What is Cohort Analysis?
Cohort analysis groups customers by a shared characteristic (usually their signup month) and tracks their behavior over time.
Cohort analysis groups customers by a shared characteristic (usually their signup month) and tracks their behavior over time. It reveals patterns that aggregate metrics hide.
The most important SaaS cohort analysis is the revenue retention curve: for each monthly cohort, what percentage of their original revenue remains after 3 months, 6 months, 12 months, and 24 months?
Healthy cohort curves flatten (customers who stay beyond month 6 tend to stay indefinitely). Unhealthy curves continue declining (customers never stop churning). The best cohort curves increase over time as expansion revenue exceeds churn — this is what negative net churn looks like at the cohort level.
Cohort analysis also reveals whether your product and acquisition are improving. If newer cohorts retain better than older cohorts, your product is getting stickier. If newer cohorts retain worse, something is degrading.
Why It Matters
Cohort analysis is the most honest retention metric because it can't be gamed by fast growth. Aggregate retention looks good when you're growing fast (new customers mask churning old ones). Cohort analysis shows the true retention picture.
Frequently Asked Questions
What is cohort analysis?
Cohort analysis groups customers by signup month and tracks their behavior over time. It reveals true retention patterns that aggregate metrics hide.
What does a good cohort curve look like?
A good cohort curve flattens after 3-6 months (retained customers stay) and ideally increases over time (expansion exceeds churn). A bad curve continues declining indefinitely.
Related Terms
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Richard Ewing is a Product Economist and AI Capital Auditor. He helps companies translate technical complexity into financial clarity.
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