How has ATT changed mobile advertising? (2026)

Published by Shamanth at April 21, 2026

What is Apple’s ATT framework and why does it matter for mobile advertisers?

ATT requires apps to ask users for permission before tracking them across other apps and websites via the IDFA. According to AppsFlyer's latest opt-in data, only 15-25% of users grant permission, which eliminated deterministic cross-app tracking for the vast majority of iOS users.

Key insight: ATT didn’t remove tracking entirely. It removed the default assumption that tracking was allowed.

IDFA now requires explicit user consent
75-85% of iOS users deny tracking
Gaming apps see higher opt-in than utilities
Apple replaced IDFA attribution with SKAdNetwork
Prompt language biases users toward opting out

App Category	Typical ATT Opt-In Rate	Source
Gaming	25-30%	Adjust Mobile App Trends 2025
Social / Entertainment	18-22%	AppsFlyer Opt-In Benchmarks
Finance / Banking	12-16%	AppsFlyer Opt-In Benchmarks
Utilities / Productivity	14-18%	Adjust Mobile App Trends 2025
E-commerce / Shopping	16-20%	AppsFlyer Opt-In Benchmarks

Before iOS 14.5 shipped in April 2021, the IDFA was available by default. Every install, every in-app event, every downstream purchase could be attributed deterministically to the ad that drove it. Marketers built entire optimization stacks on this signal.

ATT flipped the default to “off.” Users now see a prompt asking whether they consent to being tracked. The prompt language (“Ask App Not to Track”) was deliberately framed to discourage consent, and Apple knew it.

The result: roughly 75-85% of iOS users now deny tracking, per AppsFlyer's 2025 data. Gaming apps tend to see slightly higher opt-in rates (closer to 25-30% according to Adjust's Mobile App Trends report) because users understand the value exchange. Finance and utility apps hover closer to 12-18%.

For a deeper explanation of the replacement attribution system Apple introduced, see our breakdown of how SKAdNetwork works.

Does the ATT prompt wording affect opt-in rates?

Yes, significantly. Apps that add a pre-permission "context screen" explaining why tracking helps (better ads, free content) see opt-in rates 10-15 percentage points higher, according to RevenueCat's State of Subscription Apps 2025. The custom explanatory string Apple allows beneath the prompt is essential.

Apps that leave it blank or use generic language waste their one chance to frame the value exchange.

How did ATT impact mobile ad targeting and audience quality?

ATT collapsed the precision of behavioral targeting on iOS. According to Lotame's research, personalized ad targeting efficiency dropped by approximately 37% post-ATT, and Meta reported a $10 billion revenue impact in 2022 alone from signal loss.

Key insight: The targeting loss didn’t hit all advertisers equally. Broad-targeting strategies actually improved post-ATT.

Personalized targeting efficiency dropped ~37%
Meta lost $10B in 2022 revenue from signal loss
Lookalike audience effectiveness declined sharply
Broad targeting with strong creative outperformed narrow targeting
Creative quality replaced audience segmentation as primary lever

Pre-ATT, advertisers could build hyper-specific audiences: lookalikes from purchasers, retargeting pools from website visitors, suppression lists of existing customers. All of this relied on IDFA or cross-platform identifiers that ATT disrupted.

Meta’s lookalike audiences were hit hardest. The seed audiences shrank as fewer users could be matched, and the algorithmic expansion lost the deterministic signal that made it precise. In practice, many advertisers found that broad targeting with strong creative signal outperformed narrowly targeted campaigns post-ATT.

This aligns with what Matej Lancaric described in his approach to scaling UA like a hypercasual game: broad targeting with audience sizes of 20M+ keeps CPIs as low as $0.10-$0.12 because the algorithm has enough volume to optimize on creative performance rather than audience segmentation.

The shift was philosophical: instead of “find the right user and show them any ad,” it became “show the right ad and let the algorithm find the user.” Creative became the targeting lever.

How did ATT change mobile measurement and attribution?

ATT replaced deterministic, user-level attribution with probabilistic and aggregated models. Apple's SKAdNetwork (now SKAN 4.0) provides campaign-level conversion data with delays of 24-72 hours, while MMPs shifted to modeled attribution, according to AppsFlyer's SKAN documentation.

Key insight: The biggest measurement casualty was not accuracy. It was speed. Delayed signals broke real-time optimization loops.

SKAdNetwork provides aggregated, delayed conversion data
Postback delays range from 24-72 hours
Campaign ID limits constrain creative testing granularity
MMPs shifted to probabilistic and modeled attribution
Triangulation of SKAN, MMM, and incrementality is now standard

Attribution Method	Granularity	Latency	Accuracy Level
Pre-ATT IDFA (deterministic)	User-level	Real-time	Very high
SKAN 4.0	Campaign/ad-set level	24-72 hours	Moderate (lossy)
MMP probabilistic modeling	Channel-level	Near real-time	Moderate
Media Mix Modeling (MMM)	Channel-level	Weekly/monthly	Directional
Incrementality testing	Channel/campaign level	1-4 weeks	High (but slow)

Before ATT, an MMP like AppsFlyer or Adjust could attribute an install to a specific ad within seconds. Advertisers saw real-time ROAS, optimized campaigns intraday, and measured 30-day LTV with user-level cohort analysis.

SKAdNetwork replaced this with a system that is deliberately lossy. Conversion values are capped, postbacks are delayed, and campaign IDs are limited (initially to 100, expanded in SKAN 4.0 to a hierarchical system). Our summary of the Singular SKAN benchmarks report details how these limitations affect performance data.

The practical impact: advertisers lost the ability to optimize toward downstream events (like Day 7 ROAS) in real time. Instead, they had to rely on new measurement frameworks combining SKAN data, MMM (media mix modeling), and incrementality testing.

According to MobileDevMemo, most sophisticated advertisers now run a "triangulation" approach: SKAN for directional campaign-level data, probabilistic attribution from MMPs for channel-level trends, and incrementality tests for ground truth. No single source is reliable alone.

Is SKAN 4.0 actually usable for optimization?

It's better than SKAN 3.0 but still limited. SKAN 4.0 introduced hierarchical conversion values with three postback windows (0-2 days, 3-7 days, 8-35 days), providing coarser data over longer periods, per Apple's SKAdNetwork documentation. The crowd anonymity thresholds mean low-volume campaigns get almost no useful data.

Campaigns need at least 128+ daily installs to receive meaningful conversion value data, as noted in discussion of what's working post-ATT.

How did Meta (Facebook) adapt to ATT?

Meta underwent the largest platform overhaul in response to ATT. They launched Aggregated Event Measurement (AEM), rebuilt their ML models for probabilistic optimization, and shifted toward Advantage+ campaigns that consolidate targeting. Per Meta’s Q4 2023 earnings, ad revenue recovered to $40.1 billion quarterly, suggesting their adaptation succeeded.

Key insight: Meta’s recovery came from rebuilding its models around creative signal and on-platform behavioral data, not from restoring lost tracking.

Aggregated Event Measurement limited to 8 conversion events
Advantage+ campaigns consolidate targeting into broad automation
Install-optimized campaigns often outperformed AEO post-ATT
Meta rebuilt ML models around on-platform behavioral data
Ad revenue fully recovered by 2023

Meta was the loudest critic of ATT, and for good reason. Their entire advertising model was built on cross-app behavioral data. When ~80% of iOS users opted out, Meta’s optimization models lost the feedback loop they relied on.

The first phase of adaptation was AEM, which limited advertisers to 8 prioritized conversion events per domain and introduced a 72-hour attribution delay. This was painful but necessary for SKAN compliance.

The second, more transformative phase was Advantage+ Shopping Campaigns (ASC) and the broader shift to broad targeting. Meta essentially told advertisers: stop trying to define your audience. Instead, give us strong creative, and our models will find the users.

This approach mirrors what we've observed across the industry: past purchase behavior, not context, drives conversions, and Meta's on-platform behavioral data remained rich even after ATT.

One critical post-ATT finding: install-optimized campaigns often showed stronger downstream CPAs than AEO campaigns because they accumulated enough conversion volume to feed the algorithm. This was counterintuitive but consistently observed.

How did Google, TikTok, and Apple Search Ads adapt to ATT?

Each platform adapted differently based on their data assets. Google leaned into its first-party search and Play Store data with Privacy Sandbox. TikTok built out its own Advanced Matching and creative-first optimization.

Apple Search Ads became the only fully deterministic iOS channel, with CPIs averaging $2.07 across categories per Apple Search Ads benchmarks.

Key insight: Apple Search Ads became the only iOS channel with deterministic attribution, making it the highest-signal data source post-ATT.

Google UAC adopted SKAN and playable ads outperform display
Privacy Sandbox for Android will deprecate GAID
TikTok Advanced Matching recovers 15-25% more conversions
Apple Search Ads has full deterministic attribution
Snap invested in server-side conversion APIs

Platform	ATT Adaptation Strategy	iOS Attribution Method	Key Advantage Post-ATT
Meta	AEM + Advantage+ broad targeting	SKAN + probabilistic modeling	Massive on-platform behavioral data
Google UAC	SKAN modeling + Privacy Sandbox	SKAN + conversion modeling	First-party search + Play Store data
TikTok	Events API + Advanced Matching	SKAN + server-side matching	Creative-native format
Apple Search Ads	Proprietary Apple ID attribution	Deterministic (non-SKAN)	Only fully deterministic iOS channel
Snap	Advanced Conversions + SKAN	SKAN + modeled	Younger demographic, lower competition

Need help scaling your mobile app growth? Talk to RocketShip HQ about how we apply these strategies for apps spending $50K+/month on UA.

Google's response was two-pronged. On iOS, Google App Campaigns (UAC) adopted SKAN conversion modeling and encouraged advertisers to use broader creative formats. According to Matej Lancaric, playable ads significantly outperform display ads on UAC.

On Android, Google is rolling out Privacy Sandbox, which will eventually deprecate GAID (the Android equivalent of IDFA).

TikTok invested in their Events API and Advanced Matching to reconstruct conversion signals server-side. Their creative-native format meant the shift toward "creative as targeting" was less disruptive. According to TikTok's Ads documentation, advertisers using Advanced Matching see 15-25% more attributed conversions.

Apple Search Ads occupies a unique position. Because it operates within Apple’s own ecosystem, it has full access to Apple ID-based attribution. ASA doesn’t rely on SKAN; it uses its own proprietary attribution.

This makes it the cleanest signal source on iOS, which is why most sophisticated advertisers treat ASA data as a calibration benchmark for their other channels.

Snap also adapted by building their own retargeting capabilities and Advanced Conversions API, though their share of UA budgets remains smaller than the big three.

How should marketers restructure their campaign architecture post-ATT?

Consolidate campaigns aggressively. The post-ATT algorithm needs concentrated conversion volume to optimize. According to industry best practices documented by Meta's advertising documentation, campaigns should target 50+ conversions per week per ad set minimum, and AEO campaigns need 128+ daily installs to exit the learning phase reliably.

Key insight: Campaign consolidation is the single highest-impact structural change most advertisers haven’t fully committed to.

Reduce to 3-5 campaigns maximum per platform
Target 128+ daily installs per AEO campaign
Use broad targeting; let creative segment audiences
Monitor blended channel-level CPAs, not campaign-level
Weight anomaly detection by spend magnitude, not just % change

Pre-ATT, splitting campaigns by audience segment, geo, creative theme, and optimization event was common. You might run 15-20 campaigns simultaneously on Meta for a single app. Post-ATT, this fragmentation starves each campaign of the conversion data it needs.

The fix is consolidation. Reduce to 3-5 campaigns maximum per platform. Use broad targeting (no interest stacks, no narrow lookalikes). Let creative variation do the audience segmentation implicitly.

This is exactly the approach recommended for early-stage apps: concentrate budget on one or two self-attributing networks rather than spreading $100-200/day across four channels.

RocketShip HQ’s Weighted Anomaly Scoring system helps here. When you consolidate, you need better monitoring to catch real performance shifts versus noise. We weight metric changes by business impact: abs(% change) × sqrt(spend). A 15% ROAS drop on $5K/day spend scores higher than a 40% drop on $200/day.

This eliminates over 70% of false alarms when you’re running fewer, larger campaigns.

On the measurement side, blended channel-level CPAs are now more reliable than campaign-level data. Track your topline metrics (total spend, total installs, blended CPA) first, then use SKAN and MMP data directionally to allocate between channels.

How has ATT affected mobile ad fraud?

ATT created new fraud vectors while reducing some old ones. According to AppsFlyer's Mobile Ad Fraud Report, iOS fraud rates initially dropped post-ATT as click injection became harder without device IDs, but new forms of SKAN manipulation emerged.

Our analysis of the AppsFlyer fraud report findings details the evolving landscape.

Key insight: ATT reduced old fraud methods like click injection but opened new attack surfaces through SKAN postback manipulation.

iOS install fraud dropped from ~15% to 8-10% post-ATT
Click injection became harder without IDFA
SKAN postback manipulation is a new fraud vector
Probabilistic attribution creates exploit opportunities
Statistical anomaly detection is now more important than device-level

The removal of IDFA made traditional click injection and click spamming harder because fraudsters lost the ability to match fake clicks to real installs at the device level. AppsFlyer reported that iOS install fraud rates dropped from ~15% pre-ATT to around 8-10% in 2023.

However, the shift to probabilistic attribution created new opportunities. Some networks inflate their attributed installs by exploiting the probabilistic matching windows. SKAN postback manipulation is another emerging vector where bad actors forge or replay conversion postbacks.

The practical implication: advertisers need to maintain fraud protection tools even as the fraud landscape shifts. The move to aggregated data makes it harder to catch fraud at the device level but easier to spot at the statistical level (unusual conversion patterns, impossible geo distributions).

What happened to retargeting and re-engagement campaigns after ATT?

iOS retargeting was devastated by ATT. According to AppsFlyer’s retargeting data, iOS re-engagement campaigns declined by ~50% in volume post-ATT, while Android retargeting conversions grew. Detailed benchmarks are covered in our summary of AppsFlyer’s retargeting report.

Key insight: Retargeting shifted from a reliable iOS revenue channel to an Android-dominant strategy almost overnight.

iOS retargeting volume dropped ~50% post-ATT
Android retargeting remains viable but faces Privacy Sandbox
Push notifications and email partially replace retargeting
In-session activation is more reliable than re-engagement
Protected Audiences API is Google’s FLEDGE replacement

Retargeting relied entirely on device-level identification. Without IDFA consent, building retargeting audiences from lapsed users, cart abandoners, or trial expirers became impossible for the ~80% of iOS users who opted out.

The workaround strategies are limited. Push notifications and owned channels (email, in-app messaging) partially replace retargeting for existing users. Some advertisers use Apple’s own tools (App Clips, custom product pages) to re-engage users without cross-app tracking.

On Android, retargeting remains viable with GAID, though Privacy Sandbox for Android will eventually constrain this too. The Protected Audiences API (formerly FLEDGE) is Google’s proposed replacement, running auctions on-device rather than server-side.

The strategic shift: invest more in onboarding and activation rather than retargeting. Converting users in-session is cheaper and more reliable than trying to win them back after they leave.

How has ATT affected iOS vs. Android ad spend allocation?

Many advertisers shifted budget toward Android post-ATT due to better measurability. According to data.ai's State of Mobile 2024, Android's share of global app install ad spend grew by ~8 percentage points between 2021 and 2024.

However, iOS users still generate 1.7x higher average revenue per user than Android users in most subscription app categories, per RevenueCat data.

Key insight: Chasing measurability by shifting all spend to Android sacrifices the highest-LTV users in most app categories.

Android’s share of install ad spend grew ~8 points post-ATT
iOS users generate 1.7x higher revenue in subscription apps
iOS trial-to-paid averages ~15% vs ~10% on Android
Use Android data to calibrate iOS measurement models
Android expansion makes sense for ad-monetized and gaming apps

Metric	iOS (Post-ATT)	Android	Source
IDFA/GAID Availability	15-25% opt-in	~85% available (declining)	AppsFlyer / Adjust
Avg. Revenue Per User (Subscription)	1.7x baseline	1.0x baseline	RevenueCat 2025
Trial-to-Paid Conversion	~15%	~10%	RevenueCat 2025
Attribution Method	SKAN 4.0 + modeled	Deterministic (for now)	Platform documentation
Retargeting Viability	Severely limited	Fully functional (for now)	AppsFlyer Retargeting Report

The temptation post-ATT was obvious: Android still had GAID, deterministic attribution, and real-time optimization signals. Many advertisers (especially those without sophisticated measurement stacks) moved 15-25% of their iOS budgets to Android.

This was often a mistake. iOS users in subscription categories convert to paid at higher rates and retain longer. According to RevenueCat's State of Subscription Apps, iOS trial-to-paid conversion rates average ~15% versus ~10% on Android for subscription apps.

The smarter approach is maintaining iOS investment but adapting the measurement framework. Build a post-ATT measurement framework that combines SKAN data with incrementality testing, and use Android’s deterministic data as a calibration layer for iOS models.

That said, Android budget expansion makes sense for apps where the LTV gap is narrow (games, ad-monetized apps) or in markets where Android dominates (Southeast Asia, Latin America, India).

What role does creative play in post-ATT mobile advertising?

Creative is now the single most important performance lever in mobile UA. With audience targeting degraded, the ad creative itself determines who responds and converts. Industry data suggests that creative variation accounts for 70-80% of campaign performance variance post-ATT, according to analysis by MobileDevMemo's Eric Seufert.

Key insight: Creative replaced audience targeting as the primary optimization variable. Your ad IS your targeting.

Creative accounts for 70-80% of campaign performance variance
Test 10-20 new concepts monthly at $50K+ spend
Creative self-selects audiences when targeting is broad
SKAN campaign ID limits constrain creative testing granularity
Custom Product Pages matched to ad themes lift CVR 10-15%

When broad targeting is the norm (as it is on Meta’s Advantage+ and Google’s UAC), the algorithm uses creative engagement signals to determine who sees the ad. A UGC testimonial about weight loss attracts a different user than a polished product demo showing calorie tracking. The creative self-selects the audience.

This means creative volume and diversity matter more than ever. You need multiple concepts running simultaneously, each speaking to a different user motivation. Testing velocity should be 10-20 new creative concepts per month for apps spending over $50K/month on paid UA.

SKAN’s campaign ID limits create a tension here: you want creative diversity, but SKAN restricts how many campaigns (and therefore creative variants) can receive attributed data. SKAN 4.0’s hierarchical source identifiers help, but advertisers still need to group creatives into thematic buckets rather than testing at the individual asset level.

Custom Product Pages on the App Store (up to 35 variants) offer a complementary lever. Matching ad creative themes to corresponding CPPs lifts conversion rates by 10-15%, according to Apple Search Ads case studies.

What does privacy-first attribution look like in 2026?

Privacy-first attribution in 2026 combines SKAN 4.0, server-side events APIs, MMPs running probabilistic models, and periodic incrementality tests. No single method replaces the pre-ATT IDFA system. According to AppsFlyer, 68% of top-100 advertisers now use at least three attribution methods simultaneously.

Our comprehensive privacy-first attribution guide covers the full framework.

Key insight: There is no single replacement for IDFA. Triangulating multiple imperfect signals is the only viable approach.

68% of top advertisers use 3+ attribution methods
SKAN 4.0 provides campaign-level directional data
MMP probabilistic models are ±15-20% accurate
Monthly incrementality tests validate attributed data
Server-side events APIs recover lost client-side signals

The “triangulation” approach has become standard. Here’s how sophisticated advertisers structure it in 2026:

Layer 1: SKAN 4.0 for iOS campaign-level directional data. Use the three postback windows to understand early conversion patterns. Accept that low-volume campaigns will receive minimal data due to crowd anonymity thresholds.

Layer 2: MMP probabilistic attribution for near-real-time channel-level trends. AppsFlyer, Adjust, and Singular all offer modeled conversions that fill gaps in SKAN data. The accuracy varies by category and is generally within ±15-20% of ground truth, per Singular’s SKAN benchmarks.

Layer 3: Incrementality testing for ground truth. Run geo-based or time-based holdout tests monthly to validate that your attributed data matches actual business impact. This is expensive (you’re intentionally not spending in test markets) but essential.

Layer 4: Server-side events APIs (Meta’s CAPI, Google’s server-to-server, TikTok’s Events API) feed first-party conversion data directly to platforms, recovering some of the signal lost from client-side tracking.

For apps approaching this for the first time, start with setting up a post-ATT measurement framework and build complexity incrementally.

Is ATT coming to Android through Google’s Privacy Sandbox?

Yes, but differently. Google’s Privacy Sandbox for Android will deprecate the GAID and replace it with privacy-preserving APIs, though the timeline has been pushed repeatedly.

According to Google's developer documentation, the Topics API and Attribution Reporting API are in testing, with full GAID deprecation expected by late 2026 or 2027.

Key insight: Android’s privacy changes will be less abrupt than ATT but will ultimately produce similar measurement constraints.

GAID deprecation expected late 2026 or 2027
Topics API replaces interest targeting with broad categories
Protected Audiences API preserves some retargeting capability
Attribution Reporting API is more granular than SKAN
Build measurement infrastructure now while GAID is available

Google’s approach differs from Apple’s in three key ways. First, it’s more gradual: Google is running parallel systems (GAID and Privacy Sandbox) rather than flipping a switch. Second, the replacement APIs are more advertiser-friendly: the Attribution Reporting API provides more granular data than SKAN.

Third, Google has been collaborating with the ad industry throughout development.

The Topics API replaces third-party cookie-based interest targeting with a system where the browser/OS assigns users to broad interest categories (“Fitness,” “Travel”) without exposing individual behavior. This is more useful than no signal but far less precise than GAID-based targeting.

The Protected Audiences API (formerly FLEDGE) enables on-device retargeting auctions without sharing user data with servers. This preserves some retargeting capability that iOS lost entirely.

Smart advertisers are preparing now. Use the current GAID period to build predictive models, establish Android benchmarks, and develop the measurement infrastructure (server-side events, incrementality testing) that you’ll need when GAID goes away.

ATT didn’t end mobile advertising. It ended one era of mobile advertising and started another.

The marketers winning in 2026 are those who embraced creative as the primary targeting lever, built triangulated measurement stacks, consolidated campaigns for algorithmic efficiency, and prepared their Android infrastructure for the same privacy constraints coming next.

Frequently Asked Questions

Can I still run remarketing on iOS after ATT?

Very limited. iOS retargeting volume dropped ~50% post-ATT, according to AppsFlyer. You can retarget the 15-25% who opted in, use owned channels (push, email), or leverage Apple’s on-platform tools like custom product pages. Cross-app retargeting for opted-out users is effectively dead.

Does ATT affect web-to-app campaigns?

Yes. ATT blocks the IDFA handoff from web to app, breaking deferred deep linking for opted-out users. According to Adjust, web-to-app campaign attribution accuracy dropped by ~40% post-ATT. Server-side events APIs and Apple's Private Click Measurement partially mitigate this.

What’s the difference between SKAN 3.0 and SKAN 4.0?

SKAN 4.0 added hierarchical source identifiers (up to 4 digits), three postback windows (instead of one), and coarse conversion values for lower-volume campaigns, per Apple's documentation. The practical improvement is ~35% more usable data points for advertisers running diverse campaigns.

Should I use a pre-permission prompt before the ATT dialog?

Absolutely. Apps using a well-designed pre-permission screen see opt-in rates 10-15 percentage points higher than those showing Apple’s prompt cold, according to RevenueCat. Explain the value exchange clearly: better ad relevance, supporting free content, or personalizing the experience.

How does ATT impact subscription app economics specifically?

Subscription apps were hit hard because they rely on measuring trial-to-paid conversion, which takes days or weeks. SKAN's delayed postbacks and coarse conversion values make it difficult to optimize toward paid subscribers. Per RevenueCat, subscription apps using predictive early-funnel signals (like onboarding completion) as SKAN conversion events see 20-30% better optimization outcomes.

Has ATT actually improved user privacy?

Partially. Cross-app tracking by third parties declined dramatically. However, Apple’s own advertising platform (Apple Search Ads) gained competitive advantage since it retained deterministic attribution. According to the Financial Times, Apple’s ad revenue grew over 4x in the two years following ATT’s launch, raising questions about whether the policy was purely privacy-motivated.

What budget threshold do I need for reliable SKAN data?

Campaigns need at least 128+ daily installs to consistently receive non-null conversion values from SKAN, as discussed in post-ATT strategy analysis. At a $2.00 CPI, that's roughly $256/day minimum per campaign. Anything below that threshold risks receiving mostly null postbacks.

Will fingerprinting work as an ATT workaround?

No. Apple explicitly prohibits fingerprinting (using device signals like IP, screen size, and OS version to identify users) in their developer guidelines. Apps caught fingerprinting risk App Store removal. In January 2025, Apple began enforcing "Required Reason" APIs that further restrict access to signals commonly used for fingerprinting.

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Shamanth

Shamanth Rao is the founder of RocketShip HQ, a performance creative and growth marketing agency helping mobile apps scale through ad creatives, experimentation, and data-driven marketing systems. With over a decade in mobile user acquisition, he has managed growth for apps with hundreds of millions of installs across Meta, TikTok, Apple Search Ads, and Google. He hosts the Mobile User Acquisition Show podcast and has spoken at MAU, Pocket Gamer Connects, and App Promotion Summit.