It flips the model entirely. Instead of dashboards, you get conversations. Instead of lagging indicators, you get proactive strategy and reports and prompts. Instead of guesswork, you get clarity instantly. And not just clarity. It’s clarity built on millions of keywords, ten years of app marketing-specific, complex data. I would argue this isn’t just automation; it’s a new operating system for mobile growth.

“Finally, Iris was built to work with you. It speaks your language. It understands your category. It adapts to your KPIs and delivers strategy-level recommendations.”

This episode of App Talks was created by Business of Apps and David Murphy for SplitMetrics. Find out more about the company on their website.

Watch the full video, embedded above, to learn more about Iris. You can also watch all episodes of App Talks here.

Eoin’s responses have been shortened and slightly edited for clarity.

The post Solving app growth complexity with AI appeared first on Business of Apps.

In 2025, Buy-Now-Pay-Later (BNPL) has crossed over from an emerging payment trend to what can be reasonably considered core eCommerce infrastructure. Driven by exceptionally quick global adoption, BNPL gross merchandise volume is expected to exceed $560b this year, according to a 2025 report by Research and Markets. At this scale, it’s safe to call BNPL an expectation embedded in consumer purchasing behavior.

Even 5 years ago, BNPL was a trendy nice-to-have feature you’d rarely see in online stores. Now it is treated by many customers as a standard part of modern online shopping. For eCommerce merchants, meeting this expectation has become as necessary as offering mobile-friendly checkout or order tracking.

The metric-boosting power of BNPL

Global adoption wouldn’t be climbing so quickly if the evidence of BNPL’s impact on performance metrics wasn’t unmistakable. The most notable improvements merchants report after integrating BNPL into their checkout flows are in CR, average order value, and repeat purchasing.

Conversion rates are the clearest indicator. Studies across multiple markets show that merchants offering BNPL options can achieve a 20-33% CR increase after implementation. It’s easy to trace the psychology behind this boost: when shoppers can distribute payments over time, hesitation decreases and cart abandonment falls.

Average order value experiences a similar lift for similar reasons. Merchants report increases of 30-40% in basket size with BNPL enabled, as shoppers feel more comfortable purchasing premium products or adding complementary items to their order.

The long-term customer impact is even more compelling. BNPL users are more likely to return to merchants who offer installment options, with repeat purchase growth in the 15-27% range after enabling BNPL. As customer acquisition costs continue to rise, anything that meaningfully improves retention becomes a non-negotiable feature.

Expedited settlement offered by BNPL platforms is another direct benefit for merchants. Unlike traditional financing options, BNPL providers remit funds to merchants almost instantly while carrying the repayment risk. That allows retailers to grow revenue without tying up cash flow or taking on consumer credit exposure.

Opting out is not an option

We are now at the point where BNPL is becoming a competitive necessity. The 2025 Merchant Risk Council report shows that 35% of all polled merchants offer a BNPL option, compared to 23% for cash on delivery and 10% for crypto. In verticals like fashion, beauty, home goods, and consumer electronics, BNPL has become so pervasive that merchants without it look positively Web 1.0.

Installment flexibility without interest rates is too attractive a proposition to roll back. It’s here, and if a consumer doesn’t find it available in an online store, they are increasingly likely to abandon the cart and buy elsewhere. This is happening even as regulatory scrutiny increases across the globe. Providers respond by tightening underwriting models and enhancing consumer transparency. Despite these pressures, BNPL adoption continues to rise because the demand from both consumers and merchants doesn’t show signs of slowing, and the economic benefits far outweigh the compliance challenges.

eCommerce affiliate’s BNPL playbook

Here is the first play in the BNPL playbook for eCommerce affiliates: prioritize offers promoting stores with BNPL functionality because they have better CR and AOV. There is no second play. The benefits BNPL brings to merchants convey directly to affiliates commissions, except maybe for repeat purchases, which depends on the offer’s commission structure.

When BNPL boosts conversion rates and AOV for merchants, affiliates benefit from it automatically. A user who could have abandoned cart at a $120 price point may complete the order when a “4 payments of $30” option is on the table. A customer shopping for a single item may be tempted to add extras when the upfront cost is reduced. For affiliates running revenue share offers, this means higher payouts without changing the campaign structure or ad spend.

The best part: BNPL does not interfere with affiliate tracking. BNPL-funded purchases trigger the same confirmation page events and server-side postbacks involved in normal affiliate attribution. That means affiliates get a free ROI boost without worrying about broken tracking, alternative payment flows, or special integration requirements. BNPL supercharges the merchant’s offer, and affiliates are downstream beneficiaries.

The window of opportunity is still here

eCommerce today is shaped and governed by the same truth as traditional retail: customer expectations are setting the rules. In 2025, consumers want immediacy, flexibility, clarity, and financial control. BNPL satisfies all of these desires in a way that aligns organically with how people already shop. When BNPL increases conversion rates, multiplies order value, improves retention, boosts merchant liquidity, and strengthens the value of affiliate-driven traffic, it becomes a vital strategic advantage.

That is why BNPL is no longer optional for merchants in 2025. And for affiliates who understand this shift, it’s an opportunity worth leaning into while it’s still at 35% adoption worldwide. Don’t doubt for a second this number is only going to climb in the coming years.

If you’re looking to gain more insights and start earning with ClickDealer, register here.

The post Why BNPL is no longer optional for merchants appeared first on Business of Apps.

For years, connected TV (CTV) has been treated as the domain of brand advertisers — a big-screen storytelling channel, strong in reach and emotional impact, but rarely considered a performance engine. That perception is now rapidly shifting.

In this App Talk video interview, Kaitlin Stebbins, Performance Media Lead at Samsung Ads, breaks down why CTV has become one of the most underrated performance channels in mobile marketing — and why 2026 will be the year mobile teams meaningfully shift budgets beyond search and social.

This article distills the key insights from the video interview, covering how CTV drives measurable in-app conversions, what makes OEM-powered data uniquely strong for performance, and why early adopters are gaining a competitive edge as acquisition on traditional platforms becomes more expensive and less predictable.

From brand awareness to bottom-funnel outcomes

Most mobile marketers still associate CTV with brand awareness — a misconception Kaitlin encounters often. But she has spent six years at Samsung Ads working directly with performance-focused advertisers, and the reality is clear: CTV has been driving measurable performance for years.

The difference now is sophistication.

Samsung Ads has spent the past two to three years integrating deeply with mobile measurement partners (MMPs), enabling precision targeting and performance optimization specifically for in-app marketers. These integrations unlock the ability to attribute mobile installs, purchases, and in-app actions directly to TV exposure — a key shift in proving CTV’s performance value.

Even before the technical enhancements, user behavior made the case for CTV. Around 77% of people use their phones while watching TV, creating a natural bridge between awareness and action. “It’s not hard to believe that having a big, beautiful ad on the TV with a clear call to action drives action on mobile,” Kaitlin notes. 

With better measurement now in place, the perception of CTV as “just brand” is increasingly outdated.

Why mobile marketers must diversify beyond search and social

Traditional UA channels still dominate mobile budgets — particularly Meta and Google — but relying solely on these environments means ignoring massive, high-value audiences that CTV reaches and social platforms don’t.

From the App Talk conversation, three themes stood out as reasons to diversify:

1. CTV reaches audiences the dominant platforms consistently miss

Samsung data shows:

• 88% of U.S. households own a smart TV, a reach unmatched by social platforms
• Facebook reaches ~54% of the market; Snapchat/TikTok skew young
•  Samsung’s median viewer is aged 35–49, has a household income of $90K+, and often has children present
  — a profile far more aligned with purchase power than the youngest social cohorts

These are precisely the audiences many app categories struggle to reach cost-effectively on social.

2. CTV delivers diverse, action-oriented ad formats

CTV today includes interactive placements, native units, and vertical video — far beyond the traditional 15- or 30-second spots. These formats are built specifically to drive immediate action on mobile.

3. The industry’s macro trend: installs decreasing, revenue increasing

Kaitlin references an insight she encountered at App Promotion Summit:
installs may be declining industry-wide, but revenue per user is going up.

That means:

Each install carries more financial responsibility.

Marketers need stronger predictive targeting — and OEM-level data offers advantages social platforms can’t replicate.

CTV as a competitive edge: scale, data, and predictive signals

As the largest smart TV manufacturer globally for 19 consecutive years, Samsung Ads sits on some of the richest household-level datasets in the industry.

And crucially, this data isn’t limited to media consumption.

Samsung sees that:

• 80% of smart TV usage involves activities beyond entertainment
• Among users aged 25–34, this threshold is even higher
• Shopping and other non-video activities offer predictive signals for mobile behavior

This gives Samsung Ads the ability to:

• identify which households are likely to install specific app categories
•  optimize campaigns toward users with higher revenue potential
• reach audiences that simply don’t surface in social lookalike models

In an environment where Meta and Google auctions grow more expensive each quarter, this type of predictive, cross-device understanding becomes a powerful competitive advantage.

CTV powering mobile conversions

Source: Business of Apps via YouTube

How Samsung Ads measures CTV-driven mobile outcomes

In the App Talk interview, Kaitlin gives a clear explanation of attribution mechanics for CTV → mobile in-app actions.

It all hinges on the household IP address:

• TVs stay in the home
• they remain connected to Wi-Fi
• the IP is persistent and stable

This creates a durable bridge between CTV ad exposure and mobile device conversion, enabling MMPs to measure everything from installs to in-app purchases.

Samsung is also accelerating toward a combined TV + mobile + web measurement model, aiming for more holistic attribution in 2026.

Metrics that matter most to performance marketers — ROAS, CPA, D7 performance, retention, and purchase events — are all measurable and actively used by Samsung Ads clients today.

How much budget should mobile marketers allocate to CTV?

Mobile marketers are still early in their adoption curve, typically allocating 5–10% of budgets to CTV. But Kaitlin draws a compelling parallel to Samsung’s earlier “Rule of 40” guidance for linear advertisers.

Three years ago:
• linear advertisers were told to shift 40% of budgets to CTV
Today:
• many spend 60–80% on CTV

Her view is that mobile marketers are on the eve of a similar shift. Early adopters — those who test now, learn now, and scale now — are the teams who will retain the advantage as competition rises.

The 2026 takeaway: CTV is becoming essential for mobile performance

At the end of the App Talk interview, Kaitlin boils it down simply:

Lean heavily into CTV in 2026 — especially with your OEM partners.

With Samsung owning both the smart TV and the smartphone in many households, it can deliver:

• cross-device understanding
• predictive household-level signals
• premium, high-intent inventory
• full-funnel performance measurement

CTV is no longer a “branding-only” channel. It has matured into one of the most undervalued engines of mobile growth at a time when acquisition costs and privacy constraints make traditional channels less predictable.

In 2026, the biggest wins will go to marketers who diversify early — tapping into rich OEM data, unique household reach, and performance capabilities that finally connect the biggest screen in the home to the most important screen in the user’s hand.

The post CTV powering mobile conversion appeared first on Business of Apps.

Last year’s Thanksgiving to Cyber Monday weekend drew a record-breaking 197 million shoppers, proving the holiday spending rush is not slowing down. For affiliates and performance marketers, Q4 is the profit crucible. We have compiled the top 8 predictions and recommendations in this guide to help you focus on high-leverage activities in these critical weeks.

In 2025, the game changes. AI-driven search, new ad formats, and fiercer competition are rewriting the Black Friday playbook. Every tweak in your traffic, tracking, or creative execution can mean the difference between a good quarter and your best one yet. Let’s strategize and take action today.

Key takeaways

• AI is reshaping search behavior — focus on Generative Engine Optimization (GEO) to stay visible in ChatGPT and Google AI summaries.
• Fewer clicks, higher intent. Traffic may decline, but conversions will rise from AI-filtered users ready to buy.
• Mobile, social, and video remain the most profitable traffic sources for affiliates in 2025.
• Early shoppers win. Launch campaigns before mid-November to extend conversion windows.
• Attribution Stacking is mandatory to accurately track long, complex customer journeys.

Table of contents

• Optimizing for AI Is the New SEO
• AI Search Means Fewer Clicks But Higher Quality Ones
• Top Traffic Sources to Focus On in 2025
• Affiliate Attribution Stacking Will Become Mandatory
• Less Browsing, More Buying: The Conversion Trinity
• AI Tools Will Supercharge Creatives
• Influencer Marketing Gets More Performance-Based
• Longer Click-to-Conversion Windows
• Learning from Retail Giants
• Final Thoughts

Optimizing for AI is the new SEO

AI search is the new high-priority channel. With tools like Gemini, ChatGPT, and Google’s AI Overview delivering direct recommendations that appear above traditional organic results, the shopping journey has fundamentally changed.

To stay visible, you must shift your focus. You are no longer optimizing for keywords, but for how the AI models understand, summarize, and quote your content.

This strategic pivot is known as Generative Engine Optimization (GEO)—the only way smart affiliates can secure prime real estate in the AI-answer box.

Here are a few things that can help:

• Write clear, fact-based, well-organized content that directly answers user questions.
• Use natural phrasing (e.g., “What’s the best VPN for remote teams?” instead of “VPN for B2B”).
• Add FAQ sections, lists, and structured data (schema markup).
• Implement High-Value Schema: Use Review, Product, and HowTo schema markup to clearly label your content for AI summarization. AI prioritizes structured data for its answers.
• Think conversationally, if AI were answering, would it quote your site?

Affiliates and brands that adapt early will own visibility in AI search. Those who don’t risk disappearing behind generative summaries. Read more about GEO in our new blog.

AI search means fewer clicks but higher quality ones

According to Google’s VP of Search, Elizabeth Reid, AI Overviews are delivering “fewer but higher-quality clicks.” This shift is transformative for affiliates: users are skipping the endless browsing phase, clicking only when they are highly motivated or ready to buy.

This is exceptional news for profitability. Fewer, more intentional clicks directly translate to higher conversion rates (CVR).

• Lower Volume, Higher Intent: AI summaries effectively pre-filter casual users, sending only qualified traffic.
• Climbing Conversion Rates: AI surfaces only top-quality, highly relevant recommendations, boosting intent.
• On-Page Conversion is Key: Since clicks are intentional, your landing pages must be perfectly optimized to convert the moment that high-intent user arrives.

The clear takeaway is that any potential dip in organic sessions will be offset by more qualified leads and greater buying intent. In 2025, being visible in AI search isn’t just an option it’s the prime real estate for capturing qualified, high-value traffic.

Top traffic sources to focus on in 2025

Not all clicks are created equal, especially in Q4. Based on Yep Ads’ internal data, these are the channels worth your attention:

Mobile Traffic

Over 55% of holiday purchases are now made via mobile, and mobile-first creatives consistently outperform desktop formats in both CTR and CVR. Make sure your sites, landing pages, and checkout flows load fast, are scroll-friendly, and display CTAs clearly.

Social Traffic

TikTok, Meta, and Instagram continue to dominate attention, especially for lifestyle, gifting, and impulse-buy products. Short-form videos and user-generated content (UGC) are still the most powerful discovery tools for Gen Z and Millennial audiences.

Video Traffic

YouTube remains one of the most valuable long-term sources for affiliate SEO and paid ads. Educational videos (“Top 5 gadgets for 2025”) perform exceptionally well when paired with strong affiliate CTAs or pinned comments.

Early-Shopping Traffic

The shopping cycle now starts earlier every year. Affiliates who launch campaigns in late October gain longer conversion windows and better engagement before CPMs peak.

Affiliate attribution stacking will become mandatory

The post-cookie, multi-device, and AI-filtered customer journey has made simple “last-click” attribution unreliable.

As conversion windows lengthen and users interact with multiple AI summaries, retargeting ads, and social posts before buying, simple attribution is failing. Affiliates must implement sophisticated ‘Attribution Stacking’ solutions combining cookieless tracking, Server-to-Server (S2S) tracking, and post-view attribution to ensure they get credit for every touchpoint. This is no longer advanced; it’s essential for accurate commission tracking in a hybrid AI/mobile environment.

Less browsing, more buying: the conversion trinity

The AI revolution and mobile-first behavior mean users are skipping research stages. Today’s shoppers don’t scroll through ten tabs anymore; they go straight to what feels trustworthy. Your job this season isn’t just to get clicks, it’s to make people confident enough to buy once they arrive.

To maximize high-intent clicks, focus on the Conversion Trinity on your landing pages:

1. Trust Signals: Prominently display transparent affiliate disclosure and clear compliance badges.
2. Social Proof: Use specific, high-quality video/image testimonials and real-world results, not just basic star ratings.
3. Scarcity/Urgency: Deploy clear, active countdown timers and transparent inventory stock alerts specifically for the Black Friday deal period.

AI tools will supercharge creatives

AI is no longer just a brainstorm buddy; it’s a full-on creative assistant. This is the year affiliates move from using AI for “ideas” to using it for real execution. We’re seeing media buyers generate ad variations, run copy A/B tests, and even build auto-personalized creatives using AI tools, all in real time.

At Yep Ads, our partners using AI-assisted creatives report:

• 20–35% faster campaign setup times
• Higher CTRs for personalized visual variations
• Reduced creative fatigue due to faster iteration cycles

The smart affiliates aren’t replacing creativity with AI, they’re using it to test faster and scale smarter.

Influencer marketing gets more performance-based

Influencers are becoming affiliates and affiliates are acting like influencers. The line between content and commerce has blurred completely.

Micro-influencers, in particular, are now embracing CPA-based partnerships. This model gives brands flexibility and creators performance incentives without big retainers.

Expect to see more affiliates running hybrid setups: UGC videos → product links → remarketing funnels.

Actionable Tip: Look into using tracking link generation platforms that offer unique, custom coupon codes tied directly to the influencer’s name. This simplifies S2S/coupon tracking and makes performance measurement transparent, eliminating the need for relying solely on a basic last-click link.

Longer click-to-conversion windows

Black Friday doesn’t end on Friday anymore, it stretches through Cyber Week and beyond. But with inflation pressures and AI-aided research, users take longer to finalize purchases.

Affiliates should extend attribution tracking and maintain robust remarketing flows. Conversions are still coming; they’re just slower.

Learning from retail giants

Amazon, Walmart, and Target all proved one thing last year – storytelling, convenience, and experience now matter more than raw discounts.

Affiliate takeaway: Mix storytelling with clarity. Lean on trusted voices. When consumers are overwhelmed with choices, the brands (and affiliates) that make buying easy win.

Final thoughts

This year, the game isn’t about chasing the biggest discounts it’s about optimizing for the smartest conversions.

AI search is reshaping how people find deals. Mobile dominates attention. Social content drives emotion. The affiliates who succeed will be those who combine performance strategy, tracking mastery, creativity, and adaptability.

Warning on Q4 Metrics: Don’t panic if your Cost-Per-Acquisition (CPA) spikes in late November. Increased competition makes high CPMs inevitable. Instead, focus strictly on Return on Ad Spend (ROAS). A higher ROAS with a higher CPA is still profitable. The smartest affiliates are willing to tolerate a temporary CPA spike to capture high-value Black Friday buyers.

At Yep Ads, we help partners do exactly that, connecting over 6,500 brands and publishers across 140+ countries, delivering 4.3 billion clicks annually through advanced targeting, proprietary traffic channels, and creative expertise.

Ready to secure your high-intent traffic this Q4? Don’t lose commissions to bad tracking. Apply to Yep Ads’ Premium Publisher Network today and get a technical tracking consultation.

FAQs about black friday predictions

1. When should affiliates start running Black Friday campaigns?

Ideally, launch your first campaigns by late October or early November. This captures early shoppers who are browsing before prices peak and lets you test creatives before ad competition skyrockets.

2. What’s the best traffic source for Black Friday 2025?

Mobile and social traffic will dominate again this year. Mobile purchases now account for 55%+ of all holiday sales, and platforms like TikTok and Instagram remain unmatched for discovery-driven shopping.

3. How can affiliates optimize for AI search results?

To appear in AI-generated answers (like ChatGPT or Google’s AI Overview), focus on structured content, FAQs, and conversational keywords. Provide full, credible answers that make your site a reliable source for AI engines to quote.

4. Why are AI Overviews important for affiliate marketers?

Google’s AI summaries now appear at the top of search results, sometimes above all organic listings. Although they generate fewer clicks, they send higher-quality visitors who are already close to purchasing.

The post Black Friday and Cyber Monday 2025 predictions for performance marketers appeared first on Business of Apps.

This article was first published on ConsultMyApp blog

Legacy brands have always walked a tightrope between heritage and modernity. But today, that tightrope isn’t something you balance on. It’s something you sprint across. Just look at the App Store.

As of November 2025, half of the UK’s Top 20 Finance apps belong to century-old institutions—Lloyds, Barclays, HSBC, NatWest, Nationwide, and even HMRC. Names once etched above marble doorways now live on home screens, shoulder to shoulder with digital-first disruptors like Monzo and Revolut.

It’s proof that heritage isn’t a handicap—if you know how to translate it for a mobile-first world.

Let’s start where legacy branding began.

In 1884, Lloyds absorbed Barnett, Hoares & Co. The black horse above the door on Lombard Street became their symbol — a sign that actually dates back to the 1600s. Before that? Lloyds used a beehive. Both ran side-by-side for years because legacy mattered.

But markets evolve. And the biggest shifts aren’t slow and steady—they’re seismic.

1. When the world went online, brands had to follow

For decades, progress was incremental. Then the internet arrived, and suddenly every brand needed a website. Those who didn’t move fast enough vanished.

Then came the iPhone.
Then the App Store.

Web presence wasn’t enough anymore — brands needed to land directly on the home screen. Apps became the new storefronts. Push notifications became the new email. ASO replaced SEO. Customer journeys collapsed into taps.

The brands that survived weren’t the strongest or the biggest. They were the ones that adopted mobile thinking early.

2. The ones that didn’t evolve? They disappeared.

Hudson’s Bay Company — 355 years old, foundational to the creation of Canada — is the latest casualty. Recently shuttered.

Not because they lacked history.
But because they lacked mobile relevance.

They kept leaning on tradition.
They didn’t evolve their app.
They didn’t lean into TikTok.
They didn’t build modern acquisition engines.
Their brand became shorthand for “your grandmother’s department store.

History is an asset—until you mistake it for a strategy.

3. But over-correcting is just as dangerous

Modern marketing can chew up even the trendiest brands.

Take Duolingo.

A company adored for its warm, human, unhinged tone decided to announce that it was becoming an “AI-first company.” The internet dragged them instantly. Why?

Because identity matters more than trends.

Their charm was human.
Their community values humour.
Their communication felt personal.

So a sudden jump to “100% AI” felt cold and alienating.
As Professor Robin Landa wrote, “You can’t joke your way through backlash.”

Irony: AI could have helped them. A playful, mobile-first demo inside the app would’ve landed perfectly. Instead, they forgot what made them them.

4. So how do legacy brands stay relevant in a mobile-first era?

One brand is doing it unusually well: Lloyds.

Instead of doubling down on “how things used to be,” they’re modernising their marketing infrastructure — from brand design to channel strategy to app experience.

A. They’re restructuring around agility

The new CMO, Suresh Balaji, has shifted from the traditional “agency on record” model to a flexible partner-led model. No silos. No rigid creative frameworks. Just expertise plugged where needed.

This is the kind of structure mobile brands rely on — fast iterations, not 12-month creative cycles.

B. Their rebrand focuses on digital touchpoints

Wolff Olins redesigned the brand around its real front doors:
the website and the app.

Typography, motion, iconography — all built with mobile UI in mind.

This is the shift most legacy brands miss.
Your brand isn’t your TV ad.
Your brand is your App Store preview video.

C. They’re experimenting with unexpected digital ecosystems

At Media360, Lloyds announced a partnership with Activision Blizzard. Whether or not it becomes operational doesn’t matter. The signal matters.

A bank entering gaming is a mobile growth mindset:

• Go where the next billion users are
• Insert your brand natively
• Don’t wait until everyone else gets there first

Imagine Lloyds running the bank in World of Warcraft’s economy. Exposure, relevance, credibility — in one of the world’s most active digital ecosystems.

5. The new rule for legacy brands: Modernisation is mobile-first

We’ve moved past the website era.
We’ve moved past the social-media-only era.
We’re deep into the mobile attention economy.

Your:

• App Store screenshots
• Push strategy
• Onboarding flow
• Performance creative
• TikTok presence
• In-app UX
• AI augmentation
• Personalization
  …all carry more weight than a 300-year-old logo.

Legacy alone won’t keep anyone relevant.
But abandoning legacy won’t either.

The brands that win blend both:

• Respect the past
• Build for mobile
• Protect your voice
• Experiment without losing yourself
• Treat your app as your flagship
• Move at the speed of users, not committees

Legacy brands don’t fail because they’re old.
They fail because they stay still.

The ones that thrive?
They capitalise on their heritage—and translate it into the mobile era.

Are you a legacy brand in need of some solid advice? Get in touch.

The post Why legacy brands need a mobile-first strategy to compete in 2026 appeared first on Business of Apps.

Why SaaS Founders Should Care About HDD

Building a startup means constantly navigating trade-offs: limited capital, aggressive timelines, and overwhelming uncertainty. Most founders operate on gut instinct. They often don’t know whether customers even want what they’re building, let alone if they’ll pay for it. This is where many startups go sideways. They move fast, build hard, and burn out because they made decisions based on assumptions, not evidence.

Hypothesis-driven development (HDD) offers a smarter path forward. It’s a methodology and mindset that shifts product development from guesswork to validation. Instead of asking, “Can we build this?” HDD asks, “Should we build this, and how do we know?”

By treating every idea as an experiment, HDD allows startups to move fast without building blindly. It prioritizes learning over assumptions and turns your product roadmap into a strategic asset, not a list of hopeful bets. HDD isn’t just another trendy acronym; it’s your blueprint for clarity, focus, and traction in a world that rewards those who learn faster than everyone else.

Why Feature Assumptions Kill Startups

Building based on untested assumptions is one of the costliest mistakes in early product development. Startups often launch features because they “feel right,” only to discover users don’t need or want them.

Evidence-based decision-making also makes communication with investors and stakeholders easier. You’re no longer saying “we believe this will work” but rather, “we tested this, and here’s what we found.” That builds trust and positions you as a thoughtful, data-savvy founder. This mindset gives you a competitive edge in today’s fast-moving tech landscape. You don’t have to guess your way to success; you can learn your way there, one hypothesis at a time. And that discipline doesn’t slow you down; it speeds you up in the right direction.

There is no need to spend precious engineering hours and burn through runway on features that may never gain traction. Worse, you’re delaying the learning that could’ve steered you in a better direction. HDD addresses this by forcing clarity early in the process. You treat assumptions as hypotheses, not truths. Instead of building a new feature because it sounds good, you define a testable prediction:

“If we simplify our signup form, we expect user completion rates to increase from 40% to 60% in 7 days.”

This data-driven approach creates team alignment and builds trust with stakeholders and investors. You’re not just saying, “We believe this will work,” you’re saying, “We tested this, and here’s the outcome.” You don’t just work harder; you work smarter. You don’t build everything; you build what matters. And that’s how you create traction, confidence, and a product that stands a chance in the real world.

What Is Hypothesis-Driven Development?

Hypothesis-Driven Development is a product strategy that treats ideas as experiments. Before investing time or resources into development, teams define clear, testable hypotheses and design lightweight experiments to validate them.

It’s the application of the scientific method to product development. You start with a hypothesis, define expected outcomes and success metrics, run a small test, and measure the results. Based on your learning, you either move forward, adjust, or discard the idea. HDD helps you eliminate guesswork, reduce waste, and prioritize only what delivers real value.

HDD doesn’t slow you down; it helps you move faster in the right direction. For SaaS startups under pressure to find a product-market fit, that speed + precision combo is a major advantage.

Hypothesis-drive development for SAAS founders

Source: Designli

HDD vs. Agile vs. Lean Startup: How They Work Together

Founders often wonder: “Is HDD just Agile or Lean Startup with a different name?”

The answer: They’re complementary frameworks. Here’s how they fit:

• Agile helps teams execute faster by breaking work into sprints and adapting to change.
• Lean Startup promotes iteration and validated learning to avoid waste.
• HDD provides the missing layer: deciding what to build in the first place by testing assumptions early.

Example:

• HDD: Hypothesis “A 7-day trial will improve paid conversions by 20%”
• Agile: Sprints to build and test the new trial flow
• Lean: Evaluate whether this aligns with the broader business model

When used together, these approaches create a closed-loop learning system. You validate ideas (HDD), build quickly (Agile), and align with business goals (Lean). These three frameworks drastically improve your learning cycle and allow you to move with confidence instead of chaos. So, rather than picking one over the other, innovative founders integrate all three, using HDD to discover what matters, Agile to build it quickly, and Lean to make sure it fits the bigger picture. That’s how modern startups win.

How to Write a Good Hypothesis

A strong hypothesis is the beating heart of Hypothesis-Driven Development (HDD). It’s not just a random idea scribbled on a whiteboard; it’s a structured, testable statement that helps your team learn something specific about your users, product, or business. The anatomy of a strong hypothesis comes down to three essential parts:

Assumption + expected outcome + measurable metric

Weak vs. Strong Hypotheses

• Weak: “We think users will like the new onboarding design.”
  → Vague, subjective, and unmeasurable
• Strong: “We believe that showing a visual progress bar during onboarding will increase completion rates from 45% to 65% within one week.”
  → Specific, actionable, and data-driven

This version is powerful because it has structure. You’ve identified what you’re changing (adding a visual progress bar), what you expect to happen (an increase in completion rates), and the metric to track (completion rate percentage). Plus, you’ve given it one week, which helps keep your experiment lean and focused. Strong hypotheses help your team stay aligned and action-oriented. Weak ones open the door to confusion, scope creep, and wasted development time. Measurable outcomes allow your team to objectively say, “Yes, this worked,” or “No, this didn’t move the needle.” And that clarity is invaluable for early-stage startups, where time and budget are limited.

As a founder, pushing your team to write better hypotheses consistently is one of the simplest ways to improve your product strategy and make smarter bets faster.

If you want a deeper breakdown of how validation supports smarter technical choices, this guide outlines the key steps and scenarios.

The HDD Process: 5 Steps to Smarter Product Development

Instead of blindly executing roadmaps, HDD helps teams break product decisions into 5 focused steps, turning assumptions into learning loops. Below is the step-by-step process that turns hopeful ideas into tested, strategic decisions.

Step 1: Identify Assumptions

Every product idea is built on a foundation of assumptions, many of them invisible, unspoken, and untested. These assumptions drive your roadmap, UI decisions, pricing, and positioning. If they’re wrong, everything downstream suffers.

Start by asking: What needs to be true for this idea to succeed?

Write down every assumption you’re making about users, value, pricing, and behavior. Then prioritize based on risk:

• Which assumptions, if wrong, would have the biggest impact?
• Which ones are easiest to test right now?

This clarity prevents you from building on shaky foundations. It gets you out of “we think” mode and into a position where you build on what you know or will soon know.

Step 2: Turn Assumptions Into Hypotheses

Once you’ve identified your assumptions, it’s time to turn them into clear, testable hypotheses. This is where things take shape, where speculative thinking becomes structured learning.

Translate high-risk assumptions into testable predictions. Structure each hypothesis around one key variable and define:

• What you’re changing
• What you expect to happen
• How you’ll measure the result
• Over what timeframe

Example:

Assumption: “Users don’t complete onboarding because it takes too long.”

Strong hypothesis: “If we reduce the onboarding steps from five to three, we will increase completion rates from 45% to 65% over one week.”

This statement is clear, specific, and measurable, ready for testing. As a founder, this step is where you start turning vague ideas into an actionable product strategy.

Step 3: Design Lightweight Experiments

Now that you’ve got a solid hypothesis, it’s time to design the smallest possible experiment to test it, quickly and without burning unnecessary resources. This is where HDD shines. Instead of building out full-blown features or launching months-long initiatives, you’re asking: What’s the leanest way we can validate this?

Low-effort experiment types include:

• Landing page A/B tests
• Click-through prototypes (e.g., Figma)
• No-code tools (e.g., Webflow, Bubble)
• Email campaigns
• User interviews

The goal is maximum learning with minimum building. You don’t need to scale what hasn’t been validated.

Step 4: Measure Results and Gather Feedback

It’s time to measure the results and gather insights. This is where your hypothesis holds water or falls apart. Either outcome is a win.

Also collect qualitative feedback:

• What confused users?
• Where did they drop off?
• What patterns emerged?

When you learn how to read your data, interpret it well, and act on it fast, you create a competitive advantage most startups never develop.

For how to spot churn early using feedback loops, see this guide.

Step 5: Decide – Pivot, Persevere, or Stop

After measuring results and reviewing feedback, it’s time to pivot, persevere, or stop. This is where HDD becomes not just a development method, but a decision-making framework.

Based on what you learn, choose one of three paths:

• Persevere: Your hypothesis was validated. Build it out.
• Pivot: You saw partial success but need to adjust the idea.
• Stop: The test failed, so it’s time to redirect.

Even a “failed” test is a win; it saves you from building the wrong thing. HDD empowers founders to make these decisions quickly and confidently.

HDD design-making framework

Source: Designli

Tools That Make HDD Work at Scale

The right tools help your team stay focused, aligned, and organized throughout the HDD process. Here are some that support each step:

1. Lean Canvas & Business Model Canvas

These are two of the most powerful foundational tools for startup founders. Why? Both canvases are designed to help you visualize assumptions in your business and identify high-risk areas worth testing. They are ideal for early-stage SaaS teams planning initial experiments.

2. OKRs (Objectives & Key Results)

Tie your hypotheses to strategic goals.

Example:

• Objective: Improve activation
• Key Result: “Increase onboarding completion from 50% to 70%”

This keeps experiments aligned with business impact.

3. Airtable

A supercharged spreadsheet that can be customized into a lightweight database. You can create columns for hypothesis statements, status, metrics, experiment owners, timelines, and outcomes. Add views by week, team, or experiment type, and your team instantly has a living experiment dashboard.

4. Notion

Create an “HDD Hub” to document hypotheses, embed results, link user interviews, and centralize all insights. Especially useful for cross-functional teams.

5. Jira Add-ons

For engineering teams already using Jira, extensions allow you to treat experiments as tickets, connecting them to sprints and user stories.

This keeps testing efforts visible in your dev workflow.

6. Google Analytics

Good for macro-level insights like bounce rate, flow analysis, and campaign performance. Essential for testing landing pages or top-of-funnel ideas.

7. Mixpanel

Track specific in-product events and funnels. Ideal for understanding user behavior and validating feature engagement.

8. Amplitude

Powerful cohort analysis and retention tracking. Great for long-term experiments measuring activation or churn over time.

Build Less, Learn More, Win Faster

HDD gives SaaS founders a smarter way to work. Instead of rushing to build and hoping for adoption, you move with clarity, focus, and evidence.

It helps you:

• Reduce waste
• Align teams around data
• Build things users actually want
• Communicate results to stakeholders with confidence

Learning faster than the next startup gives you an edge in a world where capital is limited and competition moves quickly. Hypothesis-driven development is a fundamental step to optimizing product strategy. Learn more about our view on it and how it can help you develop a product. Schedule your consultation and let’s get started.

The post Hypothesis-driven development: the smarter way to get your product on track appeared first on Business of Apps.

As the productivity app market evolves from feature-based competition to value-driven growth, global developers are seeking clear and actionable insights to navigate this transformation. Today, SocialPeta, in partnership with Mega Digital, officially released the Productivity app marketing trends 2025 report. Leveraging a database of 1.6 billion ad creatives and real-time data from over 70 countries, the report reveals both the emerging shifts and enduring trends shaping the global productivity app landscape.

Let’s begin with the global advertising landscape for non-gaming mobile apps. In 2025, the market recorded an average of 107,300 active advertisers per month, marking a 17.8% year-over-year decline. However, new advertisers accounted for 11.7% of the total—averaging over 12,500 per month, up 4 percentage points from the same period last year. Throughout the year, the proportion of new advertisers consistently stayed above 10%, peaking at 12.8%.

Overall, while the non-gaming mobile app advertising market has seen a contraction in total volume, new entrants remain highly active, driving a dynamic environment of stock optimization and continuous market renewal.

Global mobile app marketing trends in 2025

Source: SocialPeta

In 2025, the productivity app market is experiencing a noticeable reshuffle. The number of active advertisers averaged 8,900 per month, a 6% year-over-year decline, indicating a period of market consolidation and stock optimization.

Yet, the momentum of new entrants remains robust. Since April, new advertisers have consistently made up over 30% of the total, peaking at 38.2%, with more than 2,900 new advertisers joining each month. This steady influx of newcomers is actively redefining the industry’s competitive landscape.

Global productivity app marketing trends in 2025

Source: SocialPeta

Regional differences in the market have become increasingly distinct. Europe continues to lead globally in the number of productivity app advertisers, though the figure has slightly declined by 2.6% year over year. In contrast, North America demonstrates strong advertising momentum, with the average number of creatives surpassing 110 per month—a year-on-year increase of over 20%—cementing its role as the key showcase market for productivity tools.

Meanwhile, emerging regions such as Southeast Asia and Africa are experiencing steady growth in advertiser numbers. However, creative volume in South Asia and the Middle East has dropped sharply by 41.6% and 39.0%, respectively. These shifts highlight the increasing importance of precise regional targeting and localized strategies in global marketing campaigns.

Global productivity app advertising trends by region

Source: SocialPeta

The sophistication of advertising strategies has a direct influence on conversion performance. Video creatives account for 68.3% of all ad materials, with 80% lasting under 30 seconds, reflecting a clear preference for concise, high-impact storytelling. Among static formats, square images have become dominant due to their strong cross-platform adaptability.

In terms of ad copy, English represents 72.7% of total usage, with frequently used keywords such as “time” and “free” effectively resonating with user motivations. Interestingly, 27% of ad copies feature exclamatory tones or emojis, which enhance emotional appeal and audience engagement.

Types of ad creatives for global productivity apps in 2025

Source: SocialPeta

As a co-publisher of the report, Mega Digital highlighted that Southeast Asian markets—particularly Vietnam are poised for rapid growth. Despite the region’s digital economy expanding by over 20% annually, ongoing challenges such as language barriers and AI adoption rates below 40% continue to pose both obstacles and opportunities for localized innovation in productivity tools.

A standout example is Trello’s “Ramadan Productivity Mode”, which demonstrates how culturally tailored features can significantly boost user engagement and relevance in diverse markets.

Performance across different platforms for utility apps

Source: Mega Digital

For productivity tool advertisers, success in today’s highly competitive market hinges on a cross-platform advertising strategy. When choosing platforms, Google is best for reaching users with clear intent, Meta excels at precise audience targeting, and TikTok drives creative engagement through dynamic storytelling. The most effective campaigns focus on video and interactive formats, leveraging AI to optimize performance and refresh creatives quickly.

Key recommendations:

• Run cross-platform campaigns, allocating budgets strategically based on each platform’s strengths
• Prioritize video and playable ads, using AI tools to scale efficiently and test new creatives
• Keep ads short, engaging, and interactive to capture attention and sustain viewer interest
• Incorporate social proof (such as user reviews) and lifestyle-driven content to build trust and emotional connection with audiences

By following this approach, advertisers can drive measurable installs and ROI, while laying a strong foundation for sustainable, long-term growth.

Recommendations for advertisers

Source: Mega Digital

Spanning around 30 pages, the report also features the top 20 advertising cases on iOS and Android platforms, three best-practice case studies from leading productivity apps, and an overview of common creative formats used in productivity app advertising.

Download your copy today to stay ahead in this fast-changing market.

The post Productivity app marketing trends 2025 appeared first on Business of Apps.

Meta and Google have been dominating ad spend for what seems like ages. However, there is a vast world outside walled gardens, and UA teams might be leaving eager users and revenue on the table if they are only focusing on Meta and Google.

In our latest episode of App Talks, David Murphy sat with Omri Argaman, Co-Founder and CMO of Zoomd, to dissect what seems like perennial questions: what are walled gardens and how to break free of them?

What is a walled garden?

First things first, what is a walled garden? Most probably know this already, but as a refresher: walled gardens, or closed platforms, are software systems wherein the provider of the platform has complete control over the content, applications, and other media published on the platform and can restrict access as it sees fit.

In the app industry, Meta and Google are by far the biggest and most important walled gardens. To them, we can add TikTok, which, too, has emerged as a major UA channel in recent years.

Walled gardens, however, are very hard and very expensive to get into, because, as Omri puts it, “everyone is advertising there. Your biggest competitors are there. Your new competitors are there. Your old competitors are there. Everyone needs to advertise there because that’s where most of the traffic is.”

“Everyone is advertising there. Your biggest competitors are there. Your new competitors are there. Your old competitors are there. Everyone needs to advertise there because that’s where most of the traffic is.”

Beyond walled gardens

Omri argues in favour of a more diversified approach to app advertising, advising UA teams to look beyond walled gardens and consider the open internet. This includes ad networks, SDKs, affiliate channels, mobile operators, device manufacturers, DSPs, incentivised channels, and video networks.

He suggests that UA managers should be advertising there first for three important reasons. First, it’s a bigger space with less competition, making it easier and less expensive to get into. Second, many advertisers are not tapping into the open internet opportunity since it’s perceived as riskier and more difficult to make work. Finally and most importantly, the open internet is a huge space with a lot of potential users waiting to be impressed and convinced to download.

Advertisers, however, are often daunted by the open internet and unwilling to venture out of the comforts of walled gardens. As Omri explains, “a lot of times advertisers do not advertise there because they don’t have the knowledge, they don’t have the experience, they don’t know how to measure it. That does not mean that there are no customers there for you. There’s a lot of business for any type of advertiser to grow and a lot of users to acquire.”

Beyond walled garden: Unlocking new paths for UA

Source: Business of Apps, App Talks

“A lot of times advertisers do not advertise there because they don’t have the knowledge, they don’t have the experience, they don’t know how to measure it. That does not mean that there are no customers there for you. There’s a lot of business for any type of advertiser to grow and a lot of users to acquire.”

Challenges and opportunities, swings and roundabouts

As one could probably guess, success outside of walled gardens comes at a price, usually in the form of sweat, blood, and tears… Well, not literally at least… Well, maybe just a few beads of sweat…

Omri outlines some of the challenges one must be prepared to face:

• Knowing what you want — one must know what, or rather who, they want. To get real value from the open internet, one needs to be strategic about one’s budget and targeting. Simply put, spending money willy-nilly won’t get you far; one needs to know exactly what type of user one wants to acquire and the GEOs one wants to acquire them in.
• Playing the long game — one requires a lot of patience to make it on the open internet. Success doesn’t come quickly and it doesn’t come easily, and rushing things will only slow you down… and waste you money.
• Fraud — one must be prepared to face and hack one’s way through a lot of fraud on the open internet.

While this is not exactly the most flowery praise for the open internet, Omri believes it’s still worth the investment and the effort… and the sweat. According to an internal case study of an international eCommerce company, by leveraging advertising on the open internet, they managed to decrease their acquisition costs by 30% while growing their market share by 20%.

Advice for success

So, how does one actually make it on the open internet? Omri has a few pieces of advice:

• Don’t be afraid — it’s daunting for sure, but nothing good comes from staying in your comfort zone and doing what everyone else is doing
• Set a budget and test — preferably in a big enough country
• Be patient and don’t rush it
• Be cautious of fraud — consider using a third-party anti-fraud solution

This episode of App Talks was created by Business of Apps and David Murphy for Zoomd. Find out more about the company on their website.

Watch the full video, embedded above, to discover all of Omri’s insights and advice. You can watch all the episodes of App Talks here.

The post Beyond walled gardens: Unlocking new paths for UA appeared first on Business of Apps.

We all start somewhere.

In the app world, that “somewhere” is usually zero downloads, zilch users, zip revenue.

Whether you’re a bright-eyed start-up or a legacy brand elbowing your way into the market, at some point the thought hits:

What if the app isn’t just struggling… what if it’s actually bad?

But listen…

Imagine a restaurant with the best chefs in the world. It should be packed, aye?

• Except imagine it’s buried down a back street — no signage. (Hint: acquisition.) 
• And if someone does stumble inside, there’s no warm welcome. (Hint: engagement.)
• No inviting décor, no thoughtful design. (Hint: UX.) 

We wouldn’t doubt the talent of the chefs if the restaurant flopped.

This post was first published on consultmyapp.com.

It should be the same with apps. Yet the industry’s only 17 years old — essentially a teenager, still trying to drive home what older industries learnt long ago: the core product isn’t the whole picture.

Before blaming the app, nine times out of ten there’s something in the growth funnel holding you back.

So, instead of catastrophising in your Monday meetings, ask these questions instead:

Are we looking for our target audience in the wrong places?

Weak acquisition often gets mistaken as weak demand.

Giving a false impression of market interest and a skewed view of all your team’s hard work.

Acquisition channels need testing just as much as the product itself. And chances are, your budget isn’t limitless, investors, internal or otherwise, won’t wait forever to see traction.

That’s why it’s crucial to move fast, read the signals, and focus on the channels that bring decent volume.

Top tip: If your store impressions aren’t growing (but your conversion rate is healthy), you don’t have an app problem, you have a channel problem.

App Store Optimization is usually the first port of call

Source: ConsultMyApp

Sometimes paid acquisition is the way to go

In that case, paid acquisition would make far more sense to supplement the work of App Store Optimization. If you were running Meta campaigns, for example, you could reach people by job role or interest, spark intent there, and then hand them over to a well-optimized store page to convert.

Ultimately, it all comes back to the same principle:

Make sure you’re looking for your users in the right place.

If you’re struggling and thinking, “no one is downloading our app,” it might be time to switch channels — not question the app’s potential. You’d be amazed how often teams double down on the wrong path. Don’t let that be you. You’ve heard it before:

“Float like a butterfly, sting like a bee.”

That applies here too.

Are we telling the right story once people find us?

Okay, you’ve nailed your “hero” channels.

You’ve gone from Ant-Man to the Hulk in terms of app visibility.

You’re bigger, you’re bolder — but are people converting?

Let’s talk about optimizing your app store listings for conversion — arguably one of the most valuable elements of App Store Optimization. This is an absolutely critical moment: the point where awareness turns into decision.

Most developers spend months perfecting the product, and not nearly enough time on the page that sells it.

Your store page is your storefront. Not just technically — psychologically.

If we dissect what makes any app store page work, it really comes down to a few core pillars — design, pain points, features, and proof.

Everything you show (or say) ties back to one of those.

That’s where hypothesis-led testing comes in.

“Which of these pillars do I think is under-performing and how can I test it?”

• If design isn’t landing → test new visual hierarchy or tone
• If messaging isn’t resonating → test the pain point or promise
• If trust feels weak → test social proof elements (badges, ratings, testimonials)

Each test gives you data on which part of the story is connecting — or falling flat.

And when you run those tests consistently (through PPO, CPPs, or Play Store experiments), you’re not guessing anymore — you’re iterating your way to clarity.

In the Shopping category, Walmart is a great example. Over the past six months, it’s refreshed its App Store screenshots more than ten times — cycling through seasonal campaigns (Easter, Back-to-College, Halloween, Black Friday) while continuously reinforcing core features like Express Delivery, curbside pickup, and its “Sparky” AI assistant. It’s not reinventing the product — it’s systematically testing what story converts best.

Those changes aren’t aesthetic refreshes — they’re experiments in narrative and value proposition. Each one tests which emotional hook converts best at a given moment.

That’s the mindset to borrow — treat your store as a living experiment, not a static poster. This is where you control perception. It’s where you show what your app feels like, not just what it does.

Are users getting value fast enough?

This question alone could fill an entire whitepaper.

It’s a tasty skillset-cocktail of onboarding, UX, lifecycle strategy, and feature discovery, all heavy hitters that could take this article to 10,000 words.

But for now, keep it simple. Again, Ask yourself:

Are users getting value fast enough?

The quickest way to find out? Drop-off.

If you’re seeing a steep fall between install and activation, that’s your signal.

Though, remember, some industries are naturally high-churn (fintech for example), so always compare against category benchmarks before diagnosing a problem.

If your early retention curve looks like a cliff, it means users are slipping away before they hit their first “aha” moment” — that instant where they truly understand your app’s benefit.

You can measure this in Mixpanel, Amplitude, or Firebase by tracking time-to-value — the average time it takes new users to complete their first key action, whether that’s a workout, transaction, spin, or show watched.

Each test should aim to shorten that window.

That delay often comes from one of three sources: 

• Onboarding friction — too many steps before reward
• Weak value communication — users don’t yet see the benefit
• Lack of guidance — users hit a dead-end before success

Once you’ve looked at the numbers, look at the language. Metrics show when people drop off, but reviews tell you why.

 Top tip: Run review sentiment analysis to surface the real blockers behind drop-off. Comments like ”too confusing”, ”didn’t know what to do”, or ”took too long to get started” usually point straight to friction in onboarding or feature discovery.

Every second you shave off that first “aha” moment — and every barrier you identify in your reviews — compounds into stronger engagement and healthier retention.

That’s how smart onboarding becomes sustainable growth.

Are we keeping momentum after the first session?

If the first session is the spark, the second is the fire. Most apps don’t lose users because the product’s bad — they lose them because nothing happens next.

That post-install silence is deadly. The user’s done their first workout, played their first hand, or ordered their first coffee — and then? No reminder, no reinforcement, no reason to return.

This is where engagement and MarTech really earn their keep. You need a lifecycle strategy that turns your app into a living rhythm, not a one-hit wonder. Push notifications, in-app messages, emails, badges — every touchpoint should nudge users from curiosity into habit.

The key KPIs to watch here:

• Day-2 and Day-7 retention: Does the spark catch?
• Return-session rate: How many users come back unprompted?
• Push influence rate: Are your messages driving real action?

When these numbers stall, look at your creative and CRM alignment.  If UX, product, and lifecycle are telling three different stories, users feel it — and they’ll quietly drift away.

 Top tip: Audit your push and email triggers. If more than 50% of your messages are reactive (sent after inactivity), your lifecycle isn’t a loop — it’s a lifeline. Flip that ratio: proactive beats panic every time.

Are we learning fast enough?

When growth slows, most teams look inward — they blame the app, the market, or even the budget.

But more often than not, it’s not a product problem — it’s a learning problem.

App marketing isn’t a single playbook; it’s a constant experiment. Every store test, creative variation, and push campaign leaves behind a data trail — a story of what your audience responded to.

The question is: Are you listening to it? 

Here, BI & Data Analytics meets Creative and Engagement. It’s not just about running experiments; it’s about building a culture that turns results into repeatable knowledge.

You’ll need a clear testing framework (hypothesis, metric, outcome), a way to store insights, and a rhythm for iteration. Otherwise, you’re just throwing darts in the dark and calling it innovation.

The KPIs that matter most:

• Experiment velocity: How many meaningful tests do you run per month?
• Iteration rate: How often do insights lead to an actual change?
• Learning adoption: Do teams use what they learn?

 Top tip: Keep a single shared “experiment ledger” — a living document where every hypothesis, test, and outcome is logged. Over time, it becomes your most valuable asset: proof that you’re not guessing anymore — you’re evolving.

It’s rarely the app itself: The real reasons apps struggle to grow

At some point, every team asks the question: “Is our app in trouble?”

But more often than not, it isn’t. The cracks usually live elsewhere — in how users find you, what they see when they arrive, how fast they feel value, how consistently you re-engage them, and how well you learn from it all.

Because apps don’t fail in a single moment. They fade — quietly — when one part of the growth engine slows and no one notices.

So before deciding your app’s “in trouble”, look wider. Fix the friction. Tighten the loops. Keep learning.

Chances are, the app itself isn’t broken. It’s just waiting for everything around it to catch up.

The post Five questions to ask before worrying if your app is in trouble appeared first on Business of Apps.

Food delivery apps are now an everyday utility for millions of consumers worldwide. The category surged during the pandemic, but even as offline dining returned, delivery has not slowed. Instead, competition has intensified. Dozens of players are fighting for attention in every market, often with overlapping restaurant supply and nearly identical user experiences. For growth marketers, this means acquisition and retention strategies must be sharper, more localized, and more sustainable than ever.

We’ve seen what works in food delivery campaigns across regions and what falls flat. Below are the pillars that consistently drive growth when executed with precision.

Acquisition strategies: Getting the first order right

Incentivize the download with a promo code or discount

Promotions remain the single most effective lever to nudge a first-time order. A free delivery, discounted basket, or a “first item free” mechanic can reduce the friction of trying yet another delivery app. The key is to tie promo codes directly to install attribution. This allows teams to measure which channels justify the cost of the subsidy. In our testing, offers structured around specific items (for example, a free pizza or dessert) often outperform generic “X off your first order” because they spark an immediate craving and drive order intent faster.

Localize ads for relevance and compliance

Food is inherently local, and ads should reflect that. Consumers respond to language, cuisine, and restaurant names they know. Campaigns that highlight “order pho from your local favourite” or “get tacos from the spot around the corner” outperform generic messages. Localization also applies to compliance: promotions and creative must meet city or country-specific regulations. Marketers who ignore these nuances risk wasted spend or outright rejection from ad platforms.

Highlight top local restaurants in your creative

One of the strongest trust signals in food delivery is seeing a familiar restaurant logo. Spotlighting top local partners not only builds credibility but also signals to users that their go-to places are available. In campaigns, this tactic serves as a shortcut to trust. For emerging apps, early partnerships with popular restaurants can be a decisive growth driver.

Hyper-targeting by neighbourhood, cuisine, and time of day

Food delivery is a classic hyperlocal product. Ads perform best when they map directly to a user’s context: their location, food preferences, and even the time they are most likely to order. Serving a “late-night burgers delivered fast” ad at 11 p.m. near a university neighbourhood hits differently than a generic “download our app” ad. Programmatic targeting combined with in-app behavioural signals lets marketers spend efficiently and maximize relevance.

Event-driven bursts: Capturing peak demand

Certain moments create natural spikes in food delivery demand. Major sports games, festivals, or national holidays reliably drive order surges. The most successful apps lean into these “tentpole” events with heavy-up campaigns: more budget, time-sensitive creatives, and tailored promotions.

For example, during the World Cup, we observed that apps promoting “match day meal deals” drove significantly higher basket sizes compared to baseline orders. Similarly, during cultural holidays like Lunar New Year or Diwali, localized offers featuring traditional meals can both respect local culture and boost installs. The challenge is planning early enough to secure creative, restaurant partnerships, and media inventory before these high-demand windows.

Retention and upsell: Beyond the first delivery

Acquisition is expensive; retention is where profitability is won. We’ve seen several strategies help food delivery apps keep users active:

• Loyalty programs: Points systems or “order X times, get Y free” mechanics encourage repeat orders without requiring constant deep discounts.
• Push and in-app offers: Timely reminders (“Your favourite ramen spot is open. Free delivery tonight”) drive incremental orders.
• Personalized recommendations: Highlighting restaurants or cuisines a user has browsed but never ordered from nudges exploration and increases average order value.

Gamification elements, like streaks or badges for consecutive orders, can also create habit loops, particularly effective with younger audiences.

Measurement and margins: Balancing growth with sustainability

Food delivery apps face unique measurement challenges. Multi-touch attribution across affiliates, paid social, and organic channels is messy, especially with offline conversions like call-in restaurant orders feeding into the funnel. Some of this falls into what we call the “dark funnel,” where installs or reorders are clearly driven by marketing but not easily traceable to a single touchpoint.

At the same time, margins in food delivery are razor-thin. Every discount or promo code eats into profitability. That’s why attribution clarity is so critical. Without it, teams risk cannibalizing organic orders with unnecessary promos. In our experience, disciplined promo tracking, using unique codes by channel and tight cohort analysis, is the only way to balance scale against margin pressure.

Creative approaches: From UGC to local influencers

Creativity is as important as media efficiency. Campaigns that integrate user-generated content, for example, customers sharing their unboxing or “game day spread”,  outperform static product ads. Local influencer partnerships can also drive trust, especially when influencers highlight their own favourite neighbourhood spots on the app.

Social proof, whether via reviews, ratings, or restaurant badges like “most popular in your area,” should be featured prominently in ads. Consumers care less about the delivery brand itself and more about the reassurance that others in their community trust it.

The forward view: Sustainable growth in food delivery

The food delivery market will continue to expand, but the winners will be those who master local relevance and efficiency. Heavy discounts and broad campaigns may bring a temporary surge in installs, but they are not sustainable. The real growth comes from weaving together localized acquisition, tentpole event bursts, and smart retention strategies, while keeping a close eye on measurement and margin impact.

For marketers, the challenge is clear: treat food delivery not as a commodity service but as a deeply personal, local experience. When campaigns respect that reality, users are more willing to try, stay, and reorder.

At Creative Clicks, we’ve observed across multiple regions that the apps balancing creativity, precision targeting, and disciplined measurement are the ones that achieve durable growth. The playbook is evolving, but the fundamentals remain: relevance, timing, and trust win every time.

Let’s work together to make your food delivery app the first choice for users in your market, and build campaigns that keep them coming back long after the first order.

The post From first order to loyal customer: Growth lessons from the food delivery vertical appeared first on Business of Apps.

Across Latin America, where inflation and currency instability continue to erode purchasing power, a new generation of fintech solutions is offering something rare: financial predictability. Stablecoins, digital assets pegged to the U.S. dollar, have become a bridge between the volatility of local economies and the stability of the global financial system.

Among the projects leveraging this potential is Vesseo, a digital wallet built on the Stellar blockchain, designed to make access to stable, borderless money as seamless as sending a WhatsApp message.

Developed with technical support from Cheesecake Labs, Vesseo enables users in Argentina and Brazil to store value in USDC, send and receive payments, and convert between digital and traditional currencies with minimal friction.

A new financial reality: Stability amid volatility

In parts of Latin America where inflation frequently undermines local currencies, access to stable digital assets can make the difference between preserving or losing value overnight. Vesseo enables users in these markets to hold USD Coin (USDC), a digital dollar fully backed by U.S. reserves, helping them protect their savings and earnings from local currency volatility.

At the same time, Vesseo serves a different purpose in countries with more stable currencies. For these users, it provides convenience and flexibility. They can access digital dollars at competitive exchange rates, manage their funds directly within the wallet, and travel without relying on banks or traditional currency exchange services.

Through Vesseo, individuals and small businesses can receive payments in USDC from international clients, employers, or local transfers without the need for a U.S. bank account. Everyday transactions are equally seamless. Users can make purchases through QR code payments, a format already common across Argentina and Brazil, PIX Payments in Brazil, or through the upcoming international Visa debit card, which automatically converts USDC to local currency at the time of purchase.

Whether it is protecting savings in inflation-prone economies or simplifying access to global money in more stable ones, Vesseo connects both worlds and offers a consistent, borderless financial experience built for the modern economy.

Connecting the digital and physical worlds

One of Vesseo’s defining features is its ability to bridge blockchain finance with real‑world accessibility. Through its integration with MoneyGram International, users can withdraw or deposit cash in local currency across hundreds of thousands of locations worldwide, creating a tangible link between digital assets and physical money.

This infrastructure enables both cross‑border payments and instant P2P transfers. Users can send USDC within seconds across the Stellar network or to compatible wallets and exchanges, with options to withdraw to local bank accounts in Argentina and Brazil.

Vesseo also connects the digital and physical worlds through local payment systems that users already trust. In Brazil, the wallet integrates with Pix, the country’s instant payment network, allowing users to pay merchants or transfer funds directly from their digital balance.

In Argentina, users can complete purchases using QR code payments, a system widely adopted across retail, restaurants, and everyday services. Whether someone is paying for groceries in Rio de Janeiro, booking a coworking space in Buenos Aires, or traveling between the two countries, Vesseo ensures that payments remain seamless and familiar while being fully settled in USDC behind the scenes.

According to the World Bank Global Findex 2025, nearly 45% of adults in Latin America remain unbanked or underbanked due to high costs and institutional distrust. Platforms like Vesseo help close that gap by providing direct, compliant access to stable digital dollars and cash‑out options through a regulated global network.

Empowering freelancers and businesses with virtual U.S. accounts

Beyond an inflation hedge, Vesseo is a valuable tool for freelancers, remote workers, digital nomads, and entrepreneurs who rely on international payments. Users can open a virtual U.S. virtual account for free to receive dollar payments directly within their wallet.

Traditional remittance systems often impose high costs, fees near 7% and delays of up to five business days, according to the Inter‑American Development Bank’s (IDB) 2024 report. By contrast, USDC transfers over Stellar settle within seconds, typically costing less than a cent in network fees.

By combining compliance, user transparency, and real‑world accessibility, Vesseo and Cheesecake Labs show how blockchain can responsibly enhance financial freedom and inclusion.

Looking ahead

As digital wallets evolve, the convergence of blockchain infrastructure and traditional cash networks will define the next phase of fintech across Latin America.

Whether it helps a freelancer in São Paulo receive instant USD payments or allows a family in Buenos Aires to protect savings from inflation, projects like Vesseo signal a more connected, equitable financial future built on trust, stability, and access.

The post Building trust and access through stablecoins appeared first on Business of Apps.

When Epic Games took Apple to court in one of the most high-profile tech antitrust cases in recent memory, it wasn’t just about Fortnite skins or App Store fees. Beneath the legal wrangling and courtroom theatrics was a deeper question: who really controls the economics of mobile gaming?

Now, years after the original case first gripped Silicon Valley, the dust is beginning to settle. While both sides declared partial victories, the ruling has triggered a shift in how mobile developers can operate and how they get paid.

At the heart of this shift is a deceptively simple concept: the D2C web shop. Once an afterthought, it’s now emerging as a lifeline for developers to regain control from the walled gardens of iOS and Android.

The 30% Problem

Apple’s App Store has long operated under a strict regime: any digital transaction made through an iOS app is potentially subject to a 30% commission. For years, developers had accepted this as the cost of doing business in Apple’s tightly curated ecosystem. However, for the gaming industry, that cut represents a significant dent in profits.

Tim Sweeney, Epic’s CEO, was one of the first to say the quiet part out loud. When Fortnite introduced a direct payment option in 2020, which managed to evade the cut, it was promptly removed from the App Store. Epic responded with a federal lawsuit, setting off a chain reaction that is still rippling through the video game industry.

The case was framed as a David-and-Goliath battle, although Epic was hardly a small player. Still, for the thousands of mobile game developers watching from the sidelines, the case became a proxy war over their own economic future.

Source: Xsolla

Legal victory, moral ambiguity

In 2021, a U.S. District Court judge ruled that Apple must allow developers to link to external payment methods – effectively allowing them to build their own web shops outside the App Store’s ecosystem. However, the court also upheld Apple’s right to ban Epic’s developer account and maintain control over its in-app payment system.

Both sides claimed partial wins, but for developers, one takeaway stuck out: Apple couldn’t block links to outside payment options, so long as they adhered to the company’s new guidelines.

That ruling has only recently begun to reshape the mobile gaming landscape, and a quiet revolution is now underway.

Enter the web shop

The web shop, a web-based storefront where users can purchase in-game currency or items, has existed for years. But Apple’s dominance and policies made them more of a workaround than a core business strategy.

That’s now changing. Epic, predictably, led the charge, launching its own Fortnite web shop in 2023, offering V-Bucks and cosmetics at discounted rates for users who purchase directly rather than through the App Store. In doing so, Epic laid the groundwork for a model that other developers are beginning to adopt at scale.

At the forefront of this shift is Xsolla, a global video game commerce company quietly becoming the go-to infrastructure behind many of these D2C web shops. With a suite of white-label tools explicitly tailored for developers, Xsolla enables studios to stand up secure, fully branded web stores in a matter of days – not months.

Xsolla goes beyond being a merchant of record by delivering a complete payments solution – with built-in fraud prevention, tax management, regulatory compliance, and support for over 1,000 local payment methods worldwide. For developers, this means they can tap into global audiences without building bespoke infrastructure – while still owning the player relationship and significantly improving margins.

Importantly, Xsolla’s suite of robust tools is designed with Apple’s guidelines in mind, helping developers navigate the fine print without falling foul of the rules. In recent months, studios ranging from mid-sized indies to global publishers have turned to Xsolla to power their web shops, not just to escape the usual 30% cut, but to future-proof their monetization strategies.

Source: Xsolla

What this means for the mobile gaming economy

For developers, the implications go beyond payments. Web shops enable better customer tracking, higher profit margins, and greater flexibility in pricing and bundling.

This shift could lead to a two-tier economy: casual users who make quick in-app purchases, and more engaged players who are directed to the web for “better deals”. Developers now have the opportunity to build a more direct relationship with their most loyal customers – without Apple sitting in the middle.

Over time, this may erode Apple’s grip on mobile gaming monetisation. Even if only a small fraction of users shift to web shops, the impact on a studio’s bottom line could be significant. It also provides developers with a more stable foundation to scale their business without sacrificing a third of their revenue.

A turning point, but not a free ride

Still, it would be premature to declare victory. Apple’s App Store remains the primary discovery platform for mobile games, and its in-app purchase system is deeply entrenched.

There’s also the looming spectre of further regulation. The European Union’s Digital Markets Act (DMA) and ongoing scrutiny from U.S. lawmakers suggest that the Epic v. Apple case is only the beginning of a broader reckoning over platform power.

For now, Apple is complying with the letter of the ruling, if not the spirit. Links to web shops are allowed, but they’re still wrapped in restrictions that make them less than seamless.

In short, the game has been opened, but the path through it is narrow.

Source: Xsolla

A chance to rebalance the scales

The Epic v. Apple case didn’t break Apple’s App Store power over the industry, but it did crack the facade. And in that crack, opportunity has bloomed.

For mobile game developers long squeezed by platform fees and opaque rules, the rise of web shops offers something rare in the world of tech: leverage. Not complete freedom, but enough of a foothold to push back, reclaim margins, and rebuild more equitable relationships with their players.

In the coming years, success will favour the studios that embrace this hybrid approach, those that master the art of blending in-app convenience with the economic independence of the open web.

Because if the Epic case has proved anything, it’s that even in Apple’s walled garden, cracks can grow.

The post What the Epic v. Apple ruling means for mobile game developers appeared first on Business of Apps.

Every publisher’s journey with Adsterra is unique — some find success overnight, while others refine their strategy through months of testing. This case study follows the latter: two childhood friends from Pakistan who turned a small digital project into a sustainable business.

By leveraging Adsterra’s monetization tools and tailoring their approach to local audiences, they converted steady traffic into a reliable income stream — earning about $2,700 per month. Their story shows how independent publishers can build meaningful online revenue by choosing the right ad network and optimizing strategically.

Publisher fact file

• Names: Mutee Ur Rehman & Hamza Ali
• Location: A village in Pakistan
• Started blogging: 2019
• Big break: Joined Adsterra in 2025 after repeated rejections from other ad networks.
• Result: Earned $2,700 in one month thanks to Adsterra’s easy approval and monetization tools.

“Adsterra gave small publishers like us a real chance.” 

– Mutee Ur Rehman

The turning point

In their monetization journey, the turning point was simple but strategic: the friends concentrated exclusively on low-competition niches and local keywords. This laser focus enabled them to:

• Rank quickly within their regional market
• Attract highly targeted traffic
• Do so with relatively low effort and cost

This case study unpacks their real path — from early failures and dead ends to the specific tactics that ultimately powered their blog growth with Adsterra. If you’ve ever wondered how to break into online revenue when starting from zero, this story offers a concrete roadmap.

Let ‘s begin.

How targeting local niches and keywords unlocked millions of monthly visitors

One of the key drivers of Mutee Ur Rehman and Hamza Ali’s success has been their keyword strategy, centered on low-competition niches and geo-targeted keywords. This approach helped them rank faster and attract steady, high-quality traffic — a crucial advantage for small publishers.

Initially, they targeted Tier-1 countries, but soon realized global competition demanded greater resources. Their breakthrough came after shifting to local search intent, focusing on region-specific queries often ignored by larger sites. This strategic pivot transformed  their blogs from modest traffic sources to millions of monthly visitors.

To execute this strategy, they turned to the Ahrefs Free Keyword Generator, a free tool that helped them:

• Find country-specific keywords: By selecting target regions such as Pakistan, India, or Bangladesh and entering a broad topic, they received keyword suggestions grounded in real local search data.
• Filter by Keyword Difficulty (KD): They prioritized keywords with KD scores under 10 — often marked as Easy — enabling new blogs with limited backlinks to rank quickly.
• Uncover local search intent: Rather than competing for broad global queries, they targeted hyper-relevant local topics, such as city-based services or culturally specific trends. These carry less competition and yield strong CPMs with Adsterra.

Keyword ideas

Source: Adsterra

For instance, instead of targeting a crowded keyword like “best phones 2025”, they focused on “best budget phones in Faisalabad 2025” — a narrower query that ranks faster and still aligns with strong purchase intent.

This localized keyword strategy has been instrumental in driving high-quality traffic that converts effectively through Adsterra’s ad formats, illustrating how niche focus and audience relevance can significantly amplify monetization potential.

How a strategic setup and early promotion helped a new website hit $2,000 with Adsterra

The project started small. In February 2025, Mutee and Hamza purchased a Hostinger Business Plan during a Black Friday deal to test how fast a new social site could rank. As traffic surged, the plan reached its limit — prompting an upgrade to Hostinger Cloud Hosting for around $200, which provided better performance and full maintenance support.

Built on WordPress for flexibility and scalability, the site laid a strong foundation. For beginners, the duo recommends Blogger as a free, simpler alternative.

Once live, they focused on consistency — publishing five locally optimized articles daily and sharing them across friends, local WhatsApp groups, Facebook communities, and niche forums to build early traffic momentum.

Within just 10–15 days, this approach began to pay off. Many articles reached the first page of Google, while several were featured in Google Discover, delivering substantial spikes in traffic.

One site alone, boosted by Google Discover visibility, generated over $2,000 through Adsterra ads, demonstrating how a thoughtful setup, consistent publishing schedule, and grassroots promotion can fuel significant revenue — even without backlinks or high domain authority.

Google Discover visibility

Source: Adsterra

Smart SEO, analytics, and high-performing ad formats: the ultimate revenue growth formula

The technical setup played as big a role in monetization as content and keyword strategy. Built on WordPress and powered by Yoast SEO, the site benefits from Yoast’s automation in generating and submitting sitemaps to Google Search Console, keeping the site optimized and visible with minimal effort.

This focus on SEO efficiency paid off quickly. Google Discover soon became the main traffic source, driving rapid audience growth and, even in the early stages, generating hundreds of dollars in revenue through Adsterra’s fast and reliable monetization system.

But traffic alone wasn’t enough. Choosing the right ad formats played a critical role in maximizing revenue. After testing multiple options, the publishers identified three top-performing Adsterra formats, particularly effective for traffic from Pakistan and similar markets:

Smartlink: Top performer (CPM up to $10)

Source: Adsterra

The standout ad format in Mutee’s and Hamza’s strategy was Adsterra Smartlink, delivering CPMs as high as $10. Smartlink is simple to implement and highly versatile, performing well across Facebook, Telegram, and anchored to website banners, and call-to-action buttons.

Pro tip: The more impressions generated, the higher the CPM — but success depends on integrating the link naturally and offering real user value, rather than spamming.

Popunder ads — CPM: $2–$5

Source: Adsterra

Popunder ads open a new tab behind the current browser window, offering strong fill rates and steady performance. They work particularly well on blogs with longer session times and high user engagement, providing a reliable secondary revenue stream.

Social bar — CPM: $1–$2

Source: Adsterra

Social Bar ads, unique to Adsterra, mimic system notifications, chat prompts, or alerts — ideal for mobile audiences. Their visual appeal and non-intrusive nature make them a strong choice for user-friendly monetization.

Why blog design matters more than you think

Website design isn’t just about aesthetics — it’s a core driver of performance. This publisher duo attributes a significant part of their blogging success to a design philosophy focused on speed, simplicity, and user experience. These elements not only improve engagement but also have a direct impact on SEO rankings and ad revenue.

Clean, fast, and built for growth

Rather than relying on flashy animations or heavy plugins, the friends build websites that are:

• Fast-loading: To meet Core Web Vitals benchmarks and improve rankings.
• Mobile-responsive: Ensuring a seamless experience across devices.
• Easy to navigate: Helping users find content quickly.
• Distraction-free: Avoiding intrusive popups or excessive on-page elements.

This minimalist approach leads to lower bounce rates and longer session times, both of which enhance ad performance and revenue potential.

Themes that power performance

The team relies on two key WordPress themes, chosen for their balance of speed and functionality:

• GeneratePress: A free, lightweight option ideal for beginners looking for a clean, professional blog without compromising load times.
• Kadence Pro: A premium theme offering more layout flexibility, built-in performance features, and robust support — all while maintaining fast performance.

The recommendation is straightforward: always prioritize speed and simplicity over complex designs. Heavy themes or bloated page builders may look impressive, but they slow sites down, and slow websites lose both visitors and rankings, regardless of how strong the content is.

ChatGPT for сontent: process, prompts, and quality controls that work

Mutee Ur Rehman and Hamza Ali credit one practical tool for enabling daily, high-quality publishing at scale: the free tier of ChatGPT. In their case, the model functions as an editorial accelerator — not a replacement for human judgement — enabling faster drafting, better structure, and on-page SEO alignment after a short competitor analysis.

Workflow breakdown

• Competitor audit: Identify top-ranking articles for the target keyword and map coverage gaps.
• Draft in-house: Produce an initial draft that captures unique angles and local context.
• Refine with ChatGPT: Submit the draft to ChatGPT with a focused prompt asking it to improve readability, add missing points, update outdated facts, and check for natural keyword placement and NLP friendliness.
• Manual quality control: Edit for tone, local relevance, factual accuracy, user intent, and to remove generic or repetitive passages produced by the AI.
• On-page SEO and publication: Add images/screenshots, craft meta title/description, format headings and FAQs, then publish on WordPress or Blogger.
• Initial distribution: Share in local groups and niche channels to trigger early traffic.

Content standards followed

• Target length: 800+ words for depth.
• Structure: Strong intro/conclusion, clear subheads, bullet lists or step-by-step sections, and an FAQ that covers related queries.
• SEO: Natural keyword placement, answer user intent comprehensively, avoid keyword stuffing.

4 reasons it does the trick

• Efficiency: ChatGPT speeds up editing and restructuring, shaving hours off each article.
• Gap coverage: The competitor audit + model editing reliably identifies and fills content gaps.
• Quality control: Human review preserves local nuance, accuracy, and voice — preventing generic outputs.
• Scalability: The repeatable workflow supports a high daily publishing cadence without a large team.

Editorial caveat: ChatGPT should not be used as a black-box publisher. Human oversight is essential to make content feel natural.

Why Adsterra became a go-to network for the case

After testing multiple ad networks early in their blogging careers — including Google AdSense — Pakistani publishers Mutee Ur Rehman and Hamza Ali consistently ran into the same issues: strict approval processes, low CPMs, and monetization barriers for smaller sites.

The turning point came when they discovered Adsterra and shifted results dramatically,  earning $1,903 more than before using the same traffic and content strategy only within weeks of integration.

Source: Adsterra

Adsterra’s key differentiators 

• No strict entry barriers: Approval for small or new blogs without high traffic requirements.
• High CPM rates for South Asian traffic: Particularly valuable in regions often undervalued by other networks.
• Diverse ad formats: Options like smartlink, popunder, and social bar enabled testing and optimization for different traffic sources.
• Fast approval and timely payments: Essential for small publishers relying on consistent cash flow.
• 24/7 publisher support: A rare advantage for non-Western markets.

Breakthrough blogging in 2025

In a crowded digital ecosystem, these publishers’ approach focuses on fundamentals that still deliver measurable profits:

• Target low-competition, local keywords to rank faster.
• Use ChatGPT (Free Version) to refine and scale SEO-friendly content.
• Maintain volume and consistency — 3–5 quality articles per day.
• Promote locally through WhatsApp, Facebook, and Telegram groups.
• Monetize with Adsterra Smartlink and Popunder formats for maximum CPMs.
• Skip backlinks early on — let organic traffic mature first.

The result: millions of impressions and steady four-figure monthly income, even from low-cost, entry-level blogs.

Conclusion

This case study underscores how accessibility, adaptability, and persistence can redefine the path to online income. From modest beginnings in a small village to earning thousands per month, Mutee and Hamza’s journey highlights how combining Adsterra’s monetization flexibility with localized content strategy can turn small-scale blogs into sustainable digital assets.

Their advice to new creators: “Start small, stay consistent, think local, and you’ll go global.”

Elevate website profits with Adsterra.

The post Turning a small blog into a $2,700/month business appeared first on Business of Apps.

Mobile app advertising, monetization, and insights company Liftoff is bringing together inspirational women from across the mobile industry to share their stories. In this series, we’ll explore their achievements, the challenges they’ve overcome, and their vision for the future — offering insights from the experts shaping the world of mobile apps and technology.

This month’s article focuses on Cheryl Fowler, Head of Growth Marketing at DigiOutsource.

Can you start by walking us through your career path — how did you get to where you are today?

I began my career at DigiOutsource as a junior web developer, where my main responsibility was turning PSDs into mailers. Driven by a desire for more complex challenges, I expanded into web development for our systems, moved into backend coding, and eventually led the team that built our first mobile casino when mobile started gaining momentum.

From there, I stepped into a technical lead role, which ultimately presented me with a choice: continue down a purely technical path or pivot toward strategy. I chose strategy, but I still kept close to coding. That decision led me to the role of Head of Development, overseeing the teams responsible for all customer-facing applications and services.

Throughout my journey, I’ve always been passionate about the customer. About a year ago, I transitioned into my current role as Head of Growth Marketing, where I bring together my technical background, customer-first mindset, and marketing expertise to drive growth for the business.

What’s the single biggest challenge you face in this role, and how are you building your team and processes to overcome it?

I’d say translating great ideas into strategies that actually go live. In a large organisation with multiple priorities, influencing and aligning on a single direction is often the most difficult part. As I often say, everyone has ideas, but the real challenge is in turning ideas into something tangible that delivers a great customer experience and impacts the bottom line.

To overcome this, I focus on building credibility and trust across the business. That means listening closely, bringing data and customer insights to the table, and showing consistency in delivery. I’ve also been intentional about building a team and adaptable, collaborative, and transparent processes so that when we push a strategy forward, stakeholders feel confident, informed, and invested. Over time, this approach has helped us navigate complexity and ensure that the ideas we champion translate into measurable growth.

What advice would you have for other women in tech looking to take their next step on the career ladder?

Believe in yourself and the impact you can make. Confidence, decisiveness, and credibility are key. When you carry those, people naturally want to know who you are and trust in the value you bring.

It’s not always easy, but clarity on where you want to grow and the determination to push for it will carry you forward. At the same time, stay humble. Listen, learn, and let your results speak for you. Empowerment isn’t something that’s given—it’s something you build by showing up, being consistent, and proving to yourself (and others) that you belong at the table.

If you could go back in time and give yourself one piece of advice at the start of your career, what would it be?

I would tell myself to be patient. The saying “Rome wasn’t built in a day” is absolutely true. Your time will come, and all the effort, learning, and persistence you put in along the way will be worth it.

User acquisition can be expensive. How do you balance the need for high-volume acquisition with the imperative to acquire high-lifetime-value (LTV) users?

It really comes down to balance and vigilance. We constantly weigh ROI, CPA, spend, and LTV against each other, and we know exactly what levers to pull when one of those metrics shifts. High-volume acquisition is important for scale, but we’re equally disciplined about targeting quality users who will deliver long-term value.

That means having the right data infrastructure in place, watching signals early, and being agile enough to adjust spend, creative, or targeting when performance indicators change. Ultimately, it’s about not chasing volume at the expense of sustainability. Every decision has to balance short-term growth with long-term profitability.

The online gambling and gaming industries are heavily regulated, with rules changing frequently. Has it been difficult building a growth strategy that is both effective and compliant?

We’ve always believed regulation is essential. It protects customers and ensures black-market operators don’t have a foothold in the industry. For us, duty of care is our first priority. We want customers to enjoy our platforms for what they’re meant to be: fun and entertaining experiences within healthy boundaries.

Because we’ve held that belief, evolving regulations haven’t been a barrier. They’ve simply reinforced the way we think casinos and sportsbooks should operate. Building a growth strategy that is both effective and compliant isn’t about compromise—it’s about aligning with principles that put customers first while still driving sustainable growth.

The post From developer to Head of Growth: Cheryl Fowler’s journey at DigiOutsource appeared first on Business of Apps.

The SaaS business model remains one of the most attractive ones for founders and investors in 2026. With predictable, recurring revenue and high scalability, a well-built SaaS product can grow as far as your roadmap and market demand allow, so long as you evolve with your customers and keep churn under control.

I’ve helped hundreds of non-technical founders bring SaaS ideas to life at Designli, and one thing is clear: you don’t need to know how to code to build a successful SaaS company. What you do need is a clear process, the right mindset, and a partner who understands both your vision and the long game of product development.

A new era for non-technical founders

No-code tools have completely changed the game. In 2026, non-technical founders can test and validate ideas faster than ever without writing a single line of code. With platforms like Webflow, Lovable, Glide, and Bubble, it’s possible to build proof-of-concept applications that simulate user experience and attract early feedback, even investment.

But make no mistake: these tools are a starting point, not a strategy. You’ll need real development firepower to build a scalable, secure, and differentiated product. That means finding a partner that can make it for the long run, not just hacking together something that “works for now.”

The challenges you’ll face and how to overcome them

It’s not easy to lead a tech startup without a technical background. The biggest hurdle isn’t learning to code, but knowing how to make smart, strategic decisions in an unfamiliar landscape.

Here are some of the most common pitfalls I see:

• Choosing the wrong tech stack: Tools that seem cheap and easy up front can quickly become barriers to scaling.
• Overbuilding too soon: Adding every feature you can imagine is tempting. But building without validation often means wasted time and money.
• Misalignment with your dev team: If your developers don’t understand your business goals, you’ll struggle to prioritize the right things.

The antidote? A digital product that solves a problem with a well-structured MVP, a feedback-driven process, and a partner who speaks tech and business.

Why your MVP is your most strategic asset

Your MVP (Minimum Viable Product) isn’t just version one. It’s the first opportunity to validate your idea in the real world. For non-technical founders, it’s your most valuable learning tool and a powerful way to stay lean.

A strong MVP helps you:

• Test assumptions before heavy investment
• Collect real user feedback
• Pivot quickly if needed

At Designli, we help founders define MVPs through our SolutionLab, a two-week prototyping session that helps founders clarify their goals, map out key features, and create an interactive prototype before writing a single line of code. This isn’t just about design. It’s about business clarity.

Read our article guide with a checklist to build a successful MVP.

Build paths in 2026: no-code, custom code, or both?

Today’s non-technical SaaS founders have three viable paths to product development:

No-code/low-code tools

No-code platforms are fantastic for quickly getting an idea off the ground and on a budget. They let you present something to investors and users without hiring a full engineering team. However, they also have limits, especially regarding customization, performance, and integration with other systems, restricting product scalability.

Custom development

Custom code is the gold standard for scalability, quality, and control. If you’re building a digital product designed to grow, custom code gives you the architecture to support it. It’s a bigger upfront investment that pays off over time.

Hybrid approach

It is often the smartest move to start lean with no code or AI-generated proof of concepts, test your idea, secure early traction or funding, and then transition to custom architecture for long-term stability.

From prototype to production

Once your interactive prototype is complete, our Designli Engine kicks in. It turns your prototype into a real product with custom backend code, clean architecture, a fully owned codebase, no templates, and no lock-ins.

We prioritize:

• Backend scalability and data integrity
• Frontend usability and design consistency
• Rapid iteration cycles with continuous feedback

This is how we ensure your product is launch-ready, scale-ready, and investor-ready from day one.

Hypothesis-driven development approach

At Designli, we use Hypothesis-Driven Development (HDD) to guide our process. It’s a framework that mirrors the scientific method: form a hypothesis, test it through feature delivery, measure impact, and refine based on data.

Meaning we:

• Tie every feature to a business goal
• Use analytics and honest user feedback to validate progress
• Build out a “command center” that lets you track KPIs tied directly to development

Designli Product Owners work alongside founders to ensure every sprint moves toward a measurable outcome. We don’t build for the sake of building; we make to grow your business.

SaaS MVPs built without a technical founder

Grappos: A wine discovery platform born from a founder’s personal frustration. We helped transform a simple idea into a scalable B2B tool used by major wine brands.

AskIris: Built to optimize hospital supply rooms, this voice-enabled inventory management tool now supports multiple devices, including Apple Watch and Zebra scanners.

Cabin time: A mobile app helping families carry camp rituals into daily life. Designed for accessibility, faith-sharing, and ongoing connection post-retreat.

In every case, the founder didn’t code. They partnered smartly and built something real.

What will it cost?

Here’s the truth: launching a successful SaaS product isn’t cheap. But neither is doing it only to rebuild six months later. The smartest spend is the one that matches your build path to your long-term strategy.

For non-technical founders, the good news is there’s more flexibility than ever when it comes to how you bring your product to life:

Key costs to watch out for:

Some costs don’t show up right from the start, but they’ll hit later if you’re not careful:

• Rebuilds due to poor scalability or shortcuts
• Complex integrations lacked early planning
• Technical debt from rushed or undocumented code

Our advice: validate fast, but plan for growth. Our complete guide, which explains the cost and factors to consider when building an MVP, is available here.

2026 begins with a shift toward strategic clarity

Over the past year, early-stage SaaS founders have undergone a clear mindset shift: less chasing trends, more building lean, focused, and validated products.

You can see it in the MVPs being shipped. Instead of proving technical complexity or novelty, the most successful teams are proving something simpler and more important: who the product is for, what problem it solves, and how quickly it delivers value.

More founders are starting with single-feature products, faster validation cycles, and sharper positioning. Optimizing for traction and leaving noise out.
In 2026, traction doesn’t come from hype. It comes from solving real issues, clearly and early. For non-technical founders, that’s good news: it levels the playing field. You don’t need to build everything. You need to build the right thing, and show that it works.

Thinking beyond launch

Launching is only the beginning. A successful SaaS company evolves constantly. That means designing with scalability in mind from day one without overengineering upfront.

You’ll need clean architecture, user feedback loops, strong documentation, and an agile mindset. You’ll also need to monitor the metrics that matter, like churn, adoption, and feature engagement.

Your SaaS can grow into something much bigger than its MVP. That’s what we help founders do every day.

2026 is still your year

There’s never been a better time to start a SaaS company as a non-technical founder. Tools are better. Processes are clearer. And with the proper guidance, you can bring your idea to life faster than ever.

If you’re serious about building something that lasts, not just launches, Designli is ready to help. Schedule your consultation.

Let’s get started.

The post How to start a SaaS company in 2026 as a non-technical founder appeared first on Business of Apps.

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Tech debt and scope creep

Tech debt and bloated features quietly kill speed. Founders must learn what’s slowing their team down and decide what to fix, rebuild, or cut.

Spot the drag: modules, features, and integrations that eat dev time

The first signal is recurring pain. Your dev team is experiencing drag if it constantly encounters the same issues during sprint retrospectives, unstable integrations, hard-to-read legacy modules, or an “almost done” feature that never ships.

Patterns matter. Check your Git logs, Jira boards, or even team Slack channels. Repeated complaints about brittle tests, long QA cycles, or cryptic error messages often trace back to technical debt hotspots. Developer behavior is a tell, too: if they avoid parts of the codebase or call something “fragile,” that’s a red flag.

Don’t ignore feedback loops from support and QA either. Features that generate high bug reports or confusion often cost more than they’re worth. Tools like Code Climate, Sentry, or even a basic analysis of file churn can provide quantitative signals. But some of your best insights come from asking your team: “What parts of the code would you rewrite if you had a clean slate?”

Learn more about how to use feedback loops to decrease user churn.

How to refactor or kill what’s slowing you down

Once you’ve surfaced the problem areas, the next step is decision-making. Every underperforming module should go through a simple triage: if it’s still delivering value to users, keep it; if it’s rarely used or no longer aligns with your core value proposition, consider sunsetting it; and if it’s valuable but creating friction, weigh the pros and cons of refactoring versus rebuilding.
Use effort vs. impact as your guide. Refactoring works when the architecture is solid and the main issues are messy implementations or tech debt (long functions, duplicated logic, poor tests).

Rebuild when the underlying tech is outdated, the component actively blocks scale, or when migrating (monolith to microservices) is part of your roadmap. Don’t let nostalgia keep legacy features alive if they’re holding your roadmap hostage, it’s time to cut.

Whatever you decide, communicate it. Let stakeholders know why cleaning something up accelerates future development. Technical cleanup should be part of your roadmap, not something you “get to later.”

For a broader perspective, this guide explores the leading causes of SaaS teams’ velocity loss.

Create guardrails to prevent new tech debt from creeping in

Fixing tech debt once is good; preventing it from creeping back in is better. Start with clear coding standards. Linting tools, static analysis, and auto-formatters (like ESLint, Prettier, or SonarQube) reduce inconsistency. Make code reviews mandatory, not just to check syntax but also to flag complexity, test coverage, and maintainability.

Then, integrate debt conversations into sprint planning. When proposing features, include a short “complexity check” by asking key questions: Does this introduce new dependencies? Are we adding state to multiple services? Can it be broken down further?

Finally, track code health like a product metric. Monitor churn, test coverage, PR cycle time, and regression frequency. When quality drops, it’s time for a cleanup sprint, not a Band-Aid fix.

Build guardrails that treat clean code and maintainability as core to your business outcomes. Because if your product velocity depends on the health of your codebase, protecting it isn’t optional.

Poorly written JavaScript code example

Source: Designli

What’s wrong:

• Non-descriptive function name (g) and variable names (d, r)
• No comments or documentation
• Inconsistent formatting and spacing
• Logic is embedded in a loop without abstraction
• Weak readability and no error handling

Clean JavaScript code example

Source: Designli

Why it works:

• Descriptive function and variable names: Makes the code self-documenting
• Modular and functional approach: Uses filter and map for clarity
• Scalable and reusable 
• Code comment: Explains purpose and input/output
• Consistent formatting: Easier to read and maintain

Feature prioritization

Not every feature deserves to be built. Focus on what drives retention, revenue, or real user value and say no to the rest.

How to tie outcomes to features, customer value, retention, or revenue

In the earliest stages, startups should treat feature development as an ROI exercise. The question isn’t, “Is this feature cool?” but rather, “Will this feature help us acquire, retain, or monetize users?”

Validate every feature request by asking: Who is this for? What problem does it solve? And how does it move metrics like activation, engagement, or expansion?

To find recurring needs, use qualitative signals like support tickets and user interviews, paired with quantitative indicators like drop-off points, feature usage, and retention cohorts. If a feature doesn’t link to one of your core outcomes, it doesn’t belong on the roadmap.

Prioritization frameworks that bring clarity

When everything feels important, nothing truly is. That’s why startups benefit from simple, transparent prioritization models.

• RICE: Score each feature by reach, impact, confidence, and effort; ideal for product teams that need weighted, data-informed rankings.
• MoSCoW: Sort your backlog into must-, should-, could-, and won’t-haves; faster to run, especially in cross-functional groups.

Both frameworks bring objectivity and reduce friction between founders, developers, and stakeholders. They work best consistently in sprint planning, product reviews, and investor updates, which is the path to spending less time debating and more time shipping.

Kill pet projects to keep focus on what matters

It’s tempting when a founder’s “passion feature,” a designer’s experiment, or a stakeholder’s wishlist item slips onto the sprint board.

To stop them before they start, maintain a transparent, ranked backlog where every idea is tracked publicly and tied to a clear business case or metric. Require a lightweight justification for any new roadmap item: what’s the goal, and what happens if you don’t build it? Finally, set up an “experimental track” where new ideas get a time-boxed window to prove their value. If there’s no traction, they don’t move forward.

The key isn’t to kill creativity, it’s to channel it into solving real problems. Make it a cultural norm that every feature must earn its place.

New product development

New ideas should be treated like scoped, time-boxed, and measured bets. Build fast, validate fast, and kill what doesn’t deliver.

Think like an investor when launching new products

The best founders approach new product ideas with skepticism, discipline, and structure, the same way venture capitalists approach startups.

Before writing a single line of code:

• Define the MVP version that solves one core pain point
• Design a structured budget
• Set a success criterion: a goal that, if hit, validates the idea

Examples of MVP success signals:

• 100 signups from a landing page in 2 weeks
• 25% of beta testers use a feature 3x/week
• User willingness to pay above your current pricing

By setting guardrails upfront, you avoid pouring weeks into a dead-end. If the idea gains traction, scale it. If not, kill it and move on.

Split workstreams to keep core and experimental work from competing

Nothing slows a roadmap faster than mixing new ideas into your sprint cycle. Suddenly, your best engineers are context-switching between urgent bugs, roadmap commitments, and “just one quick prototype.”

Instead, isolate experimental work:

• Assign a separate squad, contractor, or part-time pod to run early-stage builds
• Use lighter tools, such as no-code tools or design prototypes, to explore before committing
• Create an “innovation lane” where small bets run in parallel but don’t block the main product team

Keep your core team focused on shipping what matters now, while a small group explores what might matter next.

Install kill switches to pivot, scale, or stop effectively

The biggest danger with new initiatives isn’t failure, it’s lingering half-successes. These are the kind of features that might be useful, so they stick around, eating time without delivering ROI.

That’s why you need pre-defined kill switches. For example: “If we don’t hit 30% engagement by Week 4, we shut it down.” Other triggers might include failing to get two customers to commit after a demo or not validating the idea within six weeks.

When success metrics are vague, decisions can become emotional. Clear kill switches reduce that ambiguity and protect your team’s focus.

Dedicated product teams

Dedicated teams build better, faster. With full focus on your product, they drive alignment, ownership, and long-term momentum.

Full-time focus on one team, product, and goal

Most agency models and freelance setups run on shared resources. A dedicated product team works solely on your product, builds deep domain knowledge, and eliminates wasted time from constant realignment.

That means they develop deep familiarity with your domain, users, and tech stack. They can anticipate issues instead of reacting to them, and every sprint builds on shared context.
This consistency works over time. With less onboarding and realignment, the team moves faster and builds smarter. You’re not just outsourcing tasks, you’re embedding long-term partners who think strategically about your product’s future.

Eliminate context switching and boost developer velocity

Multitasking may look efficient on paper, but it’s a silent killer in development. When developers switch between codebases or clients, they incur a switching cost, the mental overhead of reloading architecture, business logic, and user intent. This context switching can consume up to 40% of a developer’s time.

A dedicated team eliminates that waste. Developers stay immersed in your product, reducing bugs, improving build quality, and spotting optimizations opportunities through deep focus.

For early-stage founders, this focus translates into shorter release cycles, more predictable delivery, and cleaner architecture that’s easier to maintain and scale. You’re not just saving time. You’re multiplying impact.

From coders to collaborators

Great products aren’t built by people who just follow instructions. They’re built by teams who understand the “why” behind every feature.

With full context and long-term investment, developers evolve from executors to collaborators, surfacing edge cases, recommending architectural improvements, and flagging tech debt before it becomes a liability.

This shift in mindset is critical. A dedicated team feels ownership over your product’s success. They care about UX. They speak up in retros. They help product managers and founders make better tradeoffs. And because they’re invested in the long game, they optimize for maintainability and scale, not just getting tickets done.

If you are ready to launch a product effectively through a well-structured process, Designli is ready to help. Schedule your consultation, and let’s get started.

The post How proactive SaaS companies actually ship faster appeared first on Business of Apps.

The biggest season of the year is here for media buyers. Budgets are bigger, consumer intent is higher and competition is at it’s peak. In 2025, shifts in verticals and traffic sources are opening fresh opportunities for brands and publishers who know where to look.

Want to know where to look and how to get ahead this Q4? At Yep Ads, we believe the best insights come from being in the trenches every day with media buyers, affiliates and global brands. During a recent quarterly business review, our team compared market trends across regions and verticals, building a list of firsthand observations on what’s really happening as we head into the busiest season of the year.

Q4 is running from October 1 through December 31 and always brings a unique rhythm:

• October kicks things off with a back-to-school wrap-up, early holiday promotions and a spike in cybersecurity demand.
• November is dominated by Black Friday, Cyber Monday and a surge in e-commerce and gift campaigns.
• December closes the quarter with peak holiday shopping and big end-of-year sales pushes.

So today, we’re pulling back the curtain and sharing a few secrets from our internal playbook: which verticals are scaling fastest, which traffic sources are delivering and how to position yourself for the biggest wins this quarter.

Traffic source trends

Source: Yep Ads

Affiliate traffic source trends

Search: Capturing high-intent buyers

Search traffic is the workhorse of Q4 performance marketing. Whether it’s “best Black Friday VPN deal” or “last-minute Christmas gifts under $50”, buyers are literally telling you what they want.

Why it works in Q4:

• Purchase-ready intent: Shoppers are past browsing and ready to click “buy”.
• Seasonal long-tails (e.g., “Cyber Monday smartwatch deals”) drive cheaper conversions than generic terms.
• Extensions and promos boost visibility, perfect for e-commerce flash sales or insurance offers.

Best for: Cybersecurity (VPN/antivirus), e-commerce, home warranty and finance.

Facebook and Instagram

No surprise here, Facebook and Instagram are still the go-to channels for Q4 e-commerce and gift campaigns. Shoppers scroll through feeds looking for deals, inspiration and last-minute buys and Meta’s algorithms are laser-focused on conversions.

Why it works in Q4:

• Visual storytelling (video + carousel ads) makes it easy to showcase product bundles, gift guides or limited-time offers.
• Advanced retargeting lets you re-engage cart abandoners during Black Friday / Cyber Monday surges.
• Massive audience size ensures scale, even when CPMs rise.

Best for: E-commerce, gifts, health & beauty and streaming subscriptions.

Pinterest: Inspiration meets intent

Pinterest becomes a holiday powerhouse in Q4. Users come here specifically to plan purchases, whether it’s “gift ideas for mom” or “holiday home décor” – That mix of browsing + shopping intent is a goldmine for affiliates.

Why it works in Q4:

• People use Pinterest as a holiday mood board, meaning they’re actively looking to save and shop ideas.
• Visual pins keep offers evergreen, great for gift bundles and lifestyle products.
• Strong SEO within Pinterest drives ongoing traffic from seasonal searches.

Best for: Gifts, home goods, wellness and e-commerce bundles.

Trending Q4 2025 verticals

Source: Yep Ads

Trending Q4 2025 verticals to watch for

Home insurance and warranty expansion

Economic uncertainty has made people think harder about protecting their homes and big-ticket purchases. With higher repair and replacement costs, insurance and warranty offers are gaining ground as people choose safety nets over risk.

Why it’s big: Beyond economics, seasonal behaviour in Q4 also drives demand. The holidays mean more celebrations, fireworks, cooking and guests coming over, all of which naturally get people thinking about how secure their homes and belongings really are. When households are at their busiest, the idea of protection feels even more relevant.

What to focus on: For affiliates, this vertical shines when paired with content-driven funnels (reviews, comparisons, “top warranty plans”) combined with pay-per-call strategies. High-intent users researching coverage are more likely to convert after a real conversation, making hybrid content + call flows extremely effective.

Gifts, gadgets, and seasonal shopping

Q4 is gift season, plain and simple. This year, campaigns are hitting hard in November, not just December. Smart gadgets, health & beauty bundles, and lifestyle gifts are leading the charge, as consumers stock up earlier to avoid last-minute stress.

Why it’s big: Gifting campaigns benefit from emotional triggers. People aren’t just buying for themselves, they’re buying for others which amplifies urgency and spend. Social proof also matters more: unboxing videos and holiday gift guides drive discovery.

What to focus on: Affiliates should experiment with UGC-driven creatives on Instagram and Pinterest, blending lifestyle imagery with gifting themes. Native placements in holiday gift lists and influencer tie-ins can push these campaigns to the next level. Timing is everything; start in early November to capture intent before inboxes get oversaturated.

Cybersecurity and back-to-school growth

Cybersecurity has become a seasonal driver in Q4 thanks to holiday travel, device purchases and back-to-school cycles. VPNs and antivirus products are seeing double-digit growth compared to Q3 and that momentum is expected to climb as scams and data breaches spike during the holidays.

Why it’s big: Parents are buying new laptops for kids, professionals need security while travelling and consumers are simply more aware of online risks. These products solve an immediate pain point that peaks this time of year.

What to focus on: Affiliates should shift messaging from “generic protection” to lifestyle-relevant safe gaming for students, private browsing for families and streaming access while abroad. Push and in-app ads are performing especially well when they tie into everyday scenarios.

eCommerce and straight aale surges

E-commerce always dominates Q4 but this year, direct sale offers are scaling harder than ever. With Black Friday, Cyber Monday and holiday shopping in full swing, consumers are looking for fast, frictionless buying experiences, bundle purchases, limited-time deals and bundled discounts.

Why it’s big: The combination of urgency, trust and value is irresistible for shoppers who don’t want to waste time comparing. They just want to click, pay and know their gifts will arrive on time.

What to focus on: Affiliates and media buyers can lean into promotion periods and time-based mentions. Retargeting warm audiences with push and native ads can deliver strong conversion lifts, especially during high-intent shopping weeks.

Noteworthy industry updates

One thing we see every year is partner onboarding freezes. As soon as Q4 is in full swing, top-tier publishers and affiliates often close their doors to new brand partnerships. The focus shifts to maximizing revenue with existing deals during peak season. If you haven’t secured your spots yet, this is the time to move. Lock in competitive rates and finalize agreements before the freeze so partners know exactly what they’re working with.

Another essential step is to plan your promos early. Black Friday, Cyber Monday and holiday campaigns can’t be pulled together overnight. Affiliates want calendars, codes and creative assets in advance so they can align content and traffic flows. The brands that prepare early give affiliates the runway they need to launch at full speed when it matters most.

The takeaway? Q4 isn’t just about trends; it’s about being proactive. Partner smart and partner with Yep Ads. We’ve got the tools, channels and expertise to help you not only keep up with the season but get ahead of it.

Why work with a digital marketing agency

Source: Yep Ads

Yep Ads: Driving the future of performance marketing

For over 15 years, Yep Ads has connected 6.5K+ brands and publisher partners across 140+ countries, generating over 4.3 billion clicks annually. Our focus spans key verticals, from E-Commerce and Health & Wellness to Lead-Generation, Cybersecurity and beyond.

What makes us different?

• A holistic, end-to-end advertiser solution.
• Proprietary traffic channels and first-party call centers.
• Advanced targeting powered by AI and an in-house creative team.
• Brand-safe, quality-assured campaigns.

This approach has helped us become the leading performance marketing agency trusted by global innovators.

Final thoughts: Preparing for Q4 and beyond

Q4 is about more than holiday peaks; it’s about strategic positioning for the year ahead. Brands that embrace omni-channel execution, align with trending verticals and partner with the right agencies will be best positioned to succeed.

And as we head into the biggest quarter of the year, we want to wish everyone in the industry a great season ahead. May your campaigns scale, your funnels convert and your wins keep stacking up.

Ready to Grow With Yep Ads?

The post The trends defining Q4 digital marketing appeared first on Business of Apps.

When people think about artificial intelligence, they often picture chatbots spinning out text, image generators creating art, or voice assistants fielding questions. In all these cases, AI is working with unstructured data: words, pixels, sounds. Messy inputs, human in origin, and often evaluated qualitatively rather than quantitatively.

But there’s another side of AI that rarely gets airtime: the side that deals with structured data. At Kayzen, we live in this world. Programmatic advertising is built on structured data at massive scale. It’s this reality that led us to build kAI, the first AI agent for programmatic in-app supply, an entirely different challenge compared to the AI hype most people are used to.

kAI – Kayzen’s AI agent for programmatic in-app supply (1)

Source: Kayzen

In this article, I’ll unpack why structured data is both an advantage and a constraint, what unique challenges arise when building AI agents on top of it, and how we’ve approached those challenges at Kayzen.

Structured vs unstructured data: A primer

AI doesn’t care whether it’s processing language, numbers, or images, it’s all just math in the end. But the form of the data dramatically shapes the kind of problems AI can solve.

• Unstructured data: Think text, images, audio, video. Inputs are inconsistent and messy, but the models (LLMs, diffusion, transformers) are built to handle fuzziness. Answers are probabilistic and subjective.
• Structured data: Think databases, bid requests, logs. Inputs are standardized, schema-driven, and numerical. Answers should be exact, or at least within an accurate range.
  

Structured vs unstructured data

Source: Kayzen

In programmatic, structured data is the air we breathe. A single DSP can process more than 3 million bid requests per second, each carrying a payload of device IDs, geo information, app metadata, placement signals, OS versions, bid floors, and more. On top of that, every advertiser campaign comes with its own structured layer: targeting rules, creative sets, KPIs, bid strategies.

If unstructured AI is like sculpting out of clay, structured AI is like assembling a jet engine. The parts have to fit. If they don’t, the engine doesn’t just look weird, it fails.

The promise of AI in programmatic

Programmatic advertising is a perfect candidate for AI because of three dynamics:

• Data abundance: No shortage of input signals.
• Decision density: Every millisecond a bid decision is made, often with incomplete information.
• Human limitations: No person or team can parse this volume of structured data in real time.
  

That’s where kAI comes in. As the industry’s first programmatic in-app supply AI agent, it helps advertisers and marketers navigate this structured data universe. Instead of staring at dashboards or manually pulling reports, users can ask kAI direct questions about inventory availability, unique user reach, and market prices.

kAI – Kayzen’s AI agent for programmatic in-app supply (2)

Source: Kayzen

But building kAI wasn’t simply a matter of plugging ChatGPT into our data stack. We learned the hard way that structured data plays by different rules.

Challenge 1: Structured data isn’t always “clean”

In theory, structured data is tidy: rows, columns, schemas. In practice, it’s messy. Programmatic data streams often contain:

• Sparse fields (not every signal is always passed).
• Inconsistent labeling (app metadata differs across exchanges).
• Latency gaps (data pipelines may lag or drop).

An AI agent trained naively on structured data risks amplifying these inconsistencies. Ask for “highest reaching placements,” and you might get a biased view if half the exchanges didn’t populate the placement field consistently.

To mitigate this, we had to build normalization layers that sit between raw data and kAI. This isn’t glamorous work, but without it, the AI agent becomes unreliable. Structured data gives you the illusion of order, until you look closely.

Challenge 2: Focusing on what really matters

One of the paradoxes of programmatic is that abundance can be a liability. Every bid request carries dozens of signals, and when multiplied by millions per second, the sheer weight of data can overwhelm both humans and machines.

The reality is that not every signal is equally useful. Some dimensions drive outcomes; others are noise. The danger in training an AI agent on raw structured data is that it will happily process everything, but not all of it contributes meaningfully to decision-making. Worse, irrelevant signals can skew outputs or introduce inconsistencies that take time to debug.

This is where restraint matters. With kAI, we learned to start small,  prioritizing the core dimensions and metrics that matter most to programmatic marketers: ad format, OS, Geo, Publisher App, request volume, unique users, price, etc; before expanding outward into secondary signals.

Focusing early on the essentials has two advantages:

• Accuracy is easier to validate: You can benchmark against known truths in key KPIs.
• Testing becomes manageable: Instead of validating across dozens of signals, you establish confidence in a smaller set, then scale.
  

Building an AI agent on structured data isn’t about feeding it everything. It’s about designing a hierarchy of relevance, what signals actually drive value for the end-user, and which ones can wait until later iterations.

Challenge 3: Hallucinations are harder to spot

Anyone who has played with an LLM knows it hallucinates, confidently spitting out wrong answers. In unstructured settings (like writing copy), this may be tolerable. In structured settings, that eagerness to please the user with an answer, is dangerous.

Early in kAI’s development, if you asked “How many unique daily app users are there in the UK?” it might tell you 30 million. The uninitiated might have taken this question as correct, but the  real figure, validated against internal data, was closer to 50–60 million.

The problem wasn’t that the model was lazy. It was that structured data has ground truth. Numbers should add up. Percentages should reconcile. An LLM that smooths over uncertainty with a guess isn’t “creative”, it’s plainly wrong.

We solved this through iterative testing and guardrails. That meant:

• Validating answers against known benchmarks.
• Injecting domain-specific constraints into the system.
• Stress-testing across different user personas, from someone very familiar with programmatic supply data like myself, down to the junior trader taking its first steps into programmatic buying.

Bottomline, when AI meets structured data, you can’t just trust outputs. You need to design systems where wrong answers surface quickly and get corrected.

Challenge 4: Balancing accessibility with depth

One of the reasons walled gardens like Meta and Google have been so successful with AI is accessibility. Their tools don’t overwhelm users with data, but rather they abstract complexity away.

Programmatic is a bit different. The ecosystem prides itself on customization and transparency. Marketers want to see levers, controls, and data slices. The challenge is: how do you make an AI agent accessible without flattening programmatic into a black box?

Our approach with kAI has been to strike a balance:

• Include Pre-Prompts to showcase the type of questions kAI can help you with.
• Train the AI agent to ask validating questions to ensure it provides an accurate answer to the user prompt.
• Ensure varying degrees of complexity on the answer, from plain, natural language answers down to graphs and tables with multiple dimensions and metrics.
• Design a follow up logic that incentivizes users to dig deeper and break down the data further, if desired.
• Build 2 versions of kAI, one open and free to the whole industry, and kAI Pro, our enhanced version for Kayzen customers with increased data granularity and capabilities.
  

kAI is not about establishing a dichotomy between humans and AI, but rather supercharging marketers with accessible, deep insights to make informed decisions with the speed and accuracy that programmatic marketing demands.

Lessons for anyone building AI on structured data

If I had to distill our journey with kAI into a few lessons for others working with structured data, they’d be:

• Don’t assume structured = clean: Build normalization and validation layers.
• Start with what actually matters: Start small with the core dimensions and metric that matter to your users, and build from there.
• Guardrail aggressively: Hallucinations matter more when numbers have to add up.
• Balance UX with transparency: Accessibility shouldn’t mean black-boxing.

Why this matters beyond programmatic

Programmatic advertising might be a niche compared to text generation or image synthesis, but the lessons here apply broadly. Any industry that runs on structured data, finance, logistics, healthcare, faces the same challenges.

The hype cycle around AI has been dominated by unstructured use cases because they’re flashy and visible. I mean, I Ghibli’d the hell out of my family pictures. But the real economic impact of AI may be greatest in structured-data domains, where decisions are high-volume, high-stakes, and measurable.

Getting it right isn’t just about training bigger models. It’s about respecting the structure of the data, building systems that can harness it without distortion, and designing AI agents that integrate with how humans actually work.

That’s the journey we’re on with kAI. And we are just getting started.

The post Lessons from building AI agents on structured data in programmatic advertising appeared first on Business of Apps.

Sports-betting is evolving at breakneck speed, driven by shifting regulations, rapid tech innovation, and a surge of new entrants competing for the same passionate audiences. Across the globe, new regulations are opening doors while competition is intensifying. Every week, new apps enter the arena, each promising the best odds, the smoothest UX, and the biggest wins. But as seasoned performance marketers know, success in this space is less about who shouts loudest and more about who reaches the right user, at the right moment, with the right offer.

That is where strategy becomes the real game-changer. Sports-betting audiences are diverse, highly segmented, and motivated by both passion and opportunity. Understanding what drives each segment, and aligning media and creative accordingly, is what separates campaigns that just spend from those that truly scale.

Promo codes as a proven play

In a crowded market, promo codes still carry enormous weight. They offer a clear incentive and a seamless way to measure channel performance. By tailoring codes to specific audiences or partners, marketers can see exactly where conversions come from and double down on what works. The most effective brands build entire seasonal campaigns around exclusive codes, making them part of the storytelling rather than just an add-on.

Localization makes it personal

Sports is local at its core. While the Champions League may attract a global audience, it’s the hometown rivalries, local leagues, and national teams that stir the strongest emotions. Localizing ads, from language and cultural nuances to regulations and favorite sports, boosts relevance instantly. Whether it’s featuring cricket odds in India or NFL match-ups in the US, local context turns generic ads into personal invitations.

Gender targeting to reflect different motivations

Data shows that betting behavior often differs between male and female audiences. Men may be more responsive to competitive odds and event-driven promotions, while women may prefer safer betting options, tips, or features that emphasize community and shared experiences. Recognizing these nuances and crafting gender-specific creative helps to connect more authentically and improve conversion rates.

Hyper targeting to reach the highest-value users

Sports-betting audiences aren’t just “sports fans”, they are fantasy football enthusiasts, live-betting thrill-seekers, or die-hard supporters of a single team. Programmatic platforms and app-based data make it possible to target these niches with laser precision. Combining behavior, location, device data, and even proximity to sporting venues ensures that every impression has a higher chance of turning into a valuable user.

Heavy up around tentpole events

Every sport has its moments that command attention: the World Cup, the Super Bowl, Wimbledon, the Grand National. User intent surges in the run-up to these events, creating a natural window to scale budgets, test new creatives, and release limited-time offers. The key is to plan months ahead so you’re not scrambling to catch the wave but positioned to ride it from the first whistle.

Beyond the core plays

Alongside these pillars, high-performing sports-betting brands often invest in content marketing, app-store optimization, and gamification. Match previews, betting guides, and player interviews not only attract organic traffic but also build trust. Loyalty programs and achievement badges encourage repeat visits long after the first deposit.

Playing the long game

Ultimately, sports-betting marketing is about more than spikes in installs. It’s about sustained engagement, compliance with evolving regulations, and building a brand that users trust enough to return to week after week. For marketers, the challenge(and the opportunity) lies in creating strategies that balance creativity with accountability, local insight with global reach, and excitement with responsibility.

In such a competitive field, the winners will be those who treat marketing not as a one-off push, but as a carefully planned season: every campaign, every channel, every piece of creative working together towards one outcome: growth that lasts.

Let’s collaborate to make your next sports-betting campaign not only eye-catching, but precision-focused, resilient, and growth-boosting.

The post The winning formula for sports-betting app marketing appeared first on Business of Apps.

Health & fitness is one of the most challenging and competitive verticals in the mobile industry. According to the latest Health & Fitness App Report 2025, the market for health & fitness apps continues to grow. However, with market growth, competition also increases. Apps in this category often use mixed trial strategies, which require extra effort to attract and retain users. It’s crucial to quickly prove the product’s value to recover the cost of acquisition.

CPA breakdown by each funnel step in %

Source: AdQuantum

One of the most effective approaches in the Health & Fitness vertical today is web-to-web campaigns, where users complete a quiz on the web. After the initial onboarding, they proceed to payment or registration. This approach captures more user data, builds psychological commitment, and boosts LTV.

The success of these campaigns comes from a clear system of three components: the pixel, the creatives, and the funnel itself.

The formula for success

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Source: AdQuantum

Our approach to web-to-web: Three key factors for success

Many companies struggle with high CPA when running web-to-web, but the problem usually isn’t the traffic channel. More often, it’s that a crucial component of the system is underdeveloped.

When we launch a campaign, our first step is to figure out which of these three areas (pixel, creatives, or funnel) is the weakest. That way, we can focus on fixing the biggest bottleneck while also improving the other parts.

• The pixel is crucial. In some projects, it learns quickly and starts performing well after just $2,000-10,000 in spend. However, typically, these first investments go into learning, finding, and filtering the right audience.
• Creatives typically have the most significant impact on CTR and early funnel conversions. Strong creatives immediately drive more engagement and help lower acquisition costs.
• Then, there’s the funnel, which is especially important for the progression to the second screen of the quiz.

Our approach is built on constant testing and improvement across all three areas. We work on developing high-performing creatives, building efficient funnels, and training the pixel so that it learns to find and bring in the right users, the ones most likely to convert and pay.

The goal of the campaign and the main challenge

Our client for this success case was the Health & Fitness app. They wanted to grow fast but faced a common issue: a high CPA with web-to-web campaigns, making scaling unprofitable.

The main goal of the campaign was to achieve stable unit economics and acquire paying users. The campaign was run on Meta.

Step by step: What was done

Launch tests for funnels, paywalls, and creatives & begin training the pixel

From the start, the campaign’s goal was to build one strong funnel and test it with three different pricing options. We chose the three pricing models that showed the best potential, began training the pixel, and continued with further tests.

A key metric in this case was the progression to the second screen of the quiz. This is typically the step where many users drop off. The benchmark for this stage is a score of at least 60%. If it drops to 40%, scaling the campaign becomes nearly impossible.

We reached the required 60% right at the beginning, so we set this moment as our starting point, marking CPA as 100%. This allowed us to track future improvements along with other key metrics, such as click-to-paywall, paywall-to-purchase, paywall-to-checkout, and checkout-to-purchase.

After achieving this benchmark, our next step was to finalize the best pricing model, which became the basis for the next stage of testing and scaling the campaign.

Funnel and creative optimization

After that, we tested several funnels and picked the one that performed best. Then we defined the main creative direction and let Facebook optimize which ads to show more often. At the same time, we continued to collect pixel events and set up the initial audiences.

During this process, we discovered that the best CPAs came from two groups: women under 45 and men aged 45 and older. So, we built separate funnels specifically for these segments.

We launched separate campaigns for men and women and also split them by age, targeting younger and older women as well as younger and older men. We also started actively using auto-rules to automate budget management as we scaled the campaigns.

Audience-based funnels + creatives

Source: AdQuantum

Advanced сreative strategy

At this stage, we found two top-performing creatives that were designed for a broad audience.

For fitness ads, the four key elements that matter are:

• The result
• The easy way to achieve that result
• Expertise
• Social proof

It’s essential not to combine all four of these elements into one creative. It can overwhelm people. Instead, we mix them across different ads, making sure each one focuses on no more than three of these points.

A big goal for us was to find the proper wording — CTAs and key phrases — that would click with the target audience and drive conversions. At the same time, we wanted to lock in a consistent visual style and color scheme. Testing showed that shades of red, brown, and yellow performed best, so we decided to use that palette for all future creatives.

During this period, static creatives started working better than videos, which began to underperform. We quickly picked up on this trend and shifted to static formats.

Scaling the campaign and switching to ABO

Then, we gathered more top-performing creatives and gradually scaled our campaigns. However, we faced an issue: campaigns using ASC (Advantage+ Shopping Campaigns) and CBO (Campaign Budget Optimization) were unstable, and scaling with AAA(Advantage+ App Campaigns) was ineffective, with unpredictable results.

We decided to switch to ABO(Ad Set Budget Optimization), where each ad set had only one creative, and the budget was controlled at the ad set level. We started by launching one creative per campaign with small budgets and used auto-rules to scale up.

Over time, the best creatives collected more data and were duplicated, while the underperforming ones were automatically paused.

We set up detailed auto-rules to maintain this optimization: turning off ad sets up to $400, turning off ad sets above $400, and mirroring these rules for turning them back on. Additionally, we implemented rules to re-enable campaigns at midnight (account time) for the last 3 days if they were still within KPI but had been stopped the day before.

Campaigns were also paused and restarted strategically during hours when the US was asleep, since spend analysis over time showed minimal incoming results during that window. For scaling, we took a balanced approach, typically increasing budgets by 50% when performance was well ahead of targets, avoiding overly aggressive jumps.

Auto-rules played a significant role in keeping the campaign structure efficient and scalable, helping us reduce acquisition costs to 32.39% of the original level.

Funnel update and checkout improvements

At this point, we rolled out the most significant update to the funnel – we replaced four separate funnels (previously set up for two female and two male age groups) with one dynamic funnel that could adapt to different segments. We also prioritized Apple Pay at checkout, which made the payment process faster and smoother.

New creative concepts and stable growth

In the final phase, we focused on developing new creative ideas and continued to grow the acquisition volume in a more controlled way, keeping CPA stable at around 30.77%.  At this point, the CPA was slightly higher compared to the previous stage, primarily as a result of more aggressive scaling efforts.

At this stage, the main challenge was to keep results predictable and steady while increasing spending. It became clear how important it is to monitor your core metrics closely, as they show whether your pixel, creatives, or funnel is the weak link. This stage highlighted again that minor adjustments to the funnel can significantly boost overall performance.

Results

By the end of the project, we had reduced CPA by over 70%, achieved strong funnel progression rates, and built a structure that could scale reliably.

The drivers of success weren’t just great creatives or a good funnel alone. It was the combined effect of three things working together: a properly trained pixel, well-tested creatives, and a carefully optimized funnel.

The post How AdQuantum lowered CPA by 70% for a health & fitness app with web-to-web funnels appeared first on Business of Apps.

Mobile app advertising, monetization, and insights company Liftoff is bringing together inspirational women from across the mobile industry to share their stories. In this series, we’ll explore their achievements, the challenges they’ve overcome, and their vision for the future — offering insights from the experts shaping the world of mobile apps and technology.

This month’s article focuses on Catherine Beattie, Director of Programmatic at WeatherBug.

Can you start by walking us through your career path — how did you get to where you are today?

My first job out of college was in consulting, which I did for six years, ultimately focusing on project management consulting. After tiring of the travel involved in consulting, I found a Chicago-based role at Tribune Publishing where I was a Project Lead.

The first project I did there was an order management system implementation for their digital advertising operations team, which was how I first learned about digital ad operations. Once the project was completed, the main project stakeholder pulled me onto her AdOps team (shout out to Barb Healy for setting me on this path!). I ended up supporting their direct sales teams across the country for nine years at Tribune.

Then, an opportunity to move from supporting direct sales to programmatic came up with Encyclopaedia Britannica/Merriam-Webster. I worked there for six years, monetizing the company’s websites and mobile apps. About two years ago, WeatherBug reached out to inquire whether I’d be interested in further exploring mobile app programmatic strategy, and I jumped at the opportunity.

How would you describe your role at WeatherBug?

I’m the Head of Programmatic Revenue at our company, with programmatic accounting for over 80% of our annual revenue target from all sources. Our main focus is on iOS and Android mobile apps, which make up approximately 93% of our traffic, but we also support the WeatherBug website.

In your own words, what is WeatherBug, and what makes it unique?

WeatherBug is a leading weather consumer brand with over 20 million users. We have extensive weather observation networks and unique severe weather detection (check out our Spark lightning alerts!). Our mission is to provide environmental intelligence for all aspects of people’s lives.

Our programmatic team consists of one other team member and me, while the whole company is only about 50 people. I am regularly impressed by how much we get done and what we offer our consumers with such a lean team.

What inspired you to pursue a career in the app/mobile industry?

Over the last 24 years, it has felt organic to move from company to company. Each change came with an opportunity to expand my skillset and influence, from project management to direct sales, AdOps to programmatic web to programmatic in-app. I was always working for brands with content I believe in—from the Tribune’s journalism, Britannica & M-W’s reference information, or WeatherBug’s insights. I was always working with managers and team members whom I respect and enjoyed collaborating with.

What challenges have you faced in your career, and how have you navigated them?

I was laid off from my first job early in my consulting career. I did start a new role with another consulting company a couple of months later, but it was a formative experience early on in my professional life. It gave me empathy for others in the same position, and I have actively tried to help wherever I can: mock interview practice, resume reviews, mentoring programs, making introductions between people in my network, and the like.

I have made a conscious effort to maintain a great network of current colleagues, former colleagues, industry friends, and others. Making and keeping connections is something I find genuinely fulfilling.

What misconceptions do people have about programmatic advertising?

There can be a misconception about programmatic that it is “set it and forget it”. Once you’re in the weeds yourself, you’ll know that’s not the case!

I take a very active approach to managing our 37-plus programmatic demand partners. I pull monthly Share of Voice reporting, showing each partner’s portion of the pie and how that is changing month over month. It’s a helpful indicator for identifying drop-offs. We can then contact our partners to ensure there isn’t a technical issue at play. If they’re becoming less competitive in the stack, flagging for all parties is worthwhile.

I also created a Quarterly Partner Evaluation Matrix, where I stack rank all of our programmatic partners, applying weight-based criteria for several categories. For example, spend, timeouts, reporting automation and ease, iOS participation, responsive customer service, and healthy eCPMs. We typically give new partners that we onboard about three months to hit their stride. Every quarter, I may have a partner or two that fall into what we call the “Danger Zone” (yep, a Top Gun reference!), and then we have a conversation with those partners about how to turn things around.

Sometimes partners can’t hit the mark, even if they’ve been live for a while, and we deactivate them from our stack. This regular monitoring and “pruning” helps us optimize our programmatic revenue and keep things running smoothly.

How has working at WeatherBug shaped your perspective on mobile marketing?

Something that has been great to be a part of at WeatherBug is how directly we communicate with our consumers. The mission of our app is to keep people informed about current weather conditions and changing weather that might impact them.

Our marketing team diligently crafts useful and helpful push notifications and ensures that our severe weather alerts reach people in a timely fashion. The connection with your user base feels closer on mobile than it does on web, and WeatherBug treats that with significant respect.

What’s a career milestone or achievement you’re particularly proud of?

From a very specific app monetization point of view, I am proud of our 10% YOY growth from 2023 to 2024, largely driven by programmatic revenue gains. We want to continue building on that success and have ambitious goals for 2025. Q1 was a start in the right direction.

From a broader career history point of view, seeing team members or mentees grow in their careers, even when it means leaving my team, is exciting. I love seeing where people end up and succeed. I always tell them (whether they’re leaving, or I’m leaving) that they’ll never be rid of me now—we’re always going to be connected!

How do you stay up to date with changes in the industry?

I’m sure I get the same industry newsletters everyone else does — Beeler.Tech, AdExchanger, AdMonsters, Gareth Hates Ad Tech, Matthew Goldstein (msg), etc. The Beeler.Tech Community on Slack is a huge help to me. It is comprised of both ad tech partners and publishers looking to solve the challenges in our digital world. The Women in Programmatic Network is also an excellent resource for women in the industry.

Attending Beeler.Tech’s Base Camp and Navigator events are always super helpful, I come back with at least 20 pages of notes to digest afterwards. The Sell Side Summits hosted by AdMonsters, formerly the PubForums, have historically been a great place to connect with publisher peers and share wins and struggles. In the Chicago area, where I’m located, we have a monthly industry happy hour, coordinated by Sarah Wagner (Optimism) and Connie Walsh (Legacy.com), which is a great opportunity to meet new people in the industry and share stories.

The relationships I’ve formed with peers in the industry are invaluable. If I can recommend anything, it’s to cultivate those. Even if you can’t attend big events due to budget constraints or a pecking order at your company, get involved in the Slack communities, share your insights, and ask your questions.

What are the biggest trends in mobile programmatic advertising that you believe will have a major impact in the future?

I think all of us app publishers are waiting to see what will happen with the Android Privacy changes. We all took the hit when Apple rolled out ATT for iOS, and we’ve seen what happened to CPMs as a result. I’m personally hoping for messaging that is more positive and potentially open to customization.

What are some common barriers women still face in this space?

Women belong in every space and elevate the value in every space. I see women excel in operational roles time and again. If there is a barrier to entry, those of us in our roles now (men and women) should address it by actively seeking out and lifting women of potential into these digital roles.

How has the industry evolved during your career?

When I started in Digital Ad Ops, we called it “remnant” advertising, so programmatic has certainly come a long way since then! Header bidding has probably been the most significant development. I still remember standing in Lori Tavoularis’ office at the Tribune, where she enthusiastically drew a picture on her whiteboard detailing the new real-time header bidding auction workflow after she incorporated Index Exchange.

What’s the best piece of career advice you’ve received as a woman in this industry?

I don’t recall where I’ve picked all of these up along the way, but a few things ring true to me. Ask questions if you have them. Have the confidence to believe that if you are wondering, someone else probably is, too. And even if they’re not, get your answers! Lift people up with you: life is not a competition, we’re all in it together. Stop apologizing for things that aren’t your fault or don’t need an apology. Finally, say yes to opportunities without feeling entirely ready—you’ll get there.

What initiatives would help create more opportunities for women in mobile?

Mentoring programs really help women, and they’re even more successful when men also participate in those mentor roles. WeatherBug has a corporate mentorship program I’ve been involved in, but there are also options outside your company. Beeler.Tech is one industry group that has run a mentorship program for years, and I have thoroughly enjoyed being a mentor for several people through their program.

The post Catherine Beattie on forecasting for success as Director of Programmatic at WeatherBug appeared first on Business of Apps.

The post The new economics of mobile ad personalization appeared first on Business of Apps.

As AIGC technology continues to reshape industries worldwide, the global mobile app market is undergoing rapid transformation and unlocking new opportunities. To help industry professionals stay ahead, SocialPeta has released the H1 2025 AIGC Mobile App Marketing Analysis Report.

Drawing on a database of more than 1.6 billion ad creatives and real-time data updated hourly, the report provides a comprehensive look at the market landscape, key marketing trends, and standout case studies across the AIGC mobile app sector.

Smarter, more targeted advertising

In the first half of 2025, the AIGC mobile app market revealed a striking trend: while the number of active advertisers declined, ad creative investment surged. This shift signals a move from broad expansion to deeper engagement in existing markets, with marketing strategies becoming increasingly sophisticated, data-driven, and precisely targeted.

In H1 2025, the global monthly average of active AIGC advertisers reached 2,041, representing a 47.07% year-over-year decline. This drop reflects growing market saturation in certain sectors, resulting in fewer new entrants. At the same time, leading players doubled down on creative investment: the monthly average number of creatives per advertiser climbed to 416, an 84.22% year-over-year increase. This surge underscores advertisers’ efforts to enhance brand visibility and deepen user engagement through more frequent creative updates.

Advertising trends in global AI apps

Source: SocialPeta

Distinct regional dynamics further shaped the market landscape:

• Europe remains home to the largest number of AIGC advertisers but saw a steep 45.7% decline, reflecting heightened competition.
• North America, supported by mature user payment habits, leads in total creative volume, solidifying its role as a core growth hub for emerging products.
• Japan and South Korea stood out with creative volumes surging 262.3% and 266.1% year-over-year, respectively, positioning them as high-potential growth markets.
• Emerging regions such as Southeast Asia and the Middle East also recorded growth of more than 100%, highlighting the accelerated global expansion of AIGC apps.

AI app advertising trends in top countries/regions in H1 2025

Source: SocialPeta

Video remains the dominant format, accounting for 84.05% of all creatives, while images represent 14.93%. Within video ads, 15–30-second clips lead at 56.91%, followed by shorter clips under 15 seconds at 36.51%. Together, these two formats make up more than 93% of total video creatives, underscoring user preference for high-density, fast-paced content—a style that aligns perfectly with the communication dynamics of short-video platforms such as TikTok.

Practical models for technology implementation and scenario integration

This report spotlights representative AI+ products across key verticals, particularly in imaging and content creation.

AI + imaging: Meitu’s dual-engine model of “technology + product”

Meitu has sustained strong commercial growth by deeply embedding AI technology into its imaging and design products. On August 18, 2025, Meitu Inc. (1357.HK) released its H1 2025 financial report, reporting total revenue of RMB 1.8 billion, up 12.3% year-over-year, driven primarily by growth in paid subscriptions powered by AI features.

Globally, Meitu’s product portfolio has delivered impressive results. With multimodal editing functions such as AI one-click outfit changes and AI poster generation, Meitu reached the number one spot in the photography category in markets like Italy in June 2025—a major breakthrough in Europe.

Wink, focused on AI video editing and image enhancement, has surpassed 30 million monthly active users worldwide and repeatedly ranked number one overall in app charts across Southeast Asia, including Indonesia and Thailand.

Meanwhile, BeautyCam, featuring AI beauty enhancement and old-photo restoration, ranked number one during the Spring Festival in multiple countries and has consistently held leading positions in key markets such as Japan and Indonesia.

Meitu’s overseas AI products

Source: SocialPeta

In its marketing creatives, Meitu emphasizes functional impact. For example, Wink showcases “automatic image quality restoration” through direct side-by-side comparisons, while AirBrush highlights the ease of “intelligent object removal,” engaging users with clear, intuitive, before-and-after visuals.

Beyond imaging and animation, this report also features product insights in AI-powered education and health, offering a broader view of how AIGC is transforming various industries.

AI + education: A booming market

Source: SocialPeta

The H1 2025 AIGC Mobile App Marketing Analysis delivers a comprehensive overview of the global AIGC app market—covering market scale, competition, and user consumption trends across North America, Europe, and Asia-Pacific. It also examines future growth opportunities and challenges worldwide, equipping industry professionals with actionable intelligence for expansion strategies.

Spanning roughly 30 pages, the report provides data-driven insights and practical strategies to help businesses navigate the rapidly evolving AIGC mobile app landscape.

Download your copy today to stay ahead in this fast-changing market.

The post AI-generated content mobile app marketing report H1 2025 appeared first on Business of Apps.

When Apple’s iOS 14.5 update landed in April 2021, it rewrote the economics of mobile app growth. The update’s App Tracking Transparency framework effectively killed user attribution for mobile marketers, leaving app developers scrambling to understand which campaigns drove downloads and purchases.

Three years later, a new industry has emerged from that chaos. Companies like FunnelFox are helping mobile apps bypass Apple and Google’s app stores entirely for subscription revenue, routing users through web-based payment flows that save millions in commissions while restoring the attribution data that iOS 14.5 took away.

The anatomy of web-to-app

The concept behind “web-to-app” is elegantly simple. Instead of driving Facebook or TikTok ads directly to app stores, marketers route traffic to mobile-optimized web pages that mirror their app’s onboarding experience. Users complete surveys, interact with app functionality, and most importantly, purchase subscriptions directly on the web before being redirected to download the app.

“Usually app marketers run traffic through paid user acquisition channels like Meta, TikTok, Google directly to app stores,” explains Andrey Shakhtin, CEO and Co-Founder of FunnelFox, in a recent interview with Business of Apps conducted by industry veteran David Murphy. “The release of Apple’s iOS 14.5 update in 2021 dealt a massive blow to both ad campaign performance and user attribution efforts.”

The future of web-to-app funnels for app growth

The web-to-app approach solves two critical problems simultaneously. First, it restores complete attribution data, allowing marketers to optimize campaigns with the precision they enjoyed before iOS 14.5. Second, it eliminates Apple’s and Google’s commission structure, which ranges from 15% for smaller developers to 30% for larger ones, replacing it with standard payment processing fees of 2% to 5%. Web-based subscription flows also offer payment flexibility that app stores simply cannot match.

The infrastructure challenge

The shift from app store to web revenue requires rebuilding core business infrastructure. Apps moving subscription revenue to the web must suddenly handle refunds, tax calculations, payment disputes, chargebacks, and subscription management — functions that Apple and Google previously managed.

“The App Store took care of all this stuff on behalf of developers,” Shakhtin notes. “Shifting your purchases from the App Store to web leads to a lot of additional things that you need to take care of.”

This complexity explains why FunnelFox’s recently launched billing infrastructure has become crucial for the company’s 500-plus app clients. The platform’s payment orchestration system routes transactions between multiple payment providers based on factors like user location and card type, while automated recovery systems save an average of 20% of failed payments.

“The App Store took care of all this stuff on behalf of developers. Shifting your purchases from the App Store to web leads to a lot of additional things that you need to take care of.”

Perhaps most importantly, the system includes what FunnelFox calls “subscription cancellation flow”, customizable retention experiences that can save up to 30% of revenue that would otherwise be lost to churn. Unlike Apple’s restrictive cancellation process, web-based flows allow developers to survey departing users, offer win-back discounts, and implement sophisticated retention strategies.

The conversion optimization edge

The data emerging from web-to-app implementations reveals significant performance advantages over traditional app store funnels. FunnelFox’s pre-designed checkout flows demonstrate conversion rates 20-30% higher than default payment provider interfaces, while the company’s no-code funnel builder allows marketing teams to launch experiments without involving development resources.

“You can set up experiments and get web-based changes in the marketing team itself without support from product or technical teams,” Shakhtin explains.

This experimentation velocity has driven a new trend: multi-funnel strategies. Instead of funneling all traffic through a single conversion path, successful apps now deploy multiple funnels targeting different user segments. One fitness app client, previously operating a single funnel, now runs ten distinct funnels for different user types.

“More and more companies started launching multiple funnels for specific user segments,” Shakhtin observes.

“More and more companies started launching multiple funnels for specific user segments.”

AI meets human creativity

Looking ahead, FunnelFox is betting on AI-assisted funnel generation while maintaining human creative control. The platform’s AI tools can generate production-ready funnels based on prompts, allowing marketers to rapidly test concepts across different audience segments.

But Shakhtin emphasizes that creativity remains fundamentally human. “Creativity is still coming from people,” he says. “I see it as a combination of people creativity and AI automated operational stuff.”

“Creativity is still coming from people. I see it as a combination of people creativity and AI automated operational stuff.”

The Apple policy wild card

Apple’s recent policy changes allowing external payment options could accelerate web-to-app adoption. While developers must still pay Apple a commission on external payments, the reduced rate and increased payment flexibility make web-based revenue streams more attractive.

“I think it’s great momentum for the whole industry to shift strategies from App Store-only for distribution and monetization to web and other external options,” Shakhtin argues.

Industry observers suggest that web-based revenue is already becoming dominant for companies that have implemented web-to-app strategies, with marketing budgets increasingly allocated to web channels rather than traditional App Store acquisition.

“I think it’s great momentum for the whole industry to shift strategies from App Store-only for distribution and monetization to web and other external options.”

The path forward

For app developers considering the transition, Shakhtin’s advice is unequivocal: prioritize experimentation speed over building internal infrastructure. “Don’t use your in-house solutions because it’s slowing you down,” he warns. “Use proper tools to experiment faster and grow faster.”

The web-to-app revolution represents a fundamental restructuring of how mobile apps generate revenue. As attribution challenges persist and commission costs remain high, the companies that master web-based subscription flows may find themselves with sustainable competitive advantages that extend far beyond simple cost savings.

“Don’t use your in-house solutions because it’s slowing you down. Use proper tools to experiment faster and grow faster.”

In an industry where iOS 14.5 seemed to level the playing field by limiting everyone’s attribution capabilities, web-to-app has emerged as the new differentiator, separating companies willing to rebuild their growth infrastructure from those content to accept the limitations of dependency on Apple and Google.

The post The future of web-to-app funnels for app growth appeared first on Business of Apps.

As shopping season’s biggest events approach, most eCommerce app marketers and advertisers are making the same costly mistake: fighting for attention in the same overcrowded channels instead of meeting shoppers where intent is highest.

They’re fighting for the same expensive inventory while overlooking channels that could deliver better results at lower costs. With over 300,000 advertisers projected to be active in 2025, even well-resourced teams struggle to achieve performance goals in increasingly saturated markets.

Meanwhile, a completely different game is happening elsewhere.

The alternative opportunity

While brands compete in the same crowded channels, Android OEMs like Samsung, Xiaomi, Huawei, OPPO, Vivo, HONOR, and Transsion open seasonal promotional hubs.

Users enter these hubs actively hunting for deals and new shopping apps. This creates high-intent discovery moments that traditional platforms cannot replicate.

During Singles’ Day, Diwali, or Black Friday, Android OEM stores, otherwise known as alternative app stores, produce promotional hubs where conversion rates peak. Users aren’t passively scrolling through social feeds. They’re actively seeking offers, coupons, and new ways to shop.

The opportunity is massive. Samsung commands nearly 32% of the European market, while Xiaomi holds almost 14%. These alternative app stores serve hundreds of millions of users across Europe, Latin America, and Southeast Asia.

Three barriers keep brands away

Awareness

Many marketers assume alternative app stores only matter in China. In reality, they’re global, covering Europe, SEA, India, and LATAM.

Access

Unlike self-serve dashboards for Meta or Google, OEM inventory requires direct partnerships. Without that bridge, brands never see the opportunity.

Habit

User acquisition teams default to the “big three” because budgets and reporting frameworks are already set up there. Shifting spend into mobile OEMs feels like extra effort, even when returns are stronger.

The blind spot isn’t that Android OEM channels don’t work. Most brands simply aren’t set up to test them.

The preload advantage

Beyond seasonal hubs, Android OEM partners offer preload campaigns that traditional channels cannot match.

A preload puts your eCommerce app directly into a user’s device setup journey. When someone activates a new phone, your app appears on the “recommended apps” screen. If selected, it installs before they reach the home screen.

This delivers top-of-mind visibility and higher retention. Users adopt the app from day one of device use rather than discovering it through disruptive advertising later.

Beyond preloads

Native placements across Android OEMs include app store banners, category highlights, and folder placements. These put your app in front of shoppers at high-intent moments with steady scale and strong conversion rates.

Custom formats like splash ads, native banners, and folder placements let brands showcase seasonal offers directly within the device ecosystem.

For shopping seasons, splash ads and push notifications typically perform best. They provide immediate visibility and impact, making them especially effective for limited-time promotions like Black Friday or 11.11 sales.

Android OEMs also enable optimization for deeper metrics like customer retention rate and down-funnel performance rather than just install volume.

The window is open

Those who test Android OEM channels often find themselves in far less crowded competitive spaces with better ROAS and more loyal users.

The opportunity represents previously untapped inventory with attractive acquisition costs and strong down-funnel performance.

Shopping season timing makes this even more critical. While everyone competes in the same traditional channels, smart marketers are accessing high-intent users through promotional hubs specifically designed for deal discovery.

The upcoming shopping calendar presents multiple opportunities:

• 10 Shopping Festival (October 10): Major Southeast Asian eCommerce event
• Diwali (October 21, 2025): India’s biggest shopping season across all categories
• Singles’ Day (November 11): China’s largest shopping event, expanding globally
• Black Friday (November 29): Peak Western shopping day
• Cyber Monday (December 1): Online shopping focus
• 12 Shopping Festival (December 12): Major Asian eCommerce event
• Holiday Season (December 15-31): Last-minute gift shopping surge
• Chinese New Year (January 29, 2026): Massive shopping period across Asia

Each event triggers dedicated OEM promotional hubs where conversion rates consistently outperform traditional channels. The real question for marketers isn’t whether these moments matter, it’s whether you’ll continue relying on the same crowded channels, or meet shoppers where they’re already looking.

This article draws on findings from AVOW’s Mobile OEM Advertising Playbook for eCommerce Apps, which explores market shifts, diversification strategies, and success stories from leading brands.

The post The advertising window eCommerce apps are missing before shopping season appeared first on Business of Apps.

eCommerce marketers often need to balance rapid growth with sustainable returns. Customer acquisition costs continue to climb across major platforms, while consumer expectations for personalization and speed make campaigns harder to scale profitably. This means that finding cost-efficient and repeatable traffic sources is essential.

This article examines how one media buying team generated more than $833K in eCommerce sales while maintaining a stable 20–30% return on investment. The campaigns were built around push notification ads, combined with a disciplined approach to testing, scaling, and ongoing optimization. Optimization strategies were offered by Sotos Charalambous, PropellerAds Account Manager.

Rather than a one-time success, the results highlight how structured processes and careful traffic management can deliver consistent profitability across multiple projects. The following sections outline the campaign setup, performance data, optimization strategies, and lessons that other marketers can apply to their own eCommerce growth efforts.

Background and campaign setup

The advertiser behind these campaigns managed two distinct eCommerce projects. The first (Project A) focused on a global online marketplace offering a wide range of products, while the second (Project B) centered on a fast-fashion retailer targeting younger audiences in Western markets. Both projects relied on a commission-based affiliate model, where revenue was tied directly to completed sales.

To manage acquisition costs, the team used CPA Goal bidding, which automatically adjusted bids toward a target cost per action. Conversions were tracked through postback integrations, allowing real-time feedback between spend and performance.

After a range of tests, Push notifications were selected as the primary ad format. For eCommerce campaigns, Push remains a valuable channel: it provides immediate visibility on mobile devices, scales across multiple regions, and supports rapid testing of creatives and landing pages. The campaigns also incorporated other formats such as In-Page Push and Survey Exit, but Push consistently delivered the highest volume and strongest return.

Testing and scaling framework

The campaigns followed a phased lifecycle designed to minimize early risk and build momentum gradually.

Warm-up period: Campaigns began at least one week before major shopping events such as Singles’ Day, Ramadan, or Black Friday. This allowed the algorithms to gather data ahead of peak demand and reduced the volatility of last-minute launches.

Days 1–7 (Testing): During the first week, the focus was on broad data collection. The team ran A/B tests across landing pages, GEOs, and creatives, while monitoring zone-level performance. Early conversions often appeared with a delay, reinforcing the need to avoid premature decisions and give the data time to stabilize.

Days 8–20 (Scaling): Once baseline performance was established, budgets were gradually increased for high-performing zones. Underperforming sources were removed, and CPA targets were adjusted dynamically to sustain profitability.

Ongoing optimization: After the initial scaling, campaigns moved into continuous refinement. Every two weeks, zones with low conversion rates were excluded. Monthly reports also flagged traffic sources with high cancellation rates, ensuring budgets were consistently directed toward the most reliable segments.

This step-by-step approach created a balance between scale and efficiency, turning testing insights into sustainable growth.

Results and performance data

Project A: Global marketplace

This Push campaign ran for more than a year and generated over $833K in total sales. Average ROI remained above 20%, with significant month-to-month variations. April 2025 was a standout, with nearly $99K in revenue on $30K spend — a 225% ROI. Other months, such as January 2025 (17% ROI) and December 2024 (25% ROI), showed more moderate but steady performance.

Top GEOs for this campaign included: MX, FR, ES, IL, IT, CL, DE, BR, SA, KR, CO, PE, GH, NL.

The marketplace’s broad product range attracted a high-volume, price-sensitive user base, especially in emerging markets. Android devices dominated in these regions, reinforcing the importance of platform-level targeting in eCommerce campaigns.

Project B: Fast fashion retailer

The second project ran for 4-5 months and produced strong results across iOS-focused Tier-1 markets. Besides Push, In-Page Push for iOS, and Survey Exit format were used. Average ROI reached 39.7%, supported by high engagement among Gen Z and millennial shoppers who responded well to trend-driven promotions and limited-time offers.

In this case, iOS traffic consistently outperformed Android, reflecting higher conversion rates in premium markets. The contrast with Project A highlighted how device distribution and purchasing power can significantly influence campaign outcomes.

Together, the two projects demonstrate that sustained investment in Push, In-Page Push and Survey Exit traffic, supported by careful optimization, can produce both scale and profitability across different eCommerce offers.

Optimization strategies that worked

The results were achieved through systematic optimization rather than one-off tactics. Several practices proved particularly effective:

Zone management

The team conducted bi-weekly performance audits to identify underperforming traffic zones. Zones with persistently low conversion rates or high order cancellation rates were blacklisted. This continuous pruning process ensured budgets were redirected to reliable sources.

Dynamic bidding

Instead of fixed bids, the campaigns relied on dynamic CPA bidding. By passing payout data through the postback, bids were automatically adjusted in real time according to the value of each conversion. This approach allowed higher bids for profitable sales while keeping spend proportional to value.

Seasonal timing

Campaigns were aligned with major shopping events such as Singles’ Day, Ramadan, and Black Friday. Launching one week in advance gave algorithms time to adapt before traffic spikes, improving efficiency during peak demand.

Landing page testing

Multiple landing page variations were rotated during the testing phase, ranging from default product pages to seasonal sales links. This allowed the strongest performers to emerge based on conversion data, rather than assumptions.

Landing page testing

Source: PropellerAds

Audience segmentation

While language targeting was not required, device and GEO segmentation proved critical. Android traffic performed best in price-sensitive markets, while iOS conversions were stronger in higher-income regions. Creative selection, bidding, and landing pages were adjusted accordingly.

These strategies created a feedback loop where campaign health was preserved, profitability maintained, and scaling opportunities maximized.

Broader ecommerce lessons

Several takeaways from these campaigns extend beyond the specifics of push traffic.

Data patience matters

Conversions did not always appear immediately. In the first few days, results often lagged, underscoring the importance of allowing data to stabilize before making changes. Acting too quickly could have led to cutting profitable zones prematurely.

Device targeting reflects consumer behavior

Android performed better in emerging, budget-conscious markets, while iOS delivered higher conversion rates in premium regions. Aligning campaign setups with these patterns was essential to sustaining ROI.

Influencer activity complements performance ads

In fashion ecommerce especially, influencer visibility created demand that performance campaigns could then capture. Recognizing how earned attention interacts with paid channels helped maximize results.

Gamification is an emerging growth lever

Campaign managers observed that some ecommerce platforms were experimenting with gamified shopping experiences, such as rewards and bonus systems. For performance marketers, understanding how these dynamics affect user engagement may open new opportunities in future campaigns.

Final takeaways

These campaigns demonstrate that ecommerce advertisers can achieve both scale and sustainability with the right mix of traffic, bidding models, and optimization processes. By generating more than $833K in sales at an average 20–30% ROI, the advertiser showed that push notifications remain a reliable acquisition channel when paired with structured testing and disciplined scaling.

The broader lesson is that long-term profitability does not come from short-lived wins but from continuous refinement; testing landing pages, aligning with seasonal demand, managing zones, and tailoring strategies to device and market conditions.

Do you want to repeat this success or do even better? Join PropellerAds for high quality traffic, efficient tools, and expert guidance.

The post How push ads scaled eCommerce campaigns to $833k with consistent ROI appeared first on Business of Apps.

Android is the world’s most widely used mobile operating system. Yet most mobile ad budgets still treat it like second place.

Globally, Android holds over 70 percent of the smartphone market. It powers billions of devices across both premium and mid-range categories. But while users explore apps through manufacturer-owned stores, system tools, and native content feeds, most marketers remain focused on legacy channels tied to iOS or Google Play.

This is more than a missed opportunity. It is a strategic blind spot. Android’s scale is undeniable. But to unlock its full value, marketers need to go where traditional platforms cannot,  inside mobile OEM ecosystems.

Where are millions of high-intent users being overlooked?

Many mobile users do not discover apps through Google Play, the App Store, or ads on social media. Instead, they use tools already on their phones, powered by mobile OEMs.

Mobile OEMs such as Samsung, Xiaomi, OPPO, Vivo, Huawei and many others do more than build hardware. They also operate app stores, onboarding flows, security tools, and native content feeds that shape how users discover apps. These channels are built into the daily phone experience and offer touchpoints for advertisers to reach users during moments of intent.

Mobile OEM advertising connects with these users directly and contextually.

Why do mobile OEMs run their own stores, and why does it matter to advertisers?

Mobile OEMs operate their own ecosystems to control the user experience and unlock additional revenue. In many cases, they offer alternatives to Google Play due to regional restrictions, revenue share policies, or the need to deliver more localized services.

Huawei launched AppGallery after losing access to Google services. Xiaomi promotes GetApps across its global markets. OPPO and Vivo run Hot Apps and Hot Games. These platforms are not niche. In Android-first regions, they are often the default entry point for app discovery.

For marketers, mobile OEM advertising provides direct access to users at the moment they are actively searching, browsing, or setting up a device.

Where can I find new users beyond Google, Apple, and Meta?

The answer is mobile OEM advertising.

According to AVOW’s reach calculator, mobile OEM platforms now offer access to more than 1.5 billion monthly active users worldwide. These users are outside the typical programmatic and social media funnels.

• Samsung, as of June 2025, holds 33 percent of the smartphone market across Europe. Apple is just one percentage point ahead, and the two continue to trade the top spot.
• In France, Samsung reaches an estimated 17 million monthly active users through OEM channels, ahead of Apple’s 14 million.
• Germany reports 26.5 million Samsung users and 8.8 million on Xiaomi, closely matching Apple’s 25.8 million.
• In Spain, Samsung leads with 11.7 million monthly active users. Xiaomi and Apple each reach around 10.9 million.
• In the United Kingdom, Samsung connects with over 18 million users through OEM inventory. These users are still largely underutilized in most acquisition strategies.

These are not recycled impressions. They represent high-intent users in key European markets — users many advertisers have not yet reached.

What makes mobile OEM advertising work so well?

Mobile OEM advertising is effective because it places app recommendations where users already expect to find them. Placements appear during onboarding, in native app stores, or inside pre-installed system apps.

Advertisers using mobile OEM advertising benefit from:

• Lower acquisition costs in emerging markets
• Stronger install intent driven by contextual targeting
• Reach into tier 2 and tier 3 cities that are difficult to scale through traditional channels
• Privacy-resilient performance without dependency on user-level tracking

This is not about forcing ads into feeds. It is about surfacing apps in trusted environments.

Who is already scaling with mobile OEM advertising?

Performance marketers across industries are already reallocating budget into mobile OEM advertising.

• Tripledot Studios, the popular game developer, expanded into eight new countries and reached millions of users through mobile OEM campaigns
• Joom, a fast-growing e-commerce app, acquires more than 60,000 new users each month via mobile OEM stores
• Exness, a global fintech platform, drives more than 250,000 installs per month in Southeast Asia and the Middle East through mobile OEM partners

These are not isolated tests. They are scalable growth programs backed by repeatable results.

Is mobile OEM advertising still underused?

Yes. While awareness is growing, many advertisers still focus their budgets on over-saturated platforms with rising costs and declining visibility.

Mobile OEM advertising offers a way to diversify acquisition strategy, access new users, and protect performance against shifts in privacy rules and platform policies. It is one of the few remaining sources of untapped attention at global scale.

How can I calculate reach and plan mobile OEM campaigns?

To support media planning, AVOW provides a free User Reach Calculator tool, so marketers can explore user reach by country and app category. It is a simple way to size the opportunity and decide where to focus next.

For teams looking to grow in a crowded market, mobile OEM advertising is no longer optional. It is a strategic edge hiding in plain sight.

The post Calculating the real growth potential of mobile OEM advertising appeared first on Business of Apps.

Lightricks wanted to build an audience that connected with the apps. People who took the time to explore, chose to subscribe, and kept returning because they saw real creative potential in the experience. By 2025, Lightricks’ apps had surpassed 730 million downloads and over 6.6 million monthly paying users, underscoring the scale of the opportunity we were working with.

They were looking for a team that could keep up with their scale and make smart decisions in real time. As Tal A., Affiliate Marketing Lead at Lightricks, put it:

“Z2A Digital has been a great partner in achieving our mobile user acquisition goals. They’ve enabled us to reach new heights by delivering high-quality user volumes and aligning with our growth expectations and KPIs. The team provides strong, direct communication, and their responsiveness, transparency, and professionalism make them great partners.”

But making the most of that scale required precision, because each of Lightricks’ apps (Facetune, Photoleap, and Videoleap) had its audience, user behavior, and role in the market.

Scaling them together across different countries meant building a strategy that could adapt to local needs while staying focused on what made each product work. Lightricks’ core apps focus on selfie editing (Facetune), visual design (Photoleap), and video creation (Videoleap), each demanding tailored acquisition and retention tactics. Facetune alone had ranked as Apple’s most downloaded paid app in 2017 and continued evolving with advanced AI tools like virtual hair and outfit try-ons by 2023.

When Lightricks came to Z2A Digital, they were looking for a team who could think alongside them, working quickly while staying organized, and creating systems that were consistent without being one-size-fits-all. But most importantly, understanding that in user acquisition at this scale, what matters most is quality, consistency, and decision-making that evolves with context.

We were the first partner to take Lightricks truly global, expanding campaigns into new regions when most other partners focused exclusively on the U.S. market.

We also pioneered growth on non-iOS platforms, exploring Android and tablets at scale, while others were still concentrating their efforts on iOS alone.

Source: Z2A Digital

Channel mix, market signals, and the long game

Our approach started with a conversation, centered on questions that helped us unpack what was already driving growth, what was getting in the way, and where there was room to grow just under the radar. From there, we built outward, constructing a channel mix that spanned over 20 platforms, mapped against tiered regional performance data, and designed to align with Lightricks’ revenue goals while accounting for the unique behaviors of user segments across global markets.

The regional tier segmentation was based explicitly on ROAS thresholds, enabling optimized spend allocation across markets like the U.S., UK, Brazil, Canada, Germany, and rising markets such as Greece, Colombia, and Singapore. These regional tiers were co-developed with the client, giving Lightricks direct visibility and collaboration into which geos to prioritize based on ROAS performance.

Every interaction needed to reflect the brand’s creative spirit, so we focused on aligning the content with the message at every step, from the first impression to the in-app experience, making sure the tone, visuals, and delivery all worked together to support what Lightricks stood for. To keep the content experience fresh and culturally relevant, campaigns were adjusted with seasonal optimizations, tailoring visuals and messaging to local trends and calendar moments.

Z2A Digital brought that to life by blending high-quality branded visuals (like banners, GIFs, and videos) with real user-generated content, because genuine content typically leads to better results in creativity-focused platforms. The combination of professional, creative, and relatable user content increased engagement and also helped connect more deeply with Lightricks’ creative user base.

Performance data backed it up, showing that campaigns using both brand-led and user-generated content consistently delivered higher engagement and stronger trial conversion rates.

Source: Z2A Digital

We didn’t expect every campaign to be perfect from the start. Instead, we built room for them to evolve. We tracked live performance, during periods of underperformance, like why a trial offer wasn’t landing in Southeast Asia or what was driving drop-offs in LATAM, and turned those insights into smarter media plans, sharper onboarding, and refreshed creative that moved the numbers without losing the brand’s voice.

In addition to creative and media adjustments, we continuously refined in-app engagement incentives and bidding strategies to boost trial and purchase conversion without increasing CPA.

Thanks to our flexible, collaborative working model, Lightricks greenlit testing across new traffic sources and creative formats. Uriel, our account manager on the project, noted that the close and transparent way of working built the trust needed to try new ideas with confidence and that some of the strongest results came from that freedom to experiment together. Our personalized, high-trust relationship enabled us to expand experimentation in ways that drove both growth and innovation.

Source: Z2A Digital

Growth built on partnership

The results were the natural outcome of this process: a 185% increase in new installs across the product portfolio, a 35% uplift in trial conversion rates, and an 82% boost in monthly paid purchases. All achieved while maintaining efficiency on cost-per-acquisition and protecting long-term retention, proving that sustainable, high-value growth is possible when the mechanics of scale are grounded in execution.

These metrics were explicitly tied to maintaining target CPA and ROAS benchmarks while boosting retention and engagement rates. Z2A Digital and Lightricks monitored and optimized every stage of the funnel, from install to sign-up to free trial to first-time purchase, making sure the entire journey contributed to ROI. The outcomes reflect smart UA tactics and continued monetization success of Lightricks’ freemium-to-subscription model.

Source: Z2A Digital

That success was the result of ongoing partnership, weekly syncs, creative reviews, and shared problem-solving that blurred the lines between client and collaborator, and allowed us to move quickly without losing sight of what made Lightricks’ brand and user experience meaningful in the first place. It’s a way of working that only happens when there’s real trust on both sides.

In a market crowded with noise and scale-at-any-cost tactics, the Lightricks project proved that real, sustainable growth comes from building performance systems that scale with integrity, hold up under pressure, across borders, and over time.

Check out the full Lightricks case study here. If you’re curious how a similar strategy could work for your business, or want to explore growth across channels and markets, get in touch.

The post Inside the Z2A Digital strategy that drove +185% installs for Lightricks appeared first on Business of Apps.

As mobile-native audiences increasingly seek immersive yet time-efficient entertainment, short dramas have emerged as a key battleground in the global content race. More importantly, short drama apps offer a scalable, cost-effective format for storytelling that resonates across cultures, making them a powerful tool for content innovation and global expansion.

To help industry players navigate this rapid growth, SocialPeta has released its latest report, Insight into Global Short Drama App Marketing for H1 2025. This report provides comprehensive, omnichannel data insights into the evolving landscape, emerging trends, and breakthrough strategies shaping the global short drama ecosystem.

Whether you’re a developer, marketer, or platform operator, this report serves as a valuable strategic resource for understanding and succeeding in this fast-growing category.

Global short drama market landscape

In the first half of 2025, the global short drama market saw explosive growth, alongside clear regional differences.

The U.S. leads in revenue, capturing 40% of the global market, driven by a mature entertainment ecosystem and a strong willingness to pay. Japan and Brazil follow, though their market sizes remain considerably smaller.

Download growth, however, is more geographically diverse. The U.S. still holds the top spot, but emerging markets like India, Indonesia, and Brazil are quickly gaining ground in Southeast Asia and Latin America. These regions benefit from a young, mobile-first population, widespread internet access, lower user acquisition costs, and strong device compatibility, all of which support rapid user growth and scalability.

Top 10 regions by market share

Click on image for full size.

Source: SocialPeta

Regional audience tastes are crucial in shaping global short drama strategies:

• North America favors urban and dominant-male romance, often blended with fantasy (e.g., werewolves, vampires). Female viewers lead the demand, expecting high production and acting quality. TikTok and Instagram are the main marketing channels.
• Southeast Asia leans into youth, campus life, family drama, and time-travel revenge plots. Viewers aged 18–30 prefer fast-paced, twist-filled stories. Facebook and YouTube dominate traffic, while growing local platforms require strong cultural localization.
• Japan focuses on revenge and workplace dramas, while Korea prefers sweet romances featuring children or reincarnation. Female viewers of all ages dominate both markets, with content needing to match local TV formats and social media habits.
• Middle East audiences are drawn to family feuds and moral conflicts among elites. The 25–50 high-income demographic shows strong spending power, but content must strictly respect religious and cultural sensitivities.

Comparison of characteristics among mainstream markets

Click on image for full size.

Source: SocialPeta

Balancing global scale with local execution

In H1 2025, short drama marketing saw aggressive spending and hyper-targeted execution. Over 300 advertisers were active globally, a 53.9% YoY increase, while monthly ad creatives surged 275.38%, underscoring fierce competition for user attention.

• North America led in advertiser volume, making it the most competitive region.
• Europe stood out with the highest average number of creatives per advertiser, signaling deep market engagement.
• Southeast Asia ranked second in advertiser count, driven by its large, mobile-first population.
• Africa remained largely untapped, with minimal ad activity.

Advertising trends in top countries/regions in H1 2025

Click on image for full size.

Source: SocialPeta

Winning playbooks from leading platforms

Top platforms have set the pace for global short drama success:

• ReelShort, an early mover, generated $130M in IAP in Q1 2025. Its success stemmed from early market entry, aggressive social media marketing, and bold genre choices. With 1.34 million creatives globally, it focused on the U.S., Brazil, and France. Hits like HOW TO Tame A SILVERFOX thrived on taboo romance themes.
• DramaBox gained traction through precise targeting and distinct storytelling. Male-male romance drove viral growth, inspiring widespread imitation. My Secret Agent Husband 2 stood out with its spy-romance tension.
• New players like NetShort (via translated dramas and mass ad buying) and FlickReels (targeting silver-haired audiences) showed that niche strategies and aggressive scaling can still break through.

Advertising trends in global short drama apps

Click on image for full size.

Source: SocialPeta

Creative localization

Tailoring ad creatives to local language and culture is essential for effective user acquisition:

• English creatives often use exclamations and direct calls to action.
• Spanish versions lean on emotional appeal with rhetorical questions.
• French favors refined, elegant phrasing.
• Chinese emphasizes dramatic plot twists and climactic moments.

Universal phrases like “Watch FULL Episodes” remain effective global hooks. The emergence of new titles like ShortBox and Loom Drama reflects a strategic shift from competing with existing hits to generating fresh demand.

Analysis of popular short drama ad copies in H1 2025

Click on image for full size.

Source: SocialPeta

Innovation meets industrialization

The rise of short drama blockbusters isn’t accidental; it’s the result of sharp genre targeting and increasingly industrialized production:

• Mainstream genres like sweet romance continue to win over women aged 18–35 with idealized love stories, where cultural localization and attractive casting are key success factors.
• Revenge and uplift dramas thrive in emerging markets by delivering emotionally satisfying stories of social ascent, often mirroring local frustrations and aspirations.
• Meanwhile, emerging genres are bridging market gaps. Hits like Pregnant by My Ex’s Professor Dad explore taboo age-gap themes, 1955: Married the Mafia Boss by Contract taps into period nostalgia, and Don’t Mess with Billionaire’s Parents! resonates with aging audiences seeking relevance and respect.

Analysis of popular ad creatives

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Source: SocialPeta

The future of short drama: Three defining trends

Premiumization meets diversification

Platforms are moving away from high-risk bets on single blockbusters toward more calculated, high-probability content models. AI-assisted creation, combined with local production teams, is fast becoming the industry standard.

A broader monetization ecoosystem

While in-app advertising (IAA) and in-app purchases (IAP) remain core, new models are gaining traction. Free-to-watch short dramas are streamlining user acquisition, while branded content, membership subscriptions, and social commerce integrations are creating new revenue streams.

Deeper cultural co-creation

The global short drama market is projected to exceed $3 billion in 2025. China continues to lead the charge with strengths in web novel IPs, efficient production pipelines, and strong technological foundations, powering a new wave of content exports.

This 50-page white paper, led by SocialPeta and supported by industry leaders including DreameShort, OneSight, and Onset Octopus, offers a comprehensive look at the current landscape, from structural shifts to creative best practices.

The report Insight into Global Short Drama App Marketing for H1 2025 goes beyond data. It uncovers the disruptive logic behind content globalization: rooted in localization, driven by innovation, and scaled through industrialization.

Download your copy today to stay ahead in this rapidly evolving market.

The post The 2025 short drama report appeared first on Business of Apps.

With the rise of mobile internet, the mobile gaming industry has become a cornerstone of global digital entertainment. Mobile games are everywhere, fundamentally changing the way people play and engage with content.

Now, the industry stands at a pivotal moment — shifting from rapid expansion to refined, data-driven operations, where every strategic move can shape a game’s success or failure.

To help industry professionals stay ahead, SocialPeta has partnered with NewsBreak to release the report Insights into Global Mobile Game Marketing Trends for H1 2025. This comprehensive report highlights key trends and offers actionable insights for navigating the evolving mobile game landscape.

This white paper presents a deep dive into the latest global mobile game marketing trends, highlighting the following key insights:

• The advertising advantage is diminishing, giving rise to a closed-loop competitive model where content, monetization, and user acquisition are tightly interlinked. A game’s success now hinges on seamless collaboration between product and marketing teams, as well as their ability to execute rapid iterations and optimizations.
• Mid-core and hardcore game categories are entering a saturated market. Genres such as SLG and RPG are experiencing limited growth and are exploring hybrid gameplay models — such as tower defense + idle or RPG + simulation — to drive innovation and engagement.
• “Light traffic” acquisition strategies are becoming less effective, prompting marketers to shift budgets toward high-quality user segments. Compared with the broad acquisition tactics of 2022–2023, H1 2025 is characterized by more precise targeting, focusing on users with higher retention rates and stronger LTV, particularly on iOS.
• Creative fatigue is becoming increasingly challenging. The pace of creative iteration has accelerated from quarterly to monthly, and now even to weekly cycles. In mature markets, the average creative lifespan is often less than five days, demanding faster, smarter ad production strategies.

This report is designed to equip game publishers and marketers with actionable insights to stay ahead in an increasingly competitive and fast-evolving mobile gaming landscape.

The white paper offers an in-depth analysis across six major game genres — strategy, RPG, puzzle, casual, simulation, and casino — alongside detailed breakdowns of key global regions, including North America, Japan and Korea, Hong Kong–Macao–Taiwan, Europe, Southeast Asia, the Middle East, and South America.

Nearly 7K new mobile game advertisers monthly; RPGs lead with 250+ creatives per game

In H1 2025, global mobile game advertising activity experienced robust growth. The average number of active advertisers per month surpassed 78K, up 17.2% year over year, and peaked at nearly 90K in June. On average, 6,900 new advertisers entered the market each month, with April alone exceeding 7,900. New advertisers accounted for 8.8% of the monthly total, highlighting both the growing vitality of the market and the rising intensity of competition.

Advertising trends in global mobile games in H1 2025

Click on image for full size.

Source: SocialPeta

New advertiser activity in H1 2025 showed strong momentum. On average, more than 81% of advertisers released new creatives each month — an 18.2% year-over-year increase. After March, this proportion consistently remained above 80%. The share of new creatives also experienced significant growth, peaking at over 60% in April and averaging 57% across the period, up 5.7% year over year. The continued rise in both new advertiser participation and creative output points to an accelerated pace of creative iteration across the industry.

Trends in new ad creatives for global mobile games in H1 2025

Click on image for full size.

Source: SocialPeta

According to H1 2025 data on top-performing mobile game genres, RPGs remained the most competitive, averaging 251 creatives per month — the highest among all categories — followed closely by strategy games with 216 creatives. Casino games stood out with impressive growth: their share of advertisers rose to second place at 20.6% (+9.9% year over year), while their share of creatives ranked third at 11.2% (+5.7% year over year). In contrast, casual games experienced a decline in both advertiser presence and creative output, signaling a shift in genre momentum.

Advertising analysis of global mobile games by genre in H1 2025

Click on image for full size.

Source: SocialPeta

Booming North American mobile game market in H1 2025

In H1 2025, the North American mobile game market saw significant expansion. The number of advertisers climbed to 80.3K, reflecting a 25.8% year-over-year increase, while total creative volume soared to 13.6 million, up 36.4% year over year. The influx of new titles featuring unique gameplay mechanics further heightened competition across genres.

Analysis of marketing in North America

Click on image for full size.

Source: SocialPeta

Local news platforms followed well-defined strategies for creative production. Native ad formats focused on providing a non-intrusive, seamless experience, featuring concise, soft-sell headlines and a natural visual style that blended effortlessly with editorial content. Landing pages leveraged educational material and user reviews to enhance credibility and drive conversions.

Senior-friendly ad designs specifically targeted older audiences by incorporating large fonts, simple and clear language, calming background music, and slow-paced explanations. Featuring real, trustworthy figures further enhanced credibility, making the ads easier to see, understand, and trust.

Effective creative formula for local news platforms I

Click on image for full size.

Source: NewsBreak

Other creative strategies included “thrill-spot focus” and “emotional resonance”

The thrill-spot focus approach highlighted key gameplay moments and core benefits within seconds, paired with strong calls to action, fitting background music, and live-action appearances to encourage immediate downloads.

Meanwhile, the emotional resonance approach relied on relatable storytelling, genuine user reviews, emotionally driven content, and clear instructions to spark user interest. Both strategies helped mobile games effectively connect with audiences via North American news platforms.

Effective creative formula for local news platforms II

Click on image for full size.

Source: NewsBreak

This white paper also provides an in-depth analysis of the full marketing cycles of four standout mobile games from H1 2025:

• Screwdom: A casual hit featuring nail-pulling mechanics
• Penguin GO!: A mini game representing successful global expansion
• Wuthering Waves: The fastest-growing anime-style mobile game
• Kingshot: A high-quality SLG title from a leading global developer

Packed with valuable insights, this report spans over 60 pages and offers the latest, most comprehensive data and forward-looking perspectives to empower your global expansion success.

Download your copy today and stay ahead in this fast-evolving market.

The post The 2025 mobile gaming report appeared first on Business of Apps.

Scaling a mobile game in 2025 takes more than just paid traffic or clever creatives. It means understanding your players, adapting quickly, and building strategies that connect every part of the user journey.

This guide, brought to you by Gamelight, brings together what actually works. From rewarded UA and hybrid-casual design to real-time personalisation and AI-driven decision making, we’ve combined expert insights and proven tactics into one clear roadmap. If you’re focused on acquiring and retaining high-value players, this is your starting point.

The UA landscape is changing fast

The days of relying solely on CPI campaigns are over. In 2025, success in user acquisition (UA) demands adaptability, precision, and a multi-layered approach. Players expect more. So do publishers. And so does the market.

Mobile gaming is more competitive than ever, and you have less time to make an impression. It’s not enough to acquire users: you need to acquire the right ones, engage them from the first tap, and retain them with experiences that adapt. Those who rely on old playbooks risk being left behind.

At Gamelight, we’ve partnered with top experts and industry leaders to uncover what’s working, and what’s next. This report merges all six parts of our “Mastering UA” series into a single, actionable narrative, featuring expert voices and their insights.

Rewarded UA: Engaging with intent

Rewarded UA represents a shift in how publishers think about acquisition, not just as a numbers game, but as a value exchange. It encourages players to participate more deeply by rewarding them for actions that matter, like trying a new game, completing a level, or returning regularly.

This strategy drives higher engagement, longer session times, and ultimately, stronger ROAS. Unlike traditional disruptive UA methods, rewarded user acquisition enhances the gaming experience. Players feel acknowledged, publishers earn loyalty, and the data backs it up.

It’s also a privacy-conscious solution in the post-IDFA landscape. With consent at the forefront of every interaction, and a clear user value proposition, rewarded channels avoid many of the common problems that traditional UA faces.

Niels Beenen, Strategic Partnerships Director, EMEA of Singular, puts it:

“Rewarded Advertising as a channel has again seen impressive growth in 2024. Singular customers are reaping the rewards of working with rewarded partners and I envisage strong showing in our upcoming ROI Index. We expect to see this growth continue in 2025 as Rewarded Partners will focus on growing their SOV on iOS, introducing new ad formats & working on optimising engagement algorithms. What drives performance is user engagement and rewarded channels are uniquely positioned to deliver on this front.”

At Gamelight, we use AI to match rewards to player behaviour, helping boost retention and lifetime value while keeping acquisition costs steady. Our experience tells us that users that have been acquired through rewarded channels usually convert faster and stay longer, which benefits both performance marketing and long-term growth.

Hybrid-casual games: A genre built for growth

Hybrid-casual gaming has risen to a top position in the field of mobile game design, exemplifying a major evolution. This genre appeals to both casual and hardcore players, by combining simple, accessible mechanics with deeper systems like progression, customisation, and storylines.

In UA, hybrid-casual games work in a dual way: on one hand, they attract high volumes of users through simple entry points; on the other, they manage to retain these users thanks to mid-core strategies. Thanks to this, key KPIs like CPI and LTV are fully balanced.

Hybrid-casual games leverage flexible monetisation models, as they usually integrate IAP and IAA systems. In this way, they are able to generate revenue while also keeping players retained through engaging gameplay loops. This success can be showcased through real life cases like the ones of Phase 10 and UNO, where Gamelight delivered an outstanding UA performance.

Arina Yanko, Performance Director at Whalo Games, provides perspective:

“Hybrid-casual games are designed to attract a broader audience while enhancing monetisation by combining accessible gameplay with deeper progression mechanics. To effectively engage players and drive in-app purchases, these games must offer depth that appeals to users seeking long-term progression, not just quick entertainment. While more challenging to develop, hybrid-casual games foster higher lifetime value (LTV) by appealing to dedicated players, building a community that is valuable to nurture and manage for an enriched user experience.”

Personalization: Make every experience count

In the age of algorithmic content and dynamic feeds, players expect their game experience to feel as personal as their social media. Generic marketing no longer cuts through; it must be tailored, adaptive, and emotionally relevant.

Personalization begins before install, with creative assets and messaging that speak to each user’s interests. Post-install, it continues with onboarding flows, reward timing, difficulty tuning, and content curation. The goal: reduce friction, increase satisfaction, and make players feel like the game was built for them.

It’s not just about user experience: it drives results. Personalised creatives boost clicks, adaptive gameplay lowers churn, and smart offers turn casual users into payers.

Renata Ravilova, Co-Founder and CEO of CoPostly.com, shares:

“Personalisation is the word of 2025. In every aspect of users’ experience, including marketing campaigns and their acquisition and personalised monetisation approach. In terms of creative strategy and UGC ads, that means using a variety of creators and building user personas that attract hyper-specific audiences.”

At Gamelight, real-time AI powers personalisation by tracking user behaviour and adjusting each interaction. From in-app messages to post-install targeting, we build experiences that connect, driving longer playtime, deeper engagement, and higher LTV.

IP power: Trust, nostalgia, and performance

Partnering with an iconic IP gives a UA campaign immediate momentum. Familiar characters and stories capture attention in a crowded feed and reduce the cognitive friction that often blocks installs. Players are more willing to try a game they feel they already understand.

But IPs are more than surface-level bait. When used correctly, they fuel community, deepen emotional engagement, and improve retention metrics. They provide creative teams with ready-made assets and UA teams with built-in credibility.

Done right, an IP-driven game doesn’t just outperform on launch: it stays relevant through ongoing content, cross-promotions, and event-based campaigns aligned with franchise milestones.

Ksenia Nogina, Senior PR & Marketing Manager at AAA Agency, explains:

“Collaborating with popular IPs is a powerful strategy gaining momentum. It leverages the built-in passion and loyalty that fans already have for the IP, creating a shortcut to engagement and purchase intent (usually leading to higher DAU, LTV, and retention). Besides, IP-based mobile games or the ones that involve IP-based events show a reduced reliance on paid traffic sources. So, we expect more integrations of various forms in 2025.”

AI: From buzzword to backbone

AI is no longer just a tool: it’s the infrastructure behind today’s most successful UA campaigns. It can anticipate user behaviour, test and optimise creatives in real-time, and even suggest the best timing and budget allocation across channels.

It automates, yes, but more importantly, it elevates the performance. By revealing insights faster than human teams ever could, AI lets marketers make decisions with clarity and speed. That’s critical in a market where CPMs fluctuate hourly and user preferences shift by the week.

Thomas Kriebernegg, General Manager at SplitMetrics Agency, puts it bluntly:

“I personally don’t buy into the idea that AI is here to replace marketers. What’s really happening is that marketers who know how to harness AI will start replacing those who don’t — whether due to lack of motivation or because they’re stuck in slow-moving enterprise environments with legal barriers to innovation. The real disruption will come when lean, AI-native teams outperform legacy giants with teams 10x their size.”

Live Ops: Turning moments into momentum

Live Ops has become a critical pillar in modern mobile gaming. It extends the lifecycle of users acquired through paid campaigns, helping publishers maximise the value of each install.

A successful Live Ops strategy creates rhythm and builds habits. Players don’t just come back, they come back with purpose; daily rewards, co-op events, time-limited challenges… All of these keep the game fresh and make sure users remember and return to your app.

But to truly work, Live Ops needs personalisation. Players expect content that adapts to their playstyle, timing that fits their schedule, and incentives that matter to them.

Kseniia Maiboroda, CEO and Founder at Elevatix, explains the stakes:

“Live Ops without personalisation is like putting on a show without knowing your audience. To truly drive retention and engagement, it’s not enough to launch events — they need to adapt to each player’s behavior and motivation. With AI and ML, Live Ops becomes a dynamic system that responds in real time, making players feel like the game is tailored just for them.”

Final words: The synergy behind scalable UA

If there’s one thing the Mastering UA series has taught us, it’s this: no single tactic wins the game. The real power lies in synergy:

• Use rewarded UA to scale user acquisition with intent
• Leverage hybrid-casual gameplay to broaden and deepen appeal
• Drive personalization to connect meaningfully with players
• Integrate IP collaborations to supercharge brand visibility
• Let AI optimise and adapt every campaign in real time
• Maintain momentum through Live Ops that turn installs into communities

Gamelight sits at the intersection of all six: combining cutting-edge tools, strategic insights, and a platform built for high-LTV scale. Let’s redefine what growth looks like in 2025. If you’re ready to scale smarter, faster, and further, we’re here to help.

The post The definitive guide to scalable UA growth in mobile gaming appeared first on Business of Apps.

The mobile advertising landscape is experiencing a significant shift. For the first time since 2017, the limits of relying solely on walled gardens — platforms like Google, Meta, TikTok, and Snap — are becoming clear. In response, programmatic advertising is gaining traction as a scalable, transparent, and sustainable solution for brands seeking incremental growth beyond the constraints of search and social giants.

Why walled gardens are losing their grip

Walled gardens built their dominance for good reasons: intuitive platforms, powerful automation, and granular targeting. But these strengths are being challenged by growing costs, privacy limitations, and lack of transparency.

Rising costs, lower ROI

In 2025, the average cost per lead on Google Ads hit $70.11*, a 15% increase from the previous year. That’s simply not viable for many advertisers, especially when performance doesn’t scale in parallel. I’ve worked with campaigns where CPLs doubled while results stayed flat. The problem? Overcrowded platforms experiencing what economists call diminishing marginal returns.

The data tells the story: while walled gardens still command 78% of global digital advertising revenue, their growth rate is slowing. Meanwhile, advertisers report that 67% of their walled garden campaigns fail to meet ROI targets, according to recent industry surveys.

Privacy-driven attribution breakdown

Apple’s iOS 14.5 rollout fundamentally broke the mobile advertising measurement model. The numbers are staggering:

• 96% of U.S. users opted out of social media tracking
• Conversion reporting delays now extend up to 72 hours
• 25% of U.S. users are completely unidentifiable across apps
• Globally, 388 million users (10% of mobile users) are untrackable

Attribution models that advertisers relied on for years became obsolete overnight. Meta alone reported that iOS changes impacted their ability to target and measure ads for over 1 billion users. Many mobile marketers watched their iOS campaigns lose 40-60% effectiveness without warning.

Transparency limitations

Walled gardens operate as black boxes, limiting access to user-level data and cross-channel attribution. In a Forrester study, 73% of advertisers feel they lack sufficient transparency from major platforms to make informed optimization decisions.

This opacity becomes particularly problematic when trying to understand incremental lift or true attribution across channels. Platform-reported metrics often inflate their own contribution while undervaluing other touchpoints in the customer journey.

Unlocking untapped reach beyond the ecosystem

While users spend 61% of their time on the open internet, 78% of digital ad budgets still flow to walled gardens. That disconnect signals a missed opportunity. Programmatic opens access to diverse global inventory, including emerging channels like CTV.

With the open web programmatic market expected to exceed $50B by 2025 (growing at 12% annually, vs. 6% for walled gardens), it’s clear this is where forward-thinking advertisers are placing their bets.

AI-powered real-time optimization

Today’s leading DSPs process over 10 million bid requests per second, making split-second decisions based on user behaviour, context, and conversion probability. This isn’t just faster than human optimization, it’s more intelligent.

Advertisers using AI-powered DSPs report 35-40% improvements in campaign performance metrics, according to a 2024 Winterberry Group study. But here’s the key difference: these optimizations serve your goals, not platform revenue goals. When Facebook optimizes your campaign, they’re optimizing for Facebook’s success. When a DSP optimizes, it’s optimizing for your KPIs.

Transparent measurement

Programmatic offers full visibility into performance, including attribution models that balance deterministic data with privacy-compliant probabilistic signals. You get to audit your own results, instead of relying on vendor-reported metrics.

The performance reality: Mobile DSP metrics that matter

When evaluating the shift to programmatic, focus on metrics that drive business outcomes, not vanity metrics that platforms prefer to highlight.

Scale and quality

Leading mobile DSPs provide access to 85-90% of global mobile inventory, but quality matters more than quantity. Look for platforms offering premium inventory with transparent quality scores and fraud detection.

Attribution accuracy

In our post-iOS 14.5 world, the best DSPs combine multiple attribution methodologies; deterministic where available, contextual signals, and privacy-compliant probabilistic matching. This hybrid approach delivers 60-80% attribution accuracy compared to 30-40% for platforms relying solely on device IDs.

Efficiency gains

Advertisers switching from pure walled garden strategies to diversified programmatic approaches typically see 20-30% improvements in cost efficiency within 90 days. The key is having clear baseline metrics before making the transition.

Cross-channel performance

Advanced DSPs enable true cross-channel measurement, showing how mobile, CTV, and display work together. This holistic view often reveals that programmatic channels drive 15-25% of walled garden conversions through upper-funnel influence.

How to win with mobile programmatic

If you’re still too dependent on walled gardens, now is the time to diversify. Yes, search and social will continue to deliver foundational scale. The opportunity is clear: layer programmatic on top to capture incremental reach and data transparency that closed ecosystems can’t match.

Here’s how successful advertisers are building programmatic muscle.

Diversify your media mix strategically

Don’t abandon existing channels overnight, that’s tactical suicide. Instead, allocate 20-30% of your budget to programmatic testing while maintaining current performance levels. Use programmatic to access incremental reach that walled gardens can’t provide.

The most successful transitions follow a 70/20/10 rule: 70% in proven channels, 20% in programmatic scaling, 10% in experimental channels.

Invest in privacy-ready infrastructure

Build robust first-party data collection and activation capabilities. Choose DSP partners with demonstrated expertise in contextual targeting and cohort-based optimization.

Leading advertisers are seeing 40-60% of their programmatic performance coming from contextual and first-party data targeting, proving that post-cookie advertising is not only viable but often more effective.

Prioritize transparent, advanced DSPs

Evaluate DSP partners based on these critical capabilities:

• Inventory quality: Access to premium publishers with transparent quality metrics
• Attribution methodology: Hybrid attribution models that work in privacy-first environments
• Optimization flexibility: Ability to optimize for your specific goals (CPI, ROAS, LTV)
• Cross-channel integration: Unified measurement across mobile, CTV, and display
• Fraud protection: Advanced fraud detection with transparent reporting

Launch systematic test-and-learn campaigns

Start with a simple A\B testing framework. Run programmatic campaigns alongside your existing UA strategies, measuring incremental lift rather than just direct performance. Track cost per install (CPI), cost per action (let’s say an order – CPA), return on ad spend (ROAS), and most importantly, incremental revenue.

Most advertisers see 20-30% improvements in cost efficiency, but the real value often comes from reaching high-value users that walled gardens miss entirely.

The path forward

I’ve been in this industry long enough to see several major shifts, and this one feels different.

The combination of cost pressures, transparency demands, and the need for incremental growth is forcing advertisers to think differently about their media mix.

We’re not witnessing the end of walled gardens. Rather, we’re seeing a shift toward a more balanced, diversified, and transparent digital media ecosystem.

Programmatic is no longer optional for mobile advertisers seeking scale, precision, and data-driven outcomes. It complements your current stack by filling in measurement gaps, unlocking new channels, and surfacing high-value users traditional platforms often miss.

The future is programmatic but one that thrives alongside search and social, not in place of them. The brands that embrace both will lead the next era of mobile growth.

The post Why programmatic must be in every mobile UA stack appeared first on Business of Apps.

AI search engines are having a moment — and by moment, we mean they’re everywhere. With ChatGPT leading the way and others catching up, AI tools are taking over everything from answering questions to turning your cat’s blurry photo into a professional portrait.

The numbers tell a story that’s hard to ignore. ChatGPT hit a million users in — drum rolls, please — five days. Yes, within five days of launch, it hit a million users. For perspective, it took Instagram two and a half months to reach those numbers. Netflix? More like 3.5 years.

We’ll see soon. AI-powered platforms might be gearing up to rewrite the rules of search monetization. The real puzzle here is whether these AI platforms will just copy Google’s homework and throw the ads into their responses, or if we’re about to see something completely different. Maybe they’ll surprise us all with fresh ways to generate revenue that nobody’s even thought of yet.

Can AI search compete with Google Ads?

First off, let’s ask ourselves if AI search even has the potential to compete with a giant like Google?

The thing with Chat GPT is that it’s not just another search tool, it’s an intelligent system that is interactive, personalized, and intuitive. With Google or Bing, you type a question and get pages of links that might lead to the answer. Sure, it’s fast, but it’s also impersonal.

ChatGPT doesn’t just give you a list,  it delivers tailored, conversational answers, offering a more human-like experience. Instead of saying, “Here are your search results,” it’s more like, “Here’s the answer you’re looking for, and let me explain it to you.” This shift is a game changer, especially for complex searches that involve multiple layers of information.

How will this be monetized?

Ads and subscription potential

Just like Google famously made its billions from search ads (hello, sponsored results!), there’s clear potential for ChatGPT to do the same.

Let’s just imagine how that could play out. You ask ChatGPT a question, and instead of just getting a standard answer, you receive tailored, sponsored responses or even see promoted products, all while keeping the conversation smooth and interesting. It’s like when you ask for restaurant recommendations and, instead of a list, ChatGPT offers a local restaurant and an ad for a special promotion there. Feels natural, right? Personalized ads, done well, could make the experience even better.

But, of course, ads aren’t the only option. OpenAI might also look into creating a premium subscription model or offering paid features for businesses or power users. Let’s consider companies could pay for access to advanced tools or extra data analysis capabilities that ChatGPT could offer.

A mix of both: ads and subscription tiers? You bet.

ChatGPT feels different from the usual search engines. It’s conversational, it’s human-like, and it’s smooth. So, if ads come into the picture, how do we keep that magic alive? If you’re asking for help with a complicated query and suddenly, an ad pops up in the middle of the conversation, that could throw off the whole vibe.

The challenges

Models like ChatGPT aren’t just clever conversationalists; they’re powerful machines, processing tons of data to keep everything running smoothly.

That kind of computing power doesn’t come cheap. Powerful cloud servers, GPUs working non-stop, and a lot of energy. Scaling ChatGPT to meet skyrocketing demand isn’t just a technical challenge, it’s a very expensive one. So, while ads, subscriptions, and premium features are all great ideas, OpenAI also has to factor in the reality of its operational costs, and most importantly, find a way to keep those costs from eating up its bottom line.

Is Google losing ground?

Let’s be real, a lot of things changed with OpenAI’s launch of ChatGPT Search. It was the platform’s way of stepping up to challenge giants like Google and Bing in the search engine game. ChatGPT Search mixes real-time web results into the conversation, serving up live updates on everything from sports scores to stock quotes, weather, and news.

It’s no surprise that this move reflects OpenAI’s ongoing evolution, which follows a massive funding round that valued the company at $157 billion, along with securing a $4 billion revolving line of credit.

So, are people starting to ditch Google for AI-powered search? The numbers are starting to suggest so. Google has seen a notable decrease in the use of its AI Overviews feature, which was initially designed to enhance search results with AI-generated summaries. Data from BrightEdge indicates that AI Overviews appeared in less than 7% of Google search queries by the end of June 2024, down from 15% earlier in the year. This decline suggests that users may be less inclined to rely on Google’s AI features for their search needs.

Meanwhile, platforms like ChatGPT and Perplexity are growing their user bases by offering an alternative that feels faster, easier, and often more accurate for certain types of queries.

But, can this shift flip the script?

Google’s ad revenue remains rock solid. While some users are migrating to conversational AI, Google’s search infrastructure and global user base make it difficult for competitors to dethrone it entirely – at least for now.
Its algorithms still lead in delivering hyper-relevant results, and its advertising network spans multiple platforms like YouTube, Gmail, and Google Maps, diversifying its revenue streams.

That said, if conversational AI platforms can figure out how to monetize effectively, whether through ads, partnerships, or subscription models, there’s potential for a significant disruption in the digital advertising market.

However, Google is not sitting idle in all of this. In fact, Gemini is designed to integrate directly into Google’s search capabilities and combine the power of AI with Google’s search infrastructure to give users smarter, more conversational results. One of the key features of Gemini is its ability to embed products directly into search results, seamlessly mixing ads with answers. Sounds familiar?

And in a world where personalization takes the lead, this could reshape how Google moves forward.

The future of search

The rise of AI-driven platforms could mark the dawn of an entirely new era for search engines. It’s like moving from a library card catalog to having a personal librarian who knows exactly what you need and how you like it.

It’s clear that moving forward, we’re likely to see a hybrid model emerge in the search market, an exciting blend of traditional search engines and AI capabilities. It’s essentially a turbocharged version of what we’re used to.

Subscription-based services could become a major player, allowing users to pay for premium access to AI-driven search features that offer deeper insights, or even a fully ad-free experience. With integrated product placements promotions, AI could seamlessly weave relevant products and services into your search experience, making them feel natural and tailored to your needs.

Along with these updates, voice search is set to become much more significant. As voice assistants like Siri, Alexa, and Google Assistant continue to grow, more users will rely on voice queries to get things done. Search engines will have to adapt, fine-tuning their algorithms to better handle natural language and conversational tones.

Image and video search, too, are on the rise too. It won’t be long before you can simply upload a picture to find similar items or information, or even search using AR or video clips to get highly accurate results.

The new era starts now

Forget the old-school, one-size-fits-all ads, AI is taking advertising to a whole new level. We’re witnessing a full-blown digital revolution in action. The way we find information, how businesses engage with customers, and even how we consume content is getting a total AI makeover. And the best part? It’s a win-win. Users get tailored, intuitive experiences while businesses finally connect with the right people, exactly when it counts.

The most interesting part is that this isn’t something far off in the future, it’s happening right now. And while we’re seeing progress, the biggest innovations are still ahead of us.

Buckle up, because the AI search revolution is only getting started and it’s going to be a wild ride.

The post The future of AI search monetization appeared first on Business of Apps.

Binh Dang has a simple answer when asked about Flo Health‘s core marketing strategy: “Fear.”

Not the fear that drives users away, rather the fear that brings them to download one of the world’s most successful female health apps. As Head of ASO at Flo, Dang has built a marketing philosophy around something most companies avoid discussing: the genuine anxieties that drive user behavior.

In this episode of App Talk recorded at App Promotion Summit London 2025, Dang reveals how Flo transformed user privacy from a compliance headache into their strongest growth driver, and why their approach to acquisition involves literally stopping strangers on London streets.

The big red button that changed everything

When Roe v. Wade was overturned in 2022, Flo Health fast-tracked the launch of its “anonymous mode” feature. This represented a fundamental reimagining of the relationship between user privacy and business growth, far beyond any public relations gesture.

“We have the option so powerful, we don’t even have the ability to gather your data,” Dang explains. “You have the power now.”

When activated, anonymous mode completely detaches all identifiable data from a user’s health information. The feature operates with such comprehensive protection that even if government bodies requested user data, Flo couldn’t provide it for users who chose anonymity. The company has deliberately engineered itself out of the surveillance equation.

Most companies would have marketed this as a technical achievement — encryption protocols, data architecture, security certifications. Flo took the opposite approach. They sold emotional relief.