Which action should I drive to reduce early churn?

Find the action count and window most associated with users who stay.

If I drive this action, will retention improve?

Not necessarily. The relationship is observational—users who would have stayed anyway may simply perform the action more. Causality requires an experiment.

What if I have many event types?

More combinations mean more candidates that look good by chance. Read sample size and lift stability together instead of trusting the single top row.

What data is required?

A row-level CSV with user id, event name, and timestamp. A long-term outcome column makes the lift calculation more reliable.

Aha moment

Aha-moment finder

Find which early action, repeated how often and within how many days, predicts lasting value.

Data setup

Find the 'aha-moment' — which early action, done how many times within how many days, predicts a user sticking around (converting).Needs: User ID · Converted (target) · Early actions3 required columns

Drag & drop a CSV file

or click to choose a file

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Reuse this analysis setup

Keep the mappings, filters and model options in your project.

Open saved setups

Required to saveProProject with device storage

Next step

What resonates

Common next stepPrepare a new dataset

Action survival & drop-off

Find other analyses