8 Reasons Most UA Strategies Fail as they Scale

Your ROAS is like an iceberg. The biggest killers are invisible. UA teams need to dig below the surface to save their ROAS.

  The full conversation with Kohort’s Nilay and Bruno on what they see as the biggest traps to UA scaling. 1. Giving paid networks credit for organics  Many studios misattribute organic installs to paid channels when they analyse performance, inflating metrics that crumble at scale.

  The fix? Use regression analysis to separate baseline organics (app store features, brand) from incremental organics that paid channels actually generate. Stop crediting networks for users who would have come anyway.

2. ROAS Curves ≠ Universal Laws Assuming ROAS curves behave consistently across geos, platforms, and campaign types is expensive. You're most vulnerable when you have the least data, launching new networks or geos.

Don’t just use ratios from one channel on a new channel. For example, a team with SDK network data will catastrophically overestimate returns on rewarded networks, where ROAS peaks much earlier and then flatlines.   Accurate prediction requires stable traffic data, clear confidence intervals, and humility about operating outside your training set.

Otherwise, you're simply gambling. 3.   When you tell Meta to hit 12% ROAS at day seven, are you looking for gross or net returns?

Your MMP doesn’t do any conversion by default -  IAP is usually gross, IAA is always net, and networks have no idea what you actually need. The difference? App store fees and VAT can reduce returns by 30-40%.

So, Meta giving you 100% ROAS does not equal a happy CFO.   Solutions are painful: convert all MMP integrations to net (breaks campaigns during transition) or build a data layer to track true net ROAS . Without alignment, you'll hit revenue targets at 63 cents on the dollar.

And just to be clear, that’s a miss. 4. Connecting LTV and CPI, revenue, and spend Higher spending doesn't just raise CPI; it changes the CPI-LTV relationship unpredictably.

As you scale beyond core audiences, CPI rises - everyone knows this. But LTV can also change (higher CPIs can mean higher-quality users, or you could start targeting users that aren’t in your core audience and retain less).   Kohort's analysis of $6 billion in UA spend shows elasticity varies dramatically between IAP/IAA apps, geos, and campaign types.

The iron triangle of marketing: you can't control spend, profit margins, and acquisition costs simultaneously. Pick two, negotiate the third. 5.

Signal Engineering: The New UA Meta Most teams set one global day-seven ROAS target across all campaigns, Meta, AppLovin, and rewarded networks, ignoring that each has radically different user behaviors and conversion curves. This one-size-fits-all approach does not scale because every developer is going after the same traffic, users who are likely to make a purchase, with the same targets.   To access traffic that no one else is looking at requires UA and product teams to identify the ideal user journey, then translate those insights into custom MMP events .

If users who log in on day three become your best cohorts, instrument that path. The organizational trap? Product teams A/B test features and accidentally destroy UA signals, while UA managers aren't invited to product meetings.

6. The Single Source of Truth  MMPs can be blind to some user behavior: re-engagement, post-day-180 activity, cross-platform play, and web store purchases.   Start with the biggest problem: Apple’s SKAN.

If you can match Android spend visibility on iOS, you've solved the critical blindness for most studios. Use opt-in data as a proxy, then find similarity patterns among non-opt-in users to classify them. Re-engagement and post-day-180 attribution offer 10-20% upside, meaningful, but secondary to fixing SKAN first.

Cross-platform attribution is a headache - it often requires significant data investment - but it could make enough difference to empower an omni-channel Marketing strategy. Getting your data under control is often the best use for your UA dollars. 7.

Bad Metrics Create Bad Confidence Accuracy gets all the focus, but confidence matters equally. One network delivers $10-15 LTV consistently; another swings between $1-100.   Great studios use MAPE (mean absolute percentage error) to constantly back-test predictions against actual performance.

But data science teams must also arm UA managers with confidence intervals; your predictions are always just predictions. High noise recently? Let campaigns mature another week before making changes.

MAPE lives with data science as your batting average before making the call. 8.   Bruno's rule: never make more than one change per conversion window.

Optimizing toward day-seven ROAS? Wait seven days between changes so algorithms can learn.   The 7-20 rule feels counterintuitive when performance looks bad (a 20% cut seems stupid), but knee-jerk reactions extend the learning process and stunt growth .

Let algorithms learn. Your impatience costs more than bad performance. Philosophical Musing: The Future of Predictive ROAS Most "predictive models" are spreadsheets with lipstick, but that's changing fast.

Ad networks will shift to tROAS-only campaigns with longer windows. AppLovin already moved from day seven to day 28, and the trend points toward day 365.   The role evolves: UA managers become auditors who manage multiple ad networks running autonomous campaigns, using independent data to verify network-reported performance.

  The future of UA is feeding algorithms the right signals, then auditing the results. : If any of the topics in this podcast are keeping you up at night, reach out to ​ Kohort ​ to access their machine learning analytics platform. It solves the tricky data science behind optimisation targets, new segment launches, and organics, so you can focus on growing your app.

This is not a paid ad. I just know the folks at the company have clients who used the platform and like them a lot.