Cohort Analysis is a method that groups users by a shared starting characteristic — usually when they first signed up or bought — and tracks how each group behaves over time.
Instead of one blended average, cohort analysis splits users into groups (cohorts) by a shared start — e.g. everyone who signed up in January — and follows each group’s retention, spend or behaviour over weeks and months. It shows how behaviour changes by when people joined.
Blended metrics hide the truth: total revenue can rise while newer customers churn faster. Cohorts expose this — you can see whether product or marketing changes actually improved retention for people who joined after them, which an overall average never reveals.
Cohort analysis answers the questions that matter for growth: is retention improving, when does churn spike, is LTV rising for newer joiners, which acquisition source produces the stickiest customers. It’s essential for any subscription or repeat-purchase business.
A method that groups users by a shared starting point — usually when they signed up or first bought — and tracks how each group behaves over time, such as their retention or spend. It reveals how behaviour differs by when users joined.
Blended averages hide important shifts — total revenue can grow while newer customers churn faster. Cohorts isolate each group so you can see whether changes actually improved retention or value for people who joined afterward.
Whether retention is improving over time, when churn spikes in a customer's life, whether lifetime value is rising for newer cohorts, and which acquisition sources produce the stickiest customers — all essential for subscription and repeat-purchase businesses.
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