Most mobile marketers assume cheaper installs are better installs. The SKAN data says otherwise.

Published by Shamanth at April 1, 2026

Step 1: Why do you need a layered measurement framework instead of just one tool?

Because no single measurement method gives you accurate, actionable data across all dimensions after ATT. Each layer has a specific job and a specific blind spot.

SKAN gives you channel-level install attribution on iOS but has timer delays of 24-48+ hours, limited conversion value granularity (64 values in SKAN 4.0's fine-grained mode, fewer at lower crowd anonymity thresholds per Apple's SKAdNetwork documentation), and zero user-level data.

Your MMP fills some gaps with probabilistic modeling and cross-platform views, but post-ATT accuracy on iOS has degraded significantly. MMM gives you portfolio-level spend allocation insights but cannot optimize individual campaigns. Incrementality testing gives you causal proof of lift but is expensive and slow.

The layered framework assigns each method to what it does best.

What specific blind spot does each layer have?

SKAN cannot measure post-install events beyond its three postback windows (the third window, days 8-35, often returns only coarse values).

Your MMP's modeled iOS data can drift materially from reality at the campaign level — in our experience comparing MMP-reported iOS ROAS against backend revenue matched by cohort date, the gap can be substantial and varies by network and campaign type.

MMM requires 12-18 months of historical data and cannot react to week-over-week changes. Incrementality tests require statistical significance, meaning you need to commit budget to a holdout for 2-4 weeks per test. Knowing these gaps tells you exactly where to trust each source.

How do you assign decision authority across layers?

Think of it as a hierarchy with incrementality at the top. When incrementality test data is available and recent (within 60 days), it overrides all other sources because it measures causation. For monthly or quarterly strategic allocation, MMM takes precedence, informed by incrementality priors.

For daily and weekly campaign optimization on iOS, use SKAN postbacks directly. Your MMP dashboard serves as the cross-platform operational view, calibrated against SKAN and validated by incrementality. In our experience, teams using all four layers with this hierarchy consistently see meaningful improvement in true ROAS versus teams relying on MMP-reported data alone.

The single biggest mistake teams make is treating their MMP dashboard as ground truth on iOS. After ATT, MMP-reported iOS ROAS can diverge significantly from true campaign performance. Use it as a directional signal, not an optimization target.

Step 2: How do you set up SKAN as your iOS channel-level attribution layer?

Start by configuring your SKAN conversion value schema in your MMP to maximize the signal you extract from Apple's limited 6-bit (64-value) system. This is your most reliable source of deterministic iOS attribution, even with its limitations.

According to Apple's developer documentation, SKAN 4.0 introduced three postback windows (0-2 days, 3-7 days, 8-35 days) with decreasing granularity based on crowd anonymity.

Your job is to design a conversion value mapping that captures the revenue or engagement signal that matters most for your business within the first window, because that is where you get the most granular data.

What conversion value schema works best for revenue-driven apps?

For subscription, ecommerce, and fintech apps, map your 64 fine-grained values to revenue buckets covering your LTV distribution.

For subscription apps, a common approach is to allocate lower values for engagement milestones (registration, onboarding complete, key feature used), mid-range values for revenue ranges spanning your typical 7-day LTV distribution, and the highest values for outlier high-value conversions.

Your conversion value timer resets each time you call updatePostbackConversionValue, so set progressively higher values as the user progresses. For gaming apps, broad targeting combined with tight SKAN measurement can keep CPIs competitive.

According to data.ai's 2024 advertising benchmarks, casual gaming CPIs on iOS average $1.50–$3.00, but hyper-casual titles with well-tuned SKAN schemas optimizing for early-retention signals can push below $1.00.

How do you handle SKAN null values and low crowd anonymity?

Null conversion values are your biggest data loss in SKAN. According to Singular’s null rates analysis, campaigns below Apple’s crowd anonymity thresholds can see null rates jump to 60%+, with gaming at 47%. For campaigns below Apple’s crowd anonymity thresholds, you only receive coarse-grained values (low, medium, high).

To mitigate this, consolidate campaigns to push more installs through fewer SKAN campaign IDs (you have 100 per network in SKAN 4.0). Also implement server-side null value modeling: use your known conversion value distribution from non-null postbacks to probabilistically assign values to nulls, weighting by network and campaign type.

Never treat SKAN conversion values as exact revenue. They are bucketed ranges. When reporting SKAN ROAS, use the midpoint of each bucket and clearly label it as estimated. Midpoint estimation introduces some error versus backend revenue, but this is generally acceptable for channel-level allocation decisions.

Step 3: How do you configure your MMP as the cross-platform operational layer?

Your MMP (AppsFlyer, Adjust, Singular, Kochava) serves as the unifying dashboard that stitches together SKAN postbacks, Android deterministic data, web-to-app flows, and modeled iOS conversions into a single operational view. The key configuration decision is how your MMP handles the gap between SKAN-attributed installs and total installs.

Most MMPs now offer a "single source of truth" (SSOT) mode that deduplicates SKAN postbacks against their own modeled attribution. According to AppsFlyer's SSOT documentation, this deduplication can reduce double-counting by 15-25% compared to running SKAN and last-touch attribution in parallel.

How should you set up MMP postback and event configurations?

Configure your MMP to receive SKAN postbacks from all ad networks (Meta, Google, TikTok, Snap, Unity, ironSource, AppLovin). Set up server-to-server event forwarding for all post-install events: purchases, subscriptions, registrations, and key engagement events.

On Android, maintain full deterministic event forwarding since Android attribution remains largely intact, though Privacy Sandbox for Android will eventually change this.

Set your MMP's attribution window to match your SKAN postback windows for consistency: 1-day view-through and 7-day click-through is standard for most networks per Meta's attribution settings documentation.

How do you calibrate MMP data against SKAN weekly?

Run a weekly comparison of SKAN-reported installs and CPA by network versus MMP-reported installs and CPA. Build a calibration spreadsheet tracking the discrepancy ratio per network over time.

We commonly observe that MMP-reported and SKAN-reported install counts diverge by network — some networks tend to over-report relative to SKAN while others under-report, and smaller networks can show particularly wide variability.

Use these ratios as adjustment multipliers when making budget decisions, updating them monthly as network algorithms and MMP models evolve.

Check your MMP's fraud detection settings. According to AppsFlyer's 2024 State of Ad Fraud report, iOS fraud exposure rates dropped post-ATT to approximately 2-4% (since there is less incentive to steal deterministic attribution), but Android financial app fraud exposure rates remain elevated at 10-15% for some ad networks. See our fraud report summary for detailed benchmarks. Make sure Protect360 or your MMP's equivalent is active and tuned.

Step 4: How do you build a Media Mix Model (MMM) for portfolio-level allocation?

MMM is your strategic allocation layer that answers the question: "If I shift $50K from Channel A to Channel B, what happens to my total revenue?" Unlike attribution models that assign credit to touchpoints, MMM uses regression analysis on historical data to estimate each channel's marginal contribution.

For mobile apps, you need 12-18 months of weekly data at minimum to build a reliable model, though app install ad spend 2025 demonstrates why portfolio-level measurement has become essential across the industry. According to Eric Seufert’s practitioner guide to MMM at MobileDevMemo, this historical depth allows the model to capture seasonality, campaign saturation effects, and channel interaction dynamics that shorter timeframes miss.

Lightweight, open-source MMM tools like Meta's Robyn and Google's Meridian have made this accessible to teams spending as little as $100K/month, though accuracy improves dramatically above $500K/month where you have more variance in spend levels to learn from.

What data inputs does your MMM need?

At minimum: weekly spend by channel, weekly installs or revenue from your backend (not your MMP, to avoid attribution bias), and external variables including seasonality indicators, app store featuring dates, organic ranking changes, PR or viral events, and competitor activity.

For subscription apps, use weekly new subscriber revenue or trial starts as the dependent variable, not installs, because install volume can be misleading if quality differs across channels. Include lagged variables: a TikTok campaign this week may drive installs next week.

In our experience, including 1-week and 2-week lagged spend variables meaningfully improves model fit for channels with longer consideration cycles like connected TV or influencer, where the relationship between spend and conversion is rarely contemporaneous.

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

How do you validate and calibrate your MMM outputs?

Never trust an uncalibrated MMM. Use incrementality test results (covered in the next step) as Bayesian priors or validation benchmarks. For example, if your incrementality test showed that TikTok drives 60% incremental lift versus its MMP-reported conversions, feed that as a prior into Robyn's calibration inputs.

Compare MMM-predicted outcomes to actual outcomes on a rolling 4-week holdout: if your model says cutting Facebook spend by 20% should reduce revenue by X, and you actually reduced spend and observed Y, the ratio X/Y is your calibration factor.

According to Meta's research blog on Robyn, calibrated models reduce mean absolute percentage error (MAPE) by 20-40% compared to uncalibrated ones.

If you do not have 12+ months of data, start with a simplified regression using 6 months of weekly data and 3-5 channels. The model will not be robust, but it will surface the biggest directional insights (for example, which channel has the steepest diminishing returns curve) while you accumulate data for a full MMM.

Step 5: How do you design and run incrementality tests for causal validation?

Incrementality testing is the only measurement layer that proves causation rather than correlation. Run geo-holdout tests or on/off tests to measure the true incremental lift of a channel or campaign.

The standard approach is to select matched geographic regions, run ads in treatment regions while withholding spend in control regions for 2-4 weeks, then compare conversion rates.

You need a minimum detectable effect (MDE) of 5-10% and at least 2 weeks of test duration to achieve statistical significance at 90% confidence for most mobile app campaigns. Adjust’s 2025 benchmarks, your incrementality test design should account for normal seasonal variance in baseline install rates when selecting test duration and geo pairs.

How do you select test and control geos?

Choose geo pairs that are demographically and behaviorally similar. In the US, common matched pairs include Dallas/Houston, Seattle/Portland, and Philadelphia/Pittsburgh. Match on baseline organic install rates, average revenue per user, and population demographics.

In our experience, mismatched geo pairs can introduce significant noise into test results, making it difficult to isolate true lift. Use at least 4 weeks of pre-test data to confirm that your treatment and control geos track within 5% of each other on key metrics before launching the test.

How do you calculate incremental lift and apply results?

Incremental lift equals (treatment conversions minus control conversions) divided by control conversions. Calculate incremental CPA as total test spend divided by incremental conversions.

Apply results by creating an "incrementality multiplier" per channel: if Meta drives 1,000 MMP-attributed conversions but incrementality testing shows only 700 are truly incremental, your multiplier is 0.70. Feed these multipliers into your MMM as calibration priors and into your weekly budget allocation model.

Refresh incrementality tests every 60-90 days per channel, because platform algorithm changes and creative rotation affect true incrementality over time.

Start incrementality testing with your largest spend channel first, because that is where misallocation costs you the most. A 10% measurement error on a $500K/month channel is $50K/month in potential waste, which easily justifies the cost of running a holdout test.

Step 6: How do you build the unified reporting layer that ties everything together?

Create a single reporting dashboard that pulls data from all four layers and applies your calibration multipliers automatically. This dashboard is your actual decision-making surface.

Use a data warehouse (BigQuery, Snowflake, or even a well-structured Google Sheet for smaller teams) that ingests SKAN postback data from your MMP's API, MMP-reported cross-platform data, MMM output coefficients, and incrementality test results.

The dashboard should show each channel with three columns: MMP-reported metrics, SKAN-adjusted metrics (using your weekly calibration ratios), and MMM/incrementality-adjusted metrics.

What should the dashboard schema look like?

Channel	MMP Installs	SKAN Installs	Calibration Ratio	Incrementality Multiplier	Adjusted True Installs	Adjusted CPA	MMM Marginal CPA
Meta	12,000	10,200	0.85	0.70	7,140	$4.20	$5.10
Google UAC	8,000	8,800	1.10	0.85	7,480	$4.01	$4.50
TikTok	5,000	4,200	0.84	0.75	3,150	$6.35	$7.20
Apple Search Ads	3,500	3,400	0.97	0.90	3,060	$3.27	$3.80

This illustrative schema lets you see at a glance where MMP over-reports (Meta), where it under-reports (Google UAC), and what the true cost of each channel looks like after adjusting for incrementality.

How often should you update each data source?

SKAN postbacks and MMP data should refresh daily. Update your SKAN-to-MMP calibration ratios weekly. Refresh your MMM coefficients monthly or when you add/remove a channel. Run incrementality tests every 60-90 days per major channel (rotate so you are always testing something).

This cadence ensures your calibration stays current without overwhelming your team. Flag any calibration ratio that shifts more than 15% week-over-week for investigation, as this usually indicates a network algorithm change or a tracking issue.

Automate the calibration ratio calculation with a simple script that compares SKAN and MMP install counts by network each Monday morning and flags outliers. Manual weekly comparison takes 2-3 hours; an automated pipeline takes 15 minutes of review.

Step 7: How do you use this framework to make actual budget allocation decisions?

The framework's purpose is to give you defensible answers to two questions: "Should I spend more or less on Channel X?" and "Where should my next marginal dollar go?" Use your MMM marginal CPA curves to identify the point of diminishing returns for each channel.

Channels where your current spend is well below the inflection point on the diminishing returns curve are candidates for scale. Channels where you are past the inflection point are candidates for cuts.

How do you run a weekly budget optimization cycle?

Every Monday, pull the unified dashboard. Compare each channel's incrementality-adjusted CPA against your target CPA. For channels exceeding target by more than 15%, reduce daily budget by 10-20% and reallocate to channels below target. For channels below target by more than 20%, increase budget by 15-25% in weekly increments.

Do not make large swings: according to Meta's best practices for campaign optimization, budget changes exceeding 20% in a single day can reset the learning phase and degrade performance for 3-5 days. Track the outcome of each reallocation over a 2-week window before making further adjustments.

How do you present this framework to stakeholders?

Stakeholders need a simple narrative, not a four-layer data architecture diagram. Present a monthly "measurement confidence" report with three sections: (1) Channel rankings by incrementality-adjusted ROAS (your most trustworthy metric). (2) A delta table showing where MMP-reported performance differs from adjusted performance and by how much.

(3) Recommended budget shifts with expected incremental revenue impact, modeled from your MMM. Frame it as: "Our MMP says Meta ROAS is 2.5x. After adjusting for incrementality and SKAN calibration, true ROAS is closer to 1.8x, which is still above our 1.5x threshold but leaves less headroom than it appears."

Always sanity-check your framework's recommendations against a simple organic multiplier analysis: if you pause a channel for 48 hours and organic installs spike (cannibalization) or stay flat (the channel was truly incremental), you have a quick gut-check on whether your model is directionally correct.

Step 8: How do you handle retargeting measurement within this framework?

Retargeting measurement became significantly harder post-ATT because you can no longer target or measure specific user segments on iOS with the same precision. According to the AppsFlyer app retargeting report, retargeting adoption has declined on iOS but remains a meaningful channel on Android.

Within your layered framework, retargeting sits primarily in the MMP and incrementality layers. SKAN does not help here since it only measures installs, not re-engagements.

How do you measure retargeting incrementality?

Use ghost ad or intent-to-treat holdout designs: randomly assign eligible retargeting audiences into a treatment group (who see ads) and a control group (who do not). Compare re-engagement and purchase rates between groups.

In our experience, Android retargeting tends to deliver meaningful incremental lift, while iOS retargeting (limited to consented users) commonly delivers more modest incremental lift due to smaller audience pools and higher overlap with organic re-engagers.

On iOS, consider shifting retargeting budgets toward owned channels (push notifications, email, in-app messaging) where you have deterministic reach, and reserve paid retargeting budgets for Android where measurement and targeting remain more intact.

Step 9: How do you use Lookalike audiences and creative testing within this measurement framework?

Your measurement framework should inform both audience strategy and creative testing. On Meta, Lookalike audiences still work but their effectiveness has shifted post-ATT. The framework helps you measure whether expanding from a 1% to 5% Lookalike actually drives incremental installs or just captures users who would have converted organically.

Use your SKAN conversion value data to build seed audiences based on high-value users (those hitting your top conversion value buckets), then measure the resulting SKAN CPA against your calibrated benchmarks.

How does the framework improve creative testing decisions?

Use SKAN conversion value distributions to evaluate creative performance. A creative that drives high install volume but concentrates in low conversion value buckets is worse than one with fewer installs but higher average conversion values.

Pull the conversion value distribution per ad or ad set from your MMP's SKAN dashboard, and compare the estimated revenue per install.

In our experience, creatives optimized using SKAN revenue signals consistently deliver higher true ROAS than creatives optimized purely on install volume, even though the latter often show lower CPI—meaning cheaper installs are not necessarily better installs.

When testing new creatives, allow at least 50-100 SKAN postbacks per variant before making performance calls. With SKAN's timer delays and null rates, snap judgments on small sample sizes lead to false negatives that kill promising creatives prematurely.

Step 10: How do you prepare this framework for upcoming privacy changes?

Your layered framework is designed to be privacy-durable, but two upcoming shifts require preparation. First, Google’s Privacy Sandbox for Android will eventually restrict Android’s Advertising ID, which means your Android MMP data will face similar degradation to what iOS experienced.

Second, Apple may evolve SKAN further or introduce new privacy constraints. Build your framework with the assumption that deterministic, user-level data will continue to shrink on both platforms.

What concrete steps should you take now?

Invest in server-side event infrastructure so your backend data (not SDK-dependent data) becomes your source of truth for MMM and incrementality. Start building your Android MMM now while you still have deterministic data, because the historical data you collect today will be invaluable for calibration after Privacy Sandbox rolls out.

As detailed in our privacy-first attribution guide, teams that build server-to-server event pipelines now will have a 6-12 month head start when Android attribution degrades. Test Privacy Sandbox APIs (Topics, Attribution Reporting) in beta to understand their granularity and limitations before they become your primary Android signal.

According to Google's Privacy Sandbox timeline, Android Advertising ID deprecation is expected to follow a gradual rollout. Start dual-running Privacy Sandbox Attribution Reporting alongside your current MMP setup now so you have comparison data when the switch happens.

Common Mistakes to Avoid

Mistake 1: Treating MMP-reported iOS ROAS as ground truth. In our experience, MMP-reported iOS campaign ROAS can diverge meaningfully from SKAN-calibrated ROAS. Teams that optimized purely on MMP numbers consistently over-invested in channels that over-reported and under-invested in channels that under-reported, resulting in materially lower true ROAS than teams using calibrated data.
Mistake 2: Running too many SKAN campaign IDs and fragmenting crowd anonymity. Apple’s crowd anonymity thresholds require minimum install volume per campaign ID to receive fine-grained conversion values. According to Singular’s SKAN benchmarks, campaigns below the threshold can see null rates jump from 25% to 60%+. Consolidate to fewer campaign IDs, even if it means less granular campaign structure in your ad account.
Mistake 3: Building an MMM without including lagged variables or external factors. A model that only regresses spend on outcomes ignores that TikTok and influencer campaigns often have 1-2 week delayed effects, and that seasonality or app store featuring can account for a significant share of revenue variance. Omitting these variables inflates or deflates channel contribution estimates.
Mistake 4: Running incrementality tests for less than 2 weeks or with poorly matched geo pairs. Short tests lack statistical power, and mismatched geos introduce bias. In our experience, tests under 14 days frequently produce lift estimates that differ substantially from properly designed 3-4 week tests with matched pairs, leading to wrong budget decisions.
Mistake 5: Updating the framework once and then neglecting it. Calibration ratios between SKAN and MMP data shift as networks update algorithms and as Apple adjusts crowd anonymity thresholds. We've observed that calibration ratios can drift materially from quarter to quarter, meaning a ratio calculated in Q1 can be meaningfully wrong by Q3 if not refreshed.
Mistake 6: Ignoring Android measurement because “it still works.” With Privacy Sandbox for Android approaching, teams that do not build layered measurement for Android now will face the same scramble iOS teams experienced in 2021. Start collecting the historical data and building the infrastructure before you lose deterministic Android signals.
Mistake 7: Over-investing in incrementality testing at the expense of action. Some teams run tests continuously on every channel but never apply the results to budget allocation. Incrementality data depreciates: a test result from 6 months ago reflects a different algorithm, creative mix, and competitive landscape. Apply results within 30 days, then plan the next test cycle.

Building a layered measurement framework is no longer optional for mobile app teams spending meaningful budgets post-ATT.

Start with SKAN configuration and MMP calibration (week 1-2), then build your unified dashboard with calibration ratios (week 3-4), then scope your first incrementality test on your largest channel (week 5-8), and finally begin collecting data for your MMM (ongoing, with a first usable model at month 4-6).

The teams that invest in this infrastructure now will have a compounding advantage as privacy restrictions tighten further on both iOS and Android.

In our experience, the first month of operating with calibrated data typically surfaces at least one channel where true CPA is meaningfully different from MMP-reported CPA, which alone justifies the effort.

If you need hands-on help, explore our privacy-first attribution guide for deeper technical detail, or contact RocketShip HQ for a full measurement audit.

Frequently Asked Questions

How much does a layered measurement framework cost to implement?

The primary costs are MMP fees ($500–$10,000+/month depending on install volume per AppsFlyer's pricing tiers), MMM tooling (free if using Meta Robyn or Google Meridian, $5K-$20K/month for managed solutions), incrementality test budget (holdout costs of 10-15% of a channel's budget during the test window), and analytics engineering time (typically 40-80 hours for initial setup).

For teams spending $100K-$500K/month, total framework cost is roughly 3-5% of ad spend.

Can I use this framework if I only spend on one or two channels?

Yes, but MMM becomes less useful with fewer channels because there is less variance for the model to learn from. Focus on SKAN configuration, MMP calibration, and incrementality testing.

Even with just Meta and Apple Search Ads, an incrementality test on Meta can reveal whether 20-40% of your attributed conversions are cannibalized from organic, which directly changes your true CPA calculation and scaling decisions.

How does this framework work for Android-only or Android-heavy apps?

Android still has deterministic attribution via the Advertising ID, so your MMP layer is more reliable. However, you should still layer in MMM and incrementality testing because last-touch MMP attribution on Android still misattributes due to view-through overlap, self-reporting network discrepancies, and fraud.

According to AppsFlyer's 2024 fraud report, Android fraud exposure rates remain significantly higher than iOS post-ATT, making incrementality testing even more critical for validating true channel contribution.

What should I do if my incrementality test results contradict my MMM outputs?

Incrementality wins for the specific channel tested, because it measures causation. Update your MMM with the incrementality result as a calibration prior and re-run the model. If the contradiction persists across multiple channels, your MMM likely has a specification error: missing external variables, wrong lag structures, or multicollinearity between channels.

A common cause is that the MMM is not accounting for a channel’s influence on organic installs (the “halo effect”), which incrementality tests capture by design.

How do I handle measurement for web-to-app campaigns or deep-linked flows?

Web-to-app flows bypass some SKAN limitations because you can capture web click data before the app install. Configure your MMP's deep linking and deferred deep linking with server-side attribution. Then reconcile web campaign spend against app-side outcomes in your unified dashboard.

The key challenge, per our privacy-first attribution guide, is matching the web session to the app install without relying on deprecated cross-context identifiers. Use first-party cookies and server-side event matching.

Is SKAN 5.0 or Apple's AdAttributionKit going to make this framework obsolete?

No. Apple's evolution of SKAN (now called AdAttributionKit in iOS 17.4+) adds features like re-engagement attribution and broader developer support, but it does not restore user-level data.

The framework remains necessary because no single Apple-provided signal will give you portfolio-level allocation insights (that is MMM's job) or causal proof (that is incrementality's job). The SKAN layer of your framework will evolve, but the four-layer structure is privacy-durable by design.

How do I get engineering resources allocated to build this framework?

Frame it as revenue protection, not measurement improvement. Calculate the dollar value of misallocation: if you spend $300K/month and your calibration data shows 20% misallocation, that is $60K/month or $720K/year flowing to the wrong channels.

A 40-80 hour engineering investment to build the unified dashboard and automate calibration ratios pays for itself within the first month of corrected allocation. Present the specific dollar figure, not the technical architecture.

Can RocketShip HQ help implement this framework?

Yes. RocketShip HQ builds and manages layered measurement frameworks as part of our mobile UA services. We handle SKAN schema design, MMP calibration automation, MMM setup using Robyn or custom models, and incrementality test design and analysis. Reach out at rocketshiphq.com for a measurement audit.

Looking to scale your mobile app growth with performance creative that delivers results? Talk to RocketShip HQ to learn how our frameworks can work for your app.

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Privacy-first attribution and measurement for mobile apps (comprehensive guide)
AppsFlyer App Retargeting Report: Benchmarks and Post-ATT Strategies (2026)
AppsFlyer Mobile Ad Fraud Report: Fraud Rates and Protection Benchmarks (2026)
How to Use Lookalike Audiences for Mobile App UA on Meta
Privacy-first attribution and measurement for mobile apps

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.