Churn Benchmarks by Industry: Metrics, Cohorts and Targets

Benchmark the customer and revenue model you actually operate#

A payment platform can lose a merchant account, a paying software subscriber or recurring transaction revenue. Those are different outcomes. Before comparing churn by industry, define which relationship can churn and what event ends it. A merchant with no transactions this month is not necessarily a canceled subscriber; a failed renewal attempt is not necessarily a lost customer.

The primary datasets below provide useful subscription context, but none is a universal payment-platform peer group. Their dates, measurement units and commercial models remain attached to the figures. Use them to challenge a matched internal result, then set an operating goal from the loss events your team can explain and address.

Dated primary benchmarks, with their limits attached#

Pages were checked on 3 October 2026. A report’s publication year does not necessarily describe the year of its underlying observations. Keep the data window, metric and denominator alongside every number in a board slide or retention review.

Read Recurly’s industry chart without turning it into a target#

Recurly’s current benchmark page explicitly labels its industry chart median annual churn from July 2026 network data. The six values above come from that chart. Its prose also gives a different SaaS median, 3.04%, while the chart and FAQ use 3.22%. The page does not disclose enough cohort construction to resolve that discrepancy or establish comparability with your platform.

Use the chart as an attributed industry reference, with its annual label and this limitation retained. Do not average it with an old monthly snapshot or use it as proof that your own churn should fall below a specific percentage. Request the applicable dataset definition before adopting a threshold. A large industry label alone does not match contract duration, account value, geography or customer age.

Read ChartMogul’s billing comparison as revenue retention#

ChartMogul’s 2025 SaaS Billing Report analyzes more than 2,500 SaaS companies using full-year 2024 data. Its methodology excludes companies below $300,000 ARR from ARPA segments and excludes single-model companies from the annual-versus-monthly comparison. The $250–500 ARPA row therefore concerns the report’s eligible mixed-billing businesses, not every software startup.

The 88% and 76% values are net revenue retention, which includes expansion alongside contraction and lost revenue. They do not mean 12% and 24% of customers canceled. “Monthly plans” describes billing frequency, not a monthly measurement period for that NRR. The association with annual billing also does not prove that changing your contracts alone will produce the same retention improvement.

Read RevenueCat’s app figures as cumulative subscription survival#

RevenueCat’s 2026 report uses its platform’s subscription-app data, with eligibility thresholds and a target data window of 2025; some calculations use older cohorts. Its Year-1 comparison shows the 2024 cohort’s medians of 28% for yearly plans, 8% for monthly and 1.2% for weekly. These are cumulative subscription-level retention figures, not churn measured separately each billing month.

The report distinguishes sequential renewal rates from cumulative retention and counts sufficient paid renewals for the elapsed retention window. A customer turning off renewal on an annual plan may still have paid access until expiry; cancellation intent and the end of paid retention occur at different times. Compare app figures only with a similarly defined paid-subscription cohort, rather than applying them to contract-led B2B merchants.

Lock metric definitions before calculating your own result#

A useful internal starting convention is a fixed set of paid customer accounts active at the period’s start. Count those that cease the defined paid relationship during the period, excluding new customers from that denominator. Record how reactivation, multiple subscriptions, grace periods and pending cancellations are handled. If a source uses another convention, reconcile it explicitly before comparing.

ChartMogul’s paid-subscriber retention definition starts with paid subscribers active on day one and excludes subscribers who reactivate before the period ends from churned subscribers. That illustrates why event counts and a provider’s net period metric can differ. Document your chosen treatment rather than switching conventions when the result looks inconvenient.

The revenue formulas here assume the same starting cohort, consistent recurring-revenue normalization and no separate reactivation adjustment. If your reporting system includes reactivation or segment transfers differently, show that reconciliation. Exclude revenue from newly acquired accounts when measuring retention of the starting base. Do not mix gross transaction volume, company revenue and monthly recurring revenue in one denominator.

A worked month: customer loss and revenue loss can diverge#

Suppose, hypothetically, a platform starts a month with 1,000 paid accounts and $100,000 in normalized recurring revenue. Forty starting accounts leave voluntarily and ten leave after unresolved payment problems, with no reactivations and no overlap between those groups. Customer churn is 50 ÷ 1,000 = 5%. Voluntary churn is 4% and payment-related churn is 1%; payment-related loss is also 20% of total lost accounts.

If the platform adds 100 new paid accounts, it ends with 1,050. That growth does not erase the 50 losses or replace the 1,000-account denominator. A net change in the account base and churn of the starting base answer different questions. Keep paid customers separate from free registrations and exclude a second subscription cancellation when that customer still has an active paid relationship under this account-level definition.

Now assume the lost accounts represented $8,000 of starting recurring revenue, other starting accounts downgraded by $2,000, and starting accounts expanded by $15,000. GRR is ($100,000 − $8,000 − $2,000) ÷ $100,000 = 90%. NRR is ($100,000 − $8,000 − $2,000 + $15,000) ÷ $100,000 = 105%. Net revenue churn is −5%. Five percent customer churn can coexist with net revenue growth in the starting base.

Use the customer view to understand who leaves and the revenue view to understand economic exposure. Neither makes the other redundant. For a transaction-led payment platform, also examine merchant inactivity and comparable processing revenue, but label those measures separately from subscription churn and account for volume seasonality.

Annualize only when the survival assumptions hold#

For a closed cohort with the same conditional probability c of permanent customer loss each month, no reactivation and twelve comparable monthly exposures, illustrative annual loss is 1 − (1 − c)^12. At c = 3%, that gives 30.62%; at 5%, it gives 45.96%. These are modeled cohort losses, not observed annual industry benchmarks.

If conditional losses vary by month, use 1 − product(1 − c_t) for that same surviving cohort. Prefer measuring actual twelve-month survival when data is available. An arithmetic average of rates from changing customer pools does not automatically satisfy the assumptions. New acquisition, changing tenure, seasonality, annual renewal concentration and reactivation can all make the shortcut misleading.

Do not use this customer-survival formula to convert NRR, gross transaction revenue changes or the percentage of cancellation requests. NRR contains expansion and contraction rather than only permanent customer losses. A Year-1 retention result for monthly-billed app subscriptions is already a year-long cohort observation; it does not need to be compounded twelve times.

Separate failed attempts from payment-related churn#

Payment operations need intermediate measures as well as the final customer outcome: first-attempt failures, recovery-eligible invoices, recovered invoices and amounts, time to recovery, and paid relationships lost after the defined recovery window. Fix the observation window and connect attempts to invoice and customer IDs. A retry event should not appear as a new customer or a new renewal obligation.

In the hypothetical month above, suppose 200 starting customers initially encounter a failed renewal and 190 recover before the cutoff. The ten remaining customers produce 1% payment-related customer churn against 1,000 starting customers. The 95% recovery result uses 200 failed customers as its denominator. It is not a 95% customer retention benchmark or evidence about recovery in another dataset.

Assign one final loss reason per customer for an additive voluntary/payment-related split, with an explicit unknown or mixed category when evidence cannot resolve it. A declined attempt followed by a customer’s deliberate cancellation may need review rather than automatic attribution to the gateway. Analyze payment method, country, gateway, plan and tenure without counting the same customer twice.

Set a defensible target from a matched internal cohort#

For the worked 1,000-account month, an operating hypothesis is to reduce payment-related losses from ten to five while voluntary losses remain forty. That would reduce total churn from 5% to 4.5%. The 0.5-percentage-point improvement follows from five fewer losses, not from copying an annual SaaS benchmark. It is a proposed goal, not a forecast or a confidence interval.

• Keep the same paid-account definition, loss cutoff and commercial segment in the next comparison; show count and rate together.
• Assign payments operations to the five-loss reduction, supported by the specific failure and recovery cases.
• Review voluntary losses with product/customer teams using customer evidence instead of assuming every exit is price-related.
• Evaluate the intervention against comparable historical or control cohorts, accounting for plan, country and tenure changes.
• Track GRR and NRR alongside the customer result so reduced logo churn does not hide a loss of high-value accounts.

For annual contracts, measure outcomes among accounts actually due to renew as a separate renewal analysis, then distinguish that denominator from all accounts active at year start. A young cohort that has not reached its renewal date cannot demonstrate twelve-month retention. With small cohorts, report the count explicitly: one loss among twenty accounts is five percentage points, so apparent precision can be misleading.

Keep a compact benchmark record for each decision#

Save the original source URL, checked date, data window, unit, denominator, event/cutoff definition, cohort eligibility and whether the statistic is a mean, median or percentile. Record any contradiction or missing methodology. Your comparison should say what matches, what differs and how the external result affects a specific internal investigation.

For the sources here, subscription-network annual churn, SaaS revenue retention and app-cohort subscription survival stay in separate rows. If none matches your merchant model, retain the outside figures as context and use your own consistently defined history for the target. That is a stronger decision record than a blended industry average with unexplained periods.