Churn Rate is the percentage of users or customers who stop using your product within a defined time window. It’s the mirror image of Retention: the higher the churn, the harder you have to work (and pay) to keep growth above water. In analytics we typically compute user churn per month or per billing cycle, but the definition is agnostic to business model.
Formula (simple, clean)
Churn Rate = (Users lost during period ÷ Users at start of period) × 100%
Notes:
• Exclude new users acquired during the period from the denominator, otherwise you’ll understate churn.
• Use the same identity definition you use for MAU/WAU/DAU to avoid apples-to-oranges.
• Don’t confuse user churn with revenue churn; the former counts people, the latter counts money (Revenue).
Mini example
You started July with 1,000 active users and ended with 1,010. You acquired 60 new users in July.
Lost users = Start + New − End = 1,000 + 60 − 1,010 = 50
Churn Rate = 50 ÷ 1,000 = 5.0%
| Metric | Value |
|---|---|
| Users at start | 1,000 |
| New users in period | 60 |
| Users at end | 1,010 |
| Users lost | 50 |
| Churn Rate | 5.0% |
Why it matters
Churn compounds. A steady 3% monthly churn wipes out ~31% of a cohort in a year. That’s why churn is paired with Cohort Analysis, Stickiness, and activation metrics: you diagnose who leaves, when they leave, and what behavior predicts survival.
Practical tips
• Align period and denominator: monthly churn should use the population that was active at the start of that month.
• Segment ruthlessly: plan vs. price, country, device, acquisition source. Broad averages hide broken experiences.
• Track voluntary vs. involuntary churn (e.g., failed payments) separately; they’re solved by different playbooks.