Quick answer
Revenue churn is the percentage of recurring revenue lost in a period from cancellations and downgrades, relative to revenue at the start. Gross revenue churn counts only losses; net revenue churn subtracts expansion and can be negative.
Formula: Gross revenue churn = (churned + contraction MRR) ÷ starting MRR
What is revenue churn?
Revenue churn (also MRR churn or dollar churn) is the share of recurring revenue a subscription business loses in a period from cancellations and downgrades. It differs from customer churn (logo churn), which counts the share of customers who leave regardless of how much they paid.
There are two versions:
Gross revenue churn = (churned MRR + contraction MRR) ÷ MRR at start of period
Net revenue churn = (churned MRR + contraction MRR − expansion MRR) ÷ MRR at start of period
Gross revenue churn shows how much revenue leaks out. Net revenue churn credits upgrades from the customers who stayed; it can be negative when expansion exceeds losses, which is often called negative churn.
Worked example
A SaaS company starts the month with $200,000 MRR from 400 customers (sample data):
| Movement | Customers | MRR |
|---|---|---|
| Cancelled | 20 | −$6,000 |
| Downgraded | 12 | −$2,000 |
| Upgraded | 18 | +$5,000 |
- Logo churn = 20 ÷ 400 = 5.0%
- Gross revenue churn = (6,000 + 2,000) ÷ 200,000 = 4.0%
- Net revenue churn = (6,000 + 2,000 − 5,000) ÷ 200,000 = 1.5%
The cancelled customers paid $300 a month on average, below the $500 average, so revenue churn (4.0%) is lower than logo churn (5.0%). If the company lost its largest customers instead, revenue churn would exceed logo churn. Report both.
Logo churn vs revenue churn
| Logo (customer) churn | Revenue churn | |
|---|---|---|
| Counts | Customers lost | Dollars lost |
| Sensitive to | Many small customers leaving | Large customers leaving or downgrading |
| Includes downgrades? | No | Yes |
| Can be negative? | No | Net version can be |
| Best for | Product fit, onboarding, support load | Financial health, forecasting |
A business can have high logo churn among small self-serve accounts and still healthy revenue retention if larger accounts stay and grow.
Relationship to retention metrics
- Gross revenue retention = 1 − gross revenue churn (over the same period). In the example, 96% for the month.
- Net revenue retention = 1 − net revenue churn. In the example, 98.5% for the month.
- Monthly figures compound: 4% gross monthly churn is roughly 39% of starting revenue lost over a year without expansion (1 − 0.96¹² ≈ 0.39).
How to reduce revenue churn
- Find who churns. Break churned MRR down by plan, cohort, industry, acquisition channel and product usage.
- Spot early signals. Falling usage, fewer active seats, support tickets and failed payments often precede cancellation; see customer health score.
- Fix involuntary churn. Failed card payments can be a large share of churn; retries and card updaters recover much of it.
- Offer downgrades and pauses instead of cancellation where it fits, but track contraction.
- Drive expansion from accounts that get value: seats, usage tiers, add-ons.
Common mistakes
- Quoting net churn alone, which can hide heavy cancellations behind a few big upgrades.
- Mixing periods: monthly and annual churn are not 12× apart because of compounding.
- Including new customers' MRR in the denominator. Use MRR at the start of the period.
- Counting annual plans at renewal only, which makes churn lumpy; spread it consistently.
In TapCub
Revenue churn itself comes from billing data. TapCub helps find the cause: send subscription events (started, upgraded, downgraded, cancelled) with MRR amounts, then compare cohorts, plans and accounts by retention and usage, and look at what churned accounts stopped doing in the weeks before they left. See churn, customer churn analysis and churn rate vs retention rate.
Sources
- Wall Street Prep. Revenue churn: MRR churn formula.
- Stripe. Revenue churn 101: how to calculate it and why it matters.
- Baremetrics. Gross churn vs net churn.
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Data Engineer
Writes about event design, data quality, and collection workflows, with verifiable implementation methods for reliable analytics.