One reconciled revenue truth
Revenue and ARPU aligned across MMP, stores, ads, and subscriptions (D1 / D3 / D7) — so teams stop arguing whose numbers are right.
COHORTFUL
Revenue Reconciliation Engine
Get early ARPU with reliability bounds — from messy, conflicting revenue sources — without waiting weeks for downstream LTV.
Cohortful reconciles your numbers and shows payback probability, not point estimates.
One decision-ready view: D7 ARPU + reliability bounds + payback probability + cohort ranking.
- Trust the numbers: reconciliation across sources
- Decide earlier: weeks before downstream LTV
- Scale with confidence: payback probability + reliability bounds
Limited early access for mobile publishers and UA teams.
Cohort leaderboard
CohortExpected D7 ARPURiskConfidence
Creative A • US
$0.82 [0.71–0.94]
Low
0.86
Creative B • CA
$0.77 [0.61–0.93]
Medium
0.71
Creative C • AU
$0.74 [0.46–1.02]
High
0.54
Guidance
Scale A • Iterate B • Hold C
Why publishers use Cohortful
Your revenue data doesn’t agree: MMP events, store reports, ad monetization, subscriptions — all tell different stories. Cohortful normalizes and reconciles them into one cohort-level revenue view — so scaling stops being a debate over “which number is right.”
What you get
Earlier payback decisions
Early ARPU with reliability bounds and payback probability at your current CPI/spend — for faster go / no-go calls.
Repeatable workflow, not ad-hoc analysis
Upload → reconcile → compare → export. Reuse it for every test: creatives, geos, channels, builds.
Revenue inputs → Output
Revenue inputs
- AppsFlyer / Adjust in-app revenue events
- App Store / Google Play financial reports
- Ad monetization (AdMob, ironSource, Unity, etc.)
- Subscriptions (Stripe / RevenueCat / backend)
- Country-level adjustments (tax, FX, fees)
Output
Reconciled cohort-level revenue estimates with reliability bounds — ready for UA, Monetization, and Finance.
How it works
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Step 1
Upload revenue data
MMP/BI exports + optional store, ads, and subscriptions.
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Step 2
Reconcile to one truth
Cohortful normalizes and aligns sources into consistent cohort-level revenue.
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Step 3
Decide with confidence
Early ARPU + reliability bounds + payback probability, with cohort ranking and comparisons.
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Step 4
Repeat for every test
Upload → reconcile → compare → export.
Access form
Apply for early access
If you run cohort-based experiments and want faster, risk-aware decisions — apply.