How Top Publishers Win at User Acquisition in 2026

The UA Landscape in 2026

Let's start with the numbers. AllInsights.ai tracking across 140 countries shows that the average cost per install for mobile games rose to $2.87 globally in Q4 2025, up from $2.66 a year earlier. For mid-core genres like strategy and RPG, CPIs in tier-1 markets now routinely exceed $5.00, and for competitive categories such as match-3 puzzle games in the US, top-of-funnel costs have crossed $8.00 for some advertisers. These are not marginal increases — they represent structural inflation driven by consolidation among ad platforms, rising floor prices, and a shrinking pool of easily addressable high-LTV users.

The privacy landscape has compounded these pressures. Apple's App Tracking Transparency (ATT) framework, now in its fourth year of enforcement, has permanently reduced the effectiveness of deterministic user-level targeting on iOS. Opt-in rates have stabilized around 25–30% globally, meaning roughly three-quarters of iOS users remain invisible to traditional attribution and targeting systems. On the Android side, Google's Privacy Sandbox is rolling out in phases, deprecating the advertising ID and replacing it with cohort-based and on-device signals that offer less granularity than the systems they replace.

The combined effect is a UA environment where the old playbook — build a lookalike audience from your best payers, run it on Facebook and Google, optimize CPIs down with event-based bidding — no longer delivers the returns it once did. The targeting precision that enabled it has eroded, the platforms have repriced to reflect reduced signal quality, and competition for the remaining targetable inventory has intensified.

Yet amid all of this, the top 50 mobile game publishers by ad spend grew their combined install volume by 14% year-over-year in 2025, according to AllInsights.ai Ad Intelligence data. Their secret is not a single tactic but a combination of strategic shifts across creative, channels, technology, and measurement. The rest of this report unpacks each one.

Creative Strategy is the New Targeting

When you can no longer target the right person with surgical precision, you need the right message to find the right person on its own. This is the fundamental insight driving the creative revolution in mobile UA. In a post-ATT world, the creative itself has become the primary targeting mechanism — different ad creatives naturally attract different audience segments based on their content, tone, and style. Top publishers have internalized this and restructured their entire creative operations accordingly.

AllInsights.ai Ad Intelligence data reveals that the top 20 mobile game advertisers by spend now test an average of 78 unique creative variants per campaign, with the most aggressive teams exceeding 100 variants per title per month. Compare this to the industry median of roughly 12–15 variants. The gap is enormous, and it directly correlates with performance: publishers running 50+ variants per campaign see 18–25% lower effective CPIs than those running fewer than 20, even when advertising on the same networks in the same geos.

The type of creative matters as much as the volume. UGC-style ads — content that mimics organic social media posts, reaction videos, or influencer reviews rather than polished studio productions — are outperforming traditional high-production-value ads by 25–40% on engagement metrics like click-through rate and video completion rate. This holds across networks but is especially pronounced on TikTok and Instagram Reels, where the native content aesthetic sets user expectations. Ads that feel like ads get scrolled past; ads that feel like content get watched.

Short-form video dominates. Creatives under 15 seconds consistently outperform longer formats on engagement and cost efficiency. The logic is simple: in a feed-based environment, you have two seconds to stop the scroll. The creative needs to deliver its hook immediately and convey enough value to drive an install action within a compressed attention window. The top-performing format we see across genres is a pattern we call "hook, gameplay, payoff" — an attention-grabbing opening frame (often a provocative question, surprising visual, or emotional trigger), followed by 5–8 seconds of real or stylized gameplay, ending with a clear value proposition or call to action.

Playable ads remain the highest-converting format in mobile gaming, with conversion rates roughly 2x higher than standard video across most genres. The catch is production cost: a quality playable ad costs 4–5x more to produce than a video creative, requires engineering resources rather than just design, and takes significantly longer to iterate. The publishers getting the best results use playables strategically — deploying them as "closer" creatives for retargeting and high-intent audiences rather than as top-of-funnel reach vehicles.

Channel Diversification

Relying on two ad platforms is a risk that top publishers are no longer willing to take. AllInsights.ai data shows that the top 50 gaming advertisers by spend distribute their budgets across an average of 5.4 ad networks, up from 3.8 two years ago. The motivation is both strategic (reducing platform dependency) and economic (finding pockets of underpriced inventory as the major platforms become more expensive).

Meta and Google remain the two largest channels for gaming UA, accounting for a combined 48% of tracked gaming ad spend in Q4 2025. But that share has been declining steadily — it was 57% in Q4 2023. The beneficiaries of this redistribution are varied. TikTok's share of gaming ad budgets grew from approximately 12% in early 2025 to 18% by year-end, making it the third-largest channel for gaming UA globally. TikTok's appeal is driven by its young user demographics, high engagement rates, and a native ad format that aligns well with the UGC-style creatives that are currently outperforming across the industry.

Unity Ads and AppLovin remain essential in-app advertising partners for gaming, collectively commanding roughly 15% of gaming UA budgets. Their strength lies in access to in-game inventory — showing ads to people who are already playing games, which means higher intent and stronger conversion rates for gaming advertisers. AppLovin's self-serve platform and machine learning-based optimization have been particularly effective for mid-market publishers who lack the engineering resources to build custom bidding solutions.

The most interesting emerging channel is connected TV (CTV). Still representing under 3% of gaming UA budgets, CTV ad spend for gaming is growing at approximately 40% year-over-year. The appeal is reach and attention quality — CTV ads are viewed in a lean-back, full-screen environment with completion rates exceeding 90%, compared to 20–30% for mobile feed ads. The challenge is measurement: CTV-to-mobile-install attribution remains imprecise, relying primarily on probabilistic methods and exposed vs. holdout incrementality testing. Sophisticated publishers are treating CTV as a brand and awareness channel that lifts performance across all other channels rather than expecting direct, last-click attributable installs.

Influencer and creator partnerships have grown 35% year-over-year in gaming UA budgets and are proving especially effective for mid-core and strategy games where community trust and authentic endorsements carry significant weight. The model has matured beyond simple sponsored posts: performance-based creator deals, where influencers are compensated partially on CPI or retention metrics, now represent roughly 40% of gaming influencer spend. This alignment of incentives has dramatically improved the quality and authenticity of creator-driven UA.

AI-Powered Campaign Optimization

Artificial intelligence has become the operational backbone of modern UA. Every major ad platform now offers automated bidding, creative optimization, and audience expansion powered by machine learning. Meta's Advantage+ campaigns, Google's Performance Max, TikTok's Smart Performance Campaigns — these tools have effectively democratized the tactical execution of UA, making it possible for a two-person growth team to achieve bid optimization that once required a dedicated data science function.

But democratization cuts both ways. When everyone has access to the same AI-powered bidding tools, the competitive advantage shifts from execution to inputs. The publishers winning at UA in 2026 are those feeding the algorithms better signals and more creative volume. First-party data — post-install events, revenue signals, engagement metrics, churn predictions — is the fuel that makes automated optimization work. Publishers with richer event taxonomies and more sophisticated postback configurations consistently outperform those sending basic install-only signals, even when bidding on the same inventory.

Machine learning-based CPI prediction is another area where top teams have pulled ahead. By training models on historical campaign data — creative features, audience signals, seasonal patterns, competitive density — these teams can forecast expected CPIs and ROAS before committing significant budget to a new campaign or geo. This "predict before you spend" approach reduces wasted budget on underperforming tests by an estimated 20–30% compared to the traditional "launch and optimize" methodology.

Testing velocity has become a key performance indicator for growth teams. AllInsights.ai analysis of top-performing publishers reveals that the highest-growth teams run upwards of 200 A/B tests per month across creative, bidding strategy, audience configuration, and landing page variations. The marginal gains from any single test are small, but the compound effect of hundreds of optimizations per month creates a meaningful and durable performance advantage over competitors testing at lower velocity.

Creative Trends We're Tracking

Beyond the strategic frameworks, the specific creative formats and styles that dominate UA are shifting rapidly. AllInsights.ai Ad Intelligence tracks creative trends across millions of ad units, and several patterns stand out heading into 2026.

Fake gameplay is declining. For years, misleading gameplay ads — showing puzzle mechanics that don't exist in the actual game, or depicting dramatically different visual quality — were tolerated by platforms and effective at driving cheap installs. That era is ending. Both Apple and Google have tightened their creative review policies, and Meta introduced automated "gameplay accuracy" checks in late 2025 that flag creatives showing mechanics absent from the advertised app. More importantly, players have become savvy: the disconnect between ad and reality drives immediate uninstalls and poor retention, which the platforms' own optimization algorithms now penalize through higher costs.

Real gameplay with a hook is winning. The format replacing fake gameplay is straightforward: show the actual game, but open with a compelling hook that creates curiosity or emotional engagement. A strategy game ad might open with "I lost my entire army in 30 seconds" before showing real battle footage. A puzzle game might start with an impossible-looking level before revealing the satisfying solution. The key is authenticity plus intrigue — giving players an honest preview while making the content compelling enough to stop the scroll.

ASMR-style ads are growing in casual genres. Oddly satisfying visual and audio content — slicing, pouring, peeling, sorting — has proven remarkably effective for casual and simulation games. These ads leverage the same psychological appeal that drives ASMR content on YouTube and TikTok, creating a sensory experience that holds attention and generates high completion rates. Our data shows ASMR-format ads achieving 35–50% higher video completion rates than standard gameplay ads in casual genres like simulation, idle, and merge.

Story-driven mini-narratives work for mid-core. For mid-core genres — RPGs, strategy, survival — the most effective emerging format is the 15–30 second emotional arc. These are micro-stories: a character faces a dilemma, makes a choice, experiences consequences, and the outcome hooks the viewer into wanting to make their own choices. Think of them as interactive fiction trailers. The production cost is higher than gameplay capture, but the engagement and conversion metrics justify it: story-driven ads show 20–30% higher install rates than pure gameplay ads in RPG and strategy categories.

Before/after transformation formats are driving strong engagement in simulation, decoration, and makeover games. The formula is simple: show a messy room, broken car, or neglected garden in its "before" state, then show the satisfying transformation after the player intervenes. This format taps into the same psychological reward loop that makes home renovation content viral on social platforms, and it translates directly into high-performing UA creative.

Measurement Challenges

The measurement infrastructure that UA teams relied on for the past decade has been fundamentally disrupted, and the industry is still rebuilding. SKAdNetwork (SKAN), Apple's privacy-preserving attribution framework, is now on version 4.0 and provides more conversion value bits and multiple postback windows than earlier versions. But it remains a fundamentally limited signal: attribution is aggregated, delayed, and lacks the user-level granularity that powered the sophisticated LTV models and cohort analyses that UA teams built their operations around.

Google's Privacy Sandbox attribution API for Android is still in its early adoption phase, with most publishers running it in parallel with traditional attribution rather than relying on it as a primary signal. The industry expectation is that Android attribution will follow a similar trajectory to iOS — a gradual reduction in deterministic signals over 12–18 months, forcing a migration to privacy-preserving alternatives.

In this environment, incrementality testing has emerged as the gold standard for measurement at sophisticated publishers. Rather than asking "which ad drove this install?" (an increasingly unanswerable question), incrementality testing asks "would this install have happened without the ad?" by running exposed vs. holdout experiments at the geo or cohort level. AllInsights.ai data suggests that approximately 35% of top-50 publishers by spend now run regular incrementality tests, up from under 15% in 2023. The results frequently reveal that 15–25% of attributed installs would have occurred organically — a finding that, while uncomfortable, enables significantly more efficient budget allocation.

Media mix modeling (MMM) is experiencing a renaissance. Once considered a legacy technique from the pre-digital era, MMM has been updated with modern machine learning approaches and is proving valuable for channel-level budget allocation decisions. MMM does not require user-level data — it operates on aggregate spend and outcome data — making it naturally privacy-compliant. Its weakness is granularity and speed: MMM works best for strategic budget allocation across channels over weeks and months, not for real-time campaign optimization.

Probabilistic attribution fills the gaps for publishers that need more granular signal than SKAN or MMM can provide. By using contextual signals — IP address patterns, device characteristics, timing correlations — probabilistic methods can infer attribution at the user level without relying on a tracking identifier. Accuracy varies widely, however, typically ranging from 70–85% depending on the methodology, the market, and the ad network. Publishers using probabilistic attribution need to treat it as directional rather than deterministic and build their decision-making frameworks accordingly.

What You Can Learn from Competitor Intelligence

Everything described in this report — creative trends, channel shifts, optimization techniques — can be observed, benchmarked, and learned from by studying what competitors and market leaders are doing. This is where ad intelligence platforms become a strategic asset rather than a nice-to-have research tool.

Using AllInsights.ai's Ad Intelligence module, growth teams can see exactly which creative formats their competitors are running, on which networks, in which geos, and for how long. When a competitor suddenly scales a new creative format from testing to heavy rotation, that is a signal: the creative is working. When a publisher that historically spent 80% of its budget on Meta begins shifting 30% to TikTok and AppLovin, that is a signal about channel performance. When seasonal spending patterns reveal that competitors pull back in January but surge in March, that creates an opportunity to capture cheaper inventory during the gap.

The most sophisticated UA teams use competitive intelligence not to copy but to understand. Seeing that a competitor's UGC-style ads are getting heavy rotation does not mean you should produce identical ads — it means the format is working in your genre, and you should develop your own variation. Noticing that top publishers in your category are increasing spend on a particular network signals that the network's audience or algorithm is currently favorable for your genre. Tracking how long competitors run specific creatives tells you about fatigue cycles and helps you time your own creative refreshes.

AllInsights.ai tracks ad creatives across major networks globally, providing creative galleries, spend estimates, network distribution, and historical trend data for hundreds of thousands of mobile game advertisers. Teams using this data report spending 40–60% less time on competitive research while gaining significantly deeper insight into market dynamics than manual monitoring could ever provide.

The bottom line: user acquisition in 2026 rewards teams that combine creative excellence, channel diversification, technological sophistication, and strategic intelligence. No single tactic wins in isolation. The publishers pulling ahead are those that treat UA as a system — where creative, data, measurement, and competitive awareness reinforce each other in a continuous optimization loop. The tools and frameworks to build that system exist today. The question is whether your team is using them.