Skip to main content
Question

Cohort Descriptions - Adjust vs RevenueCat & Late Converters Issue

  • October 7, 2026
  • 1 reply
  • 9 views
Forum|alt.badge.img

We use RevenueCat and Adjust together, but their cohorting logic is different.

1. User count mismatch:
Adjust counts each reinstall as a new install, while RevenueCat keeps the user tied to their original first_seen_time. So Adjust can show significantly more users in the short term.

What is the recommended way to reconcile these two systems and make user counts comparable?

2. Delayed conversions:
We cohort users based on first_seen_time. However, a user may not start a trial or pay during their original cohort and then return months later and become a paid user. In this case, looking only at the first_seen cohort can make us miss when the actual conversion happened.

Would it be better to keep first_seen as the acquisition cohort but create a separate conversion cohort based on the first trial/paid event? How do you usually handle this?

1 reply

Forum|alt.badge.img+5
  • Crudyg123
  • Helper
  • October 7, 2026

I’d keep both views. The acquisition cohort tells you how users acquired in a given month eventually convert, while the conversion cohort tells you when they actually start a trial or pay.

RevenueCat’s Cohort Explorer already has this split: Initial Conversions groups customers by their first conversion, including a free trial, and New Paying Customers groups them by their first payment. Someone acquired in January who first pays in April can appear in January’s acquisition cohort and April’s paying customer cohort.
https://www.revenuecat.com/docs/dashboard-and-metrics/charts/cohort-explorer

For the count mismatch, check the Adjust metric first. Adjust distinguishes reinstalls from qualifying redownload installs. Redownload installs count toward Installs and can start a new cohort depending on your settings, so every reinstall isn’t necessarily a new user.
https://help.adjust.com/en/article/redownloads

I’d compare the same date range and population, separate new acquisitions from returning users, and check whether your RevenueCat App User IDs stay consistent across reinstalls. Otherwise you’re comparing install events on one side with customer identities on the other, and the totals won’t necessarily match.
https://www.revenuecat.com/docs/customers/identifying-customers

Terms & ConditionsAccessibility statement