Accurate ROAS calculation with obscured attribution - possible?

Our finance team keeps questioning marketing spend attribution. Mobile platforms show installs but miss which campaigns drive actual subscriptions. Started using web paywalls with preserved UTMs across the entire funnel - suddenly saw TikTok ads were 3x more effective than Applovin. How are others handling cross-channel ROAS when app stores won’t share the data? Any tricks to validate accuracy beyond last-click models?

Switched to server-side conversion tracking through Web2Wave’s paywalls. Pass UTM params directly to our analytics.

Now we match subscription charges to initial campaign params. Finance finally trusts our LTV calculations. Took 2 days to set up.

Use web paywalls as the conversion point instead of app stores. Our platform preserves all UTM parameters through checkout.

We compare web-based ROAS with adjusted store analytics - discrepancies revealed 41% undervalued campaigns. Changed budget allocation and grew MRR 18%.
Marked as best answer

I track which campaigns drive users who complete at least three onboarding steps.

Even if attribution gets lost later, these high-intent cohorts convert 6x better.

Web paywalls don’t lie about revenue.

Validate ROAS with:

  1. Time-decay attribution models in web analytics
  2. Cohort analysis of subscription renewals
  3. CRM-synced lifetime value

Found 63% of ‘organic’ installs were actually paid-driven when analyzing web funnel entries. Completely changed our budget priorities.

Ran a test: same campaign, different UTMs on iOS vs Android.

Web paywall data showed iOS creatives underperformed by 28% despite similar install counts. Shifted budget and increased conversions 19%.

Key insight: App store stats mask creative-level performance.

UTM parameters help if teams use them consistently.