Cohort vs aggregate metrics: why averages can mislead

Aggregate metrics hide churn and expansion dynamics. Learn when to use cohort analysis and how to interpret retention and LTV.

Updated 2026-01-12

The problem with averages

Aggregates blend together customers acquired at different times, prices, and behaviors. When your product or acquisition channel changes, averages can look stable while underlying cohorts deteriorate (or improve).

What is a cohort?

A cohort is a group of customers that share a start date (e.g., customers acquired in January). Cohort analysis tracks retention, expansion, and churn for that group over time.

When cohort analysis matters most

  • When you change pricing, packaging, or onboarding.
  • When you add a new acquisition channel or targeting strategy.
  • When you're trying to forecast LTV or payback more accurately.

How to use cohorts with LTV and payback

  • Compute retention/churn by cohort and segment (plan, channel, geo).
  • Estimate LTV from retention curves rather than a single churn number.
  • Compare payback by cohort to see whether acquisition quality is changing.

FAQ

Is customer churn enough, or do I need revenue retention?
Customer churn is useful, but revenue retention (NRR/GRR) is often more representative for SaaS because expansion and contraction can dominate the story.

More in saas metrics

Churn: How to measure churn rate correctly
Contribution margin: what it is and why it matters