Cohort and user lifetime explorations

Cohort exploration groups users by a shared starting event and shows what share of each group came back in later periods, as a triangle table.

• Inclusion criteria: what puts a user in a cohort. First touch (acquisition date) is the classic new-user cohort; Any event, Any transaction, Any key event or a specific event also work, so you can cohort by "first purchase".
• Return criteria: what counts as coming back: Any event (just showed up), Any transaction, Any key event or a specific event.
• Cohort granularity: daily, weekly or monthly; the table shows up to 60 cohorts.
• Calculation: Standard (returned in that exact period), Rolling (returned in that period and every period before), Cumulative (returned in that period or any earlier one).
• Metric: Active users by default; or a metric per cohort user such as revenue, with Metric type sum or per-user.

Read it row by row: week-0 cohorts are 100%, and the columns show the decay. Compare cohorts vertically: if the February cohorts retain better than January's, something changed. A breakdown dimension (first user channel, country) creates one triangle per value, which is where the insight usually is.

User lifetime is a free-form-like table restricted to users active in the date range, with lifetime metrics computed over their whole history on the property: Lifetime value (LTV, revenue), Lifetime engagement duration, Lifetime sessions, Lifetime transactions, and First purchase date, Last active date, plus the predictive metrics when available. Pair them with user-scoped dimensions such as First user source or First user campaign to see which acquisition channels produced the highest-value users, not just the most users.

Both techniques are user-based and benefit from User-ID; both respect the retention window, which caps how far back a cohort can start.