What is Cohort Analysis?
Cohort analysis is a method for grouping users by a shared starting characteristic—typically a first Session or signup date—and then tracking how their behavior changes over time. Unlike simple totals, cohorts show retention, repeat activity, and downstream Conversion quality for comparable user groups.
How cohort analysis works
- Define the cohort key: e.g., “users whose first Session occurred in August 2025,” or “first purchase in Q2.”
- Pick the time bucket: day, week, or month since the start event (e.g., first App Event or first Pageview).
- Select metrics to track by bucket: Conversion Rate, retention, orders per user, revenue per user, or active users (Active Users).
- Compare cohorts side-by-side to spot trend shifts caused by product changes, pricing, seasonality, or campaign mix.
Common cohort types
- Acquisition cohorts: grouped by first visit or install; great for measuring channel quality by Source, Referral/Referrer, Campaign, or UTM tags.
- Behavior cohorts: grouped by a specific action (e.g., first purchase).
- Feature cohorts: grouped by exposure to a release or test (see also A/B Testing).
Why it matters
Cohorts answer “Are August users better than July users?” and “Do paid search users retain differently from Organic Search?” They help you connect marketing and product work to business outcomes like LTV and payback, sharpen Attribution and the chosen Attribution Model, and ultimately improve ROI.
Tooling notes
Most analytics stacks support cohorts. Beyond GA4, privacy-friendly tools like Plausible, Matomo, and Simple Analytics can compute cohorts from event streams. For deeper analysis or blending with CRM/finance, export to BigQuery or visualize in Power BI.
Practitioner tips
- Keep bucket sizes consistent across cohorts.
- Anchor cohorts on a single, well-defined start event (e.g., first Session or purchase).
- Track both leading metrics (engagement) and lagging ones (Conversion Rate, revenue).
- Pair cohorts with User Flow exploration to spot drop-offs.