Cohort Analysis
A method of analyzing user behavior by grouping users based on a shared characteristic (typically signup date) and tracking their metrics over time, revealing trends hidden in aggregate data.
Cohort analysis is the most important analytical technique in growth. Aggregate metrics can be deeply misleading: overall retention might look stable while each new cohort actually retains worse, masked by the shrinking but loyal base of older cohorts. Cohort analysis reveals the truth by tracking each group separately over time.
The most common cohort is time-based: users who signed up in the same week or month. Tracking retention, engagement, and revenue by signup cohort shows whether your product is improving over time (newer cohorts retain better) or degrading (newer cohorts retain worse). This is the earliest indicator of product trajectory, often visible months before it shows up in aggregate metrics.
Behavioral cohorts add another dimension: grouping users by an action they took (completed onboarding, used feature X, came from channel Y) and comparing their outcomes. This reveals causal relationships: users who complete onboarding within 24 hours retain at 60% versus 25% for those who take a week. These insights directly inform product and growth priorities, telling you exactly which behaviors to encourage and which user segments to prioritize.
Related Terms
Growth Loop
A self-reinforcing cycle where each cohort of users generates inputs (data, content, referrals) that attract the next cohort, creating compounding growth.
Churn
The rate at which customers stop using or paying for a product over a given period, typically measured as monthly or annual churn percentage.
Activation Rate
The percentage of new signups who complete a key action (the 'aha moment') that correlates with long-term retention and product value realization.
Product-Led Growth (PLG)
A go-to-market strategy where the product itself drives acquisition, activation, and expansion through self-serve experiences rather than sales-led motions.
Viral Coefficient (K-Factor)
The average number of new users each existing user brings to the product, where a K-factor above 1.0 indicates self-sustaining viral growth.
Net Revenue Retention (NRR)
The percentage of recurring revenue retained from existing customers over a period, including expansion, contraction, and churn — where 100%+ indicates growth without new customers.