Cohort Analysis
Analyzing user behavior by grouping them into cohorts based on when they joined or another shared characteristic.
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Definition
Cohort analysis groups users by a shared characteristic (typically signup date) and tracks their behavior over time. This reveals retention curves, engagement trends, and how different cohorts perform compared to each other.
The most common cohort analysis tracks retention: what percentage of users who signed up in Month X are still active in Month X+1, X+2, etc. This creates a retention curve that shows when users drop off and whether your product retains a stable base.
Cohort analysis is superior to aggregate metrics because it controls for growth. A growing company can show improving aggregate engagement while each individual cohort is actually retaining worse — a dangerous situation masked by new user influx.
Why It Matters for Founders
Cohort analysis reveals the truth about your product's retention that aggregate metrics hide. It answers critical questions: Are newer users retaining better than older ones? Is a recent product change improving retention? Where in the user journey do most people drop off?
For founders, improving retention cohort-over-cohort is one of the strongest signals of progress toward product-market fit. If each monthly cohort retains 2% better than the last, your product is improving in ways that matter to users.
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Real-World Example
January cohort: 1000 signups. After 1 month, 400 active (40%). After 3 months, 220 active (22%). After 6 months, 150 active (15%). February cohort shows 45% month-1 retention — an improvement.
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Frequently Asked Questions
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