Definition
Retention rate measures the fraction of customers (or revenue) that remains over a period. It is the complement of churn when measured on the same basis and time window.
Formula
Retention rate = 1 - churn rate (with consistent definitions)
Example
If monthly logo churn is 3% for a cohort, monthly logo retention is about 97% (for the same definition and period).
How to use it
- Specify whether you mean logo retention (customers) or revenue retention (dollars).
- Use cohort retention curves to see where retention drops over time.
- Pair retention with gross margin to understand LTV and payback feasibility.
Common mistakes
- Mixing logo churn with revenue retention (different denominators).
- Comparing retention across periods without consistent cohort definitions.
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 "Retention Rate" 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.
- Use a calculator that references this term (e.g., Retention Rate Calculator) to sanity-check assumptions.
- Read the related guide (e.g., Retention curves: how to read them and why they matter) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- Retention Rate Calculator: Calculate retention rate for a period accounting for new customers.
- Retention Curve Calculator: Model a simple cohort retention curve (logo retention) and translate it into expected revenue and gross profit over time.
- Two-stage Retention Curve Calculator: Model a retention curve with different churn rates for early months vs steady-state, and estimate expected value over time.
- Cohort Payback Curve Calculator: Estimate when a cohort pays back CAC using a simple retention curve (two-stage churn) and optional expansion.
Guides
- Retention curves: how to read them and why they matter: A practical guide to retention curves: what they show, how to interpret churn vs retention, and how to connect retention to LTV and payback.
- 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.
- Two-stage churn: modeling early drop-off vs steady-state retention: A practical guide to two-stage churn models: why early churn matters, how to model it, and how to connect retention improvements to LTV.
- Cohort payback curves: how to model payback with early churn: A practical guide to cohort payback: why payback matters for survival, how early churn affects payback, and how to improve it.
- Retention & churn hub: cohorts, GRR/NRR, and retention curves: A practical hub for retention measurement: churn rate, GRR/NRR, cohort retention curves, and how to set retention targets without getting misled by noise.