Cohort analysis is a way of understanding user behavior by grouping customers who share a common starting point and then tracking how each group behaves over time. In SaaS products, cohorts are most often created based on when users signed up, started a free trial, or became paying customers. Instead of looking at all users as one large group, cohort analysis helps teams see what’s really happening beneath the surface.
Think of it this way: overall metrics can look healthy, but cohort analysis shows whether newer users are actually doing better than older ones—or worse. That’s what makes it so valuable for SaaS founders, product teams, and buyers comparing software products on review platforms.
One of the most common uses of cohort analysis in SaaS is retention tracking. By grouping users by signup month and monitoring how many remain active after 30, 60, or 90 days, teams can identify whether product changes, onboarding improvements, or pricing updates are having a positive impact. If newer cohorts stick around longer, it’s usually a sign the product experience is improving. If retention drops across cohorts, it often points to usability issues or mismatched expectations.
Cohort analysis is also widely used in marketing and growth analysis. For example, customers acquired through organic search may behave very differently from those acquired through paid ads. One cohort might churn quickly, while another generates higher lifetime value. These insights help SaaS teams invest in the right acquisition channels instead of relying on surface-level growth numbers.
From a software buyer’s perspective, cohort analysis gives a clearer picture of product stickiness and long-term value. It shows whether a SaaS tool can consistently retain users, not just attract them.
Example:
A SaaS company groups customers based on their signup month. The February cohort retains 85% of users after three months, while the April cohort retains 92%. This improvement suggests that recent onboarding and feature updates are helping new users find value faster and stay longer.