Definition
Cohort age is how long a cohort has existed since start. Older cohorts often behave differently than new cohorts due to mix and lifecycle effects.
Example
A cohort that started in January is at cohort age month 6 in June.
How to use it
- Use cohort age to interpret why churn and expansion change over time.
- Avoid comparing cohorts without accounting for product and pricing changes.
- Compare cohorts at the same age to avoid maturity bias.
- Track cohort age when forecasting retention or expansion benchmarks.
Common mistakes
- Comparing cohorts with different ages as if they are equivalent.
- Ignoring major product changes that shift cohort behavior.
- Mixing calendar time with cohort time in the same chart.
Why this matters
This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.
Practical checklist
- Write a 1-line definition for "Cohort Age" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Sanity-check with a related calculator from the same category on MetricKit.
- Read the related guide (e.g., Cohort analysis playbook: retention curves, LTV forecasting, and payback) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- ARR Calculator: Estimate Annual Recurring Revenue (ARR) from customers and ARPA.
- ARR vs MRR Calculator: Convert ARR to MRR (and MRR to ARR) and understand the ARR vs MRR relationship.
- ARR Growth Rate Calculator: Calculate ARR growth over a period and convert it to CMGR and annualized growth (CAGR).
- ARR Valuation Calculator: Estimate a SaaS valuation from ARR and a revenue multiple (ARR valuation).
- ARR Valuation Sensitivity Calculator: Estimate valuation sensitivity to ARR and revenue multiple assumptions (simple 3x3 grid).
Guides
- Cohort analysis playbook: retention curves, LTV forecasting, and payback: A practical cohort analysis workflow: build retention curves, forecast LTV, and translate retention quality into payback and growth decisions.