Cohort

A cohort is a group of customers who share a common starting point, most often the month or week they signed up or made their first purchase, tracked together as a unit over time. Cohort analysis compares how different cohorts behave at the same point in their lifecycle, revealing retention and revenue patterns that blended, company-wide averages tend to hide.

This matters because aggregate metrics can mislead, especially for fast-growing businesses. A large influx of new customers can make overall retention look stable even if older cohorts are actually churning heavily, since the new signups dilute the average. Breaking customers into cohorts and tracking each separately exposes these underlying trends. Net Revenue Retention calculations are often built on a cohort basis for exactly this reason.

There's no single formula, cohort analysis is a framework rather than a calculation, but a common output is:

Cohort Retention (%) at Month N = (Active Customers from Cohort at Month N / Original Cohort Size) × 100

A company tracks its January signup cohort of 500 customers over the following months:

• Month 1: 500 active (100% retention, baseline)
• Month 3: 380 active (76% retention)
• Month 6: 310 active (62% retention)

Comparing this to a June cohort that retains 70% at month 6 instead of 62% would signal genuine improvement in onboarding, product fit, or customer experience, a trend a single blended retention number for the whole company would never reveal.

• Blended averages hide cohort-level trends, especially during fast growth. A company adding many new customers can look healthy on aggregate metrics while older cohorts are quietly churning.
• Signup or purchase month is the most common grouping, but not the only option. Acquisition channel or initial plan type can also define meaningful cohorts depending on what question you're trying to answer.
• Cohort retention curves typically decay and then flatten. Most businesses see steep early drop-off followed by a more stable, loyal remaining group, the shape of that curve itself is informative.
• Comparing cohorts over time reveals whether the business is actually improving. If newer cohorts consistently retain or spend better than older ones, that's a much stronger signal than revenue growth alone.
• Cohort analysis underlies many other retention metrics. NRR, churn rate, and customer lifetime value calculations are often more accurate and actionable when computed on a cohort basis rather than as a single blended company-wide figure.