Cohort Analysis: Comparing User Groups Over Time
Instead of averaging all user behavior, cohort analysis groups users by a shared starting point, like their sign-up month. This reveals how product changes affect retention for specific groups. The footgun is lumping everyone together, which hides real trends.
Cohort analysis avoids misleading "average user" metrics by grouping users based on a shared starting point, like their sign-up month. This lets you compare apples to apples over time. It's crucial for seeing if a feature launch improved 90-day retention for March sign-ups versus February's. The biggest footgun is analyzing all users in one bucket; this blended view can mask declining retention in new cohorts because loyal, older users are propping up the average.
Read the original → Wikipedia: Cohort analysis
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