What is Cohort Analysis?
Cohort analysis is a method of grouping users by a shared trait or time period, then tracking how their behavior changes over time.
Cohort Analysis Explained
Cohort analysis means studying groups of people who have something in common.
For example, one group might be users who signed up in January. Another group might be users who signed up in February.
Instead of mixing everyone together, cohort analysis compares each group separately.
This helps teams see whether newer users are staying longer, leaving faster, spending more, or behaving differently than older users.
What Cohort Analysis Means For
Audience | Use Case |
|---|
Product teams | Understand how different user groups adopt features, stay active, or drop off over time. |
Growth teams | Compare users from different campaigns, channels, or signup periods to find higher-quality acquisition sources. |
Founders and analysts | Measure retention, churn, revenue, and user behavior by group instead of relying only on overall averages. |
Examples
A SaaS company compares users who signed up in January, February, and March to see which group has the best 30-day retention.
A crypto protocol tracks wallets that made their first deposit during a rewards campaign and compares them with wallets acquired organically.
A marketplace studies buyers who joined through referrals versus paid ads to see which group makes more repeat purchases.
A product team analyzes users who adopted a new feature in their first week and checks whether they remain active longer than users who did not.
FAQs
What is a cohort?
A cohort is a group of users who share a common trait, event, or time period.
What is cohort analysis used for?
Cohort analysis is used to compare how different user groups behave over time.
Why is cohort analysis important?
It helps teams see patterns that overall averages can hide.
What is an example of a cohort?
Users who signed up in the same month or came from the same campaign are examples of cohorts.
Is cohort analysis only for retention?
No. It can also study revenue, churn, feature adoption, purchases, and engagement.