cohort-analysis

To analyze cohort retention data, you parse cohort tables or CSVs to compute cohort sizes and retention rates at common intervals like D1, D7, and D30. This identifies the elbow point where users drop off and highlights retention leaks for further diagnosis.

Comparing D7 and D30 retention rates against industry benchmarks involves evaluating your observed metrics against established standards for mobile, SaaS, or freemium products. This provides a clear above, at, or below assessment to gauge your product's performance.

Yes, you can use a product analytics CSV to calculate retention for weekly and monthly cohorts. The analysis requires cohort definitions and measurement events to compute retention rates and summarize curves for mobile, SaaS, and freemium products.

The best way to segment cohort retention by acquisition channel is to apply segmentation cuts to your cohort tables. This isolates specific channels or activation events to pinpoint where retention curves degrade and diagnose the root cause.

After identifying the primary drop-off point, you should run experiments targeting the diagnosed causes such as habit formation or value realization. The analysis proposes the top three prioritized actions and one sprint A/B experiment to validate the retention diagnosis.