Cohort Analysis for Mobile Apps
Blended numbers lie. Cohort analysis groups users by when and how they arrived, so you can see what is actually working before it is too late.
Key takeaways
- Cohorts group users by install date and source, so trends are not hidden by blending.
- They reveal retention and payback early, while you can still act on them.
- Always cohort by channel and geo; a blended average hides the truth.
If you only ever look at blended, account-wide numbers, you are flying blind. Cohort analysis is the habit that separates teams who understand their growth from teams who are surprised by it.
Why blended metrics deceive
A blended average mixes loyal old users with fresh ones, so a healthy-looking number can hide rotting new cohorts. By the time the blend turns, the damage is months old and expensive to undo.
How to cohort
Group users by install week, and split by channel and geo. Then track each group's retention, revenue and payback curves over time, comparing like with like instead of averaging everything into mush.
Reading the curves
Early retention and revenue curves predict where a cohort's LTV and payback will land long before they fully mature. That early read is what lets you cut or scale a channel while it still matters.
Want your cohorts read properly before they turn?
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