cohort-analysis

To identify user retention and churn patterns, you need cohort analysis to visualize how specific user segments engage over time. This process requires user data containing cohort identifiers, time periods, and engagement metrics to pinpoint exactly when and why users leave.

Performing cohort analysis requires data with three core elements: cohort identifiers, time periods, and engagement metrics. You typically upload user sign-up data alongside these engagement metrics to track feature adoption and retention accurately.

You can track feature adoption trends by grouping users into cohorts based on their sign-up period, such as a specific marketing campaign, and analyzing their engagement metrics over time. This highlights how well newly acquired segments adopt product features.

A user cohort shows lower retention when specific acquisition conditions, like a marketing campaign, attract users with different engagement behaviors. Analyzing churn patterns and feature adoption trends across these segments helps identify the underlying causes of drop-off.

Yes, you can visualize retention curves for different user segments directly within your data environment as this analysis requires no external dependencies. It processes your uploaded user engagement data to generate retention curves and churn visualizations.