Time-based cohorts

Time-based cohorts organize users by when they first joined or started using a product, typically grouped by day, week, month, or quarter. This chronological grouping allows teams to detect patterns in user behavior and product performance across different time periods. Each cohort experiences the product under unique circumstances — different features, market conditions, or seasonal factors.

When analyzing time-based cohorts, teams can identify how product changes impact newer versus older users. For example, comparing retention rates between users who joined before and after a major feature launch reveals that feature's true impact on user engagement. This analysis also helps isolate the effects of external factors like marketing campaigns or market shifts.

Time-based cohort analysis also excels at revealing gradual changes in product performance and user behavior patterns. Teams can spot trends like declining retention in recent cohorts, seasonal variations in user engagement, or improvements in activation rates following product updates.