SaaS Churn Rate: Formulas, Benchmarks and Examples

SaaS churn rate is the percentage of customers or recurring revenue that a subscription business loses over a given period, most commonly measured monthly or annually. It is the mirror image of retention: if 100 customers were active at the start of a month and 5 cancelled, monthly customer churn is 5 percent. Churn is one of the core inputs into MRR movement, customer lifetime value, and ultimately how the business is valued.

There is no single "churn rate" number for a company. In practice teams track several variants side by side: customer churn versus revenue churn, gross churn versus net churn, monthly versus annual, and cohort-level churn versus company-wide averages. Each answers a slightly different question, and conflating them is one of the most common mistakes in SaaS reporting.

Customer churn counts logos lost as a share of logos at the start of the period, while revenue churn weights that loss by the dollar value of what was lost. The plain formula for customer churn is: customer churn rate equals customers lost during the period divided by customers at the start of the period, expressed as a percentage. Revenue churn rate equals recurring revenue lost during the period divided by recurring revenue at the start of the period.

These two numbers can diverge sharply. Imagine a company with 200 customers and 40,000 dollars of MRR at the start of a month. If 10 customers on a 50 dollar plan cancel, that is 5 percent customer churn but only 500 dollars, or 1.25 percent, of revenue churn. If instead one customer on a 4,000 dollar enterprise plan cancels, that is 0.5 percent customer churn but 10 percent revenue churn.

Neither number is "more correct" on its own. Boards and investors typically care more about revenue churn because it flows directly into net revenue retentionand forecasted MRR, but customer churn matters for product and support teams because it reflects breadth of dissatisfaction across the base, not just concentration in a few large accounts.

Gross revenue churn only counts what was lost to cancellations and downgrades, while net revenue churn nets that loss against expansion revenue such as upsells, seat additions, and add-on purchases within the same customer base. The plain formula is: net revenue churn equals (revenue lost to churn and downgrades minus expansion revenue) divided by revenue at the start of the period.

Gross churn can never be negative, since it only measures loss. Net churn can go negative when expansion revenue exceeds losses, which means the existing customer base is growing in dollar terms even before any new customers are added. A business with negative net revenue churn is compounding revenue from its installed base alone, a trait investors often reward with higher multiples.

Worked example

Start of month MRR: 50,000 dollars. Churned and downgraded revenue: 3,000 dollars. Expansion revenue from upgrades: 2,000 dollars. Gross revenue churn is 3,000 divided by 50,000, which is 6 percent. Net revenue churn is (3,000 minus 2,000) divided by 50,000, which is 2 percent. If expansion had been 4,000 dollars instead, net revenue churn would be negative 2 percent, meaning the base actually grew without any new logos.

This distinction connects directly to net revenue retention, which is simply 100 percent minus net revenue churn, expressed as retained plus expanded revenue as a share of starting revenue.

Monthly and annual churn describe the same underlying attrition over different windows, and converting between them requires compounding rather than simple multiplication. Naively multiplying a monthly rate by 12 overstates annual churn because it ignores the fact that the shrinking base each month has fewer customers left to lose the following month.

The plain formula is: annual retention rate equals monthly retention rate raised to the power of 12. Annual churn rate equals 1 minus annual retention rate. Monthly retention rate is simply 1 minus monthly churn rate.

Worked example

At 5 percent monthly churn, monthly retention is 0.95. Raising 0.95 to the power of 12 gives approximately 0.54, so annual retention is about 54 percent and annual churn is about 46 percent, not the 60 percent a naive multiplication would suggest. At 2 percent monthly churn, monthly retention is 0.98, and 0.98 to the power of 12 is approximately 0.785, giving annual churn of roughly 21.5 percent rather than 24 percent.

This gap widens as the monthly rate increases, which is why comparing an annualised figure quoted by one company against a raw monthly figure quoted by another is misleading unless both are converted to the same basis first.

Cohort churn analysis groups customers by the period they first subscribed and tracks what share of each cohort remains active month by month, revealing whether retention behaviour is stable, improving or deteriorating over time. A single blended churn number can hide the fact that customers acquired six months ago are churning much faster or slower than customers acquired this month.

A typical cohort table lists cohorts down the left as rows and months since signup across the top as columns, with each cell showing the percentage of the original cohort still subscribed.

Reading across a row shows the natural decay curve of that cohort. Reading down a column at the same month-since-signup mark shows whether newer cohorts are retaining better than older ones, which is often the clearest evidence of whether onboarding, pricing, or targeting changes are actually working. Cohort views also expose whether churn concentrates in the first 30 to 90 days, which usually points to onboarding friction, versus a slower steady decay later, which usually points to product fit or competitive pressure.

Segmenting cohorts further by plan tier, acquisition channel or company size often explains more than the aggregate curve alone, since a self-serve monthly plan and an annual enterprise contract behave very differently under the same overall churn number.

A trailing three month average churn rate smooths month-to-month noise by averaging the current month's churn with the two preceding months, giving a steadier read of the underlying trend than any single month in isolation. This matters most for smaller subscriber bases, where losing two or three customers in one month can swing the raw monthly rate substantially even if nothing structural has changed.

The plain formula is: trailing three month churn equals the sum of churned customers or revenue over the last three months divided by the sum of the starting customer count or revenue over those same three months. This differs slightly from a simple average of three monthly percentages because it weights each month by its own base, but both approaches are used in practice and either is reasonable as long as it is applied consistently.

Worked example

Month 1: 4 percent churn on a base of 200 customers, so 8 lost. Month 2: 7 percent churn on 192 customers, so about 13 lost. Month 3: 3 percent churn on 179 customers, so about 5 lost. A simple average of the three percentages is (4 + 7 + 3) divided by 3, which is 4.67 percent. This rolling figure tells a manager the trend sits closer to the middle of that range rather than reacting solely to the spike in month 2, which might have been a one-off event such as a single large customer lapse or a batch of failed payments.

Dashboards that report both the current month figure and a trailing three month average give readers the full picture: the raw number shows what just happened, and the rolling average shows whether that is part of a pattern or an outlier.

Involuntary churn is subscription loss caused by a failed payment, such as an expired card, insufficient funds, or a bank decline, rather than a customer's deliberate decision to leave. It is common for involuntary churn to make up a meaningful share, sometimes cited informally as a fifth to a third, of total churn in subscription businesses, though the exact share varies widely by industry, price point and payment method mix, so treat any such figure as a rule of thumb rather than a fixed benchmark.

Because involuntary churn is a payment operations problem rather than a product or pricing problem, it responds to different fixes than voluntary churn:

• Automated card retry logic that attempts a failed charge again after a few days rather than failing immediately, since many declines are temporary.
• Dunning email sequences that prompt the customer to update their card details before the subscription is cancelled.
• Card account updater services offered by payment processors that automatically refresh expired or reissued card numbers on file.
• Offering multiple payment methods, since reliance on a single card type increases exposure to issuer-specific decline patterns.

Separating involuntary from voluntary churn in reporting matters because the remediation is entirely different. A rising voluntary churn rate points to problems with the product, pricing, competition or customer success. A rising involuntary churn rate points to gaps in payment retry or dunning configuration, which is often the cheaper and faster fix of the two.

What counts as a good churn rate depends heavily on customer segment, since small business customers churn structurally faster than mid-market or enterprise accounts due to shorter contract terms, lower switching costs, and higher business mortality among smaller companies themselves. These figures are commonly cited rules of thumb rather than fixed targets, and should be read as broad ranges.

A company selling primarily to solo founders and very small teams should expect materially higher churn than one selling multi-year contracts to large enterprises, and comparing raw churn numbers across those two business models without adjusting for segment mix is not meaningful. Blended churn across a customer base that spans multiple segments should ideally be reported separately for each tier, since averaging masks whether the enterprise motion or the self-serve motion is the one driving overall performance.

Pricing and contract length also interact with churn: annual contracts mechanically reduce the observed monthly churn rate because customers cannot cancel mid-term, even if their underlying satisfaction or renewal intent has not improved. This is worth bearing in mind when comparing a monthly-billing competitor against an annual-billing one.

Churn rate is one of the two core inputs into customer lifetime value, and lifetime value in turn feeds into how a SaaS business is valued, which means small changes in churn compound into large changes in both metrics. A commonly used simplified formula is: customer lifetime value equals ARPU divided by revenue churn rate.

Worked example

With ARPU of 100 dollars per month and revenue churn of 4 percent, implied LTV is 100 divided by 0.04, which is 2,500 dollars. If revenue churn is reduced to 2 percent through better onboarding and dunning, LTV rises to 100 divided by 0.02, which is 5,000 dollars, a doubling of lifetime value from halving churn alone, with ARPU held constant.

This same churn figure also underpins net revenue retention, which many investors treat as one of the clearest signals of a SaaS business's durability. A company with net revenue retention consistently above 100 percent is expanding its existing base faster than it loses revenue to churn and downgrades, which is generally viewed favourably in valuation discussions and can support the case for a higher multiple on ARR.

Subscription Metric applies a simple reference multiple of 5x ARR, calculated as MRR multiplied by 60, as a quick indicative valuation figure. Actual multiples paid in acquisitions or funding rounds vary considerably and are influenced by growth rate, gross margin, net revenue retention and market conditions, so this figure should be treated as a starting reference point rather than a guaranteed outcome. Because churn is embedded in net revenue retention, and net revenue retention is one of the most closely watched inputs to any multiple discussion, tracking churn accurately is not just an operational exercise but a direct lever on how the business is perceived and priced.

Reducing churn also has a compounding effect on growth efficiency: a business that must replace fewer lost customers each month can direct a larger share of new bookings toward net growth rather than simply offsetting losses. Over several years this compounds into materially different ARR trajectories for two companies growing bookings at the same rate but with different churn profiles.

Stripe stores the raw events needed to calculate churn, including subscription status changes, cancellation timestamps, plan amounts and invoice payment outcomes, but it does not surface a churn rate metric directly in the dashboard. Building an accurate churn figure from Stripe data means reconstructing the starting customer and revenue base for each period, identifying which subscriptions moved to a cancelled or unpaid state within that period, and separating voluntary cancellations from failed-payment cancellations using the relevant subscription and invoice fields.

This is more involved than it first appears, particularly once trials, proration, multiple currencies, discounts and plan changes mid-cycle are taken into account, which is why many teams either build fairly detailed internal scripts against the Stripe API or use a connected analytics tool that reads a restricted, read-only Stripe key and computes these figures automatically. See how to calculate MRR in Stripe and Stripe revenue analytics for related detail on reconstructing recurring revenue movement from the same underlying data.

Subscription Metric connects to one or more Stripe accounts using a restricted read-only key and computes customer churn, revenue churn, LTV, ARPU, subscriber lifetime and top customers by LTV directly from live subscription and invoice data, alongside CSV, JPG and PDF exports for reporting.

Reducing churn generally requires separate interventions for voluntary and involuntary causes, since the former is a product and customer relationship problem while the latter is a payment operations problem. Common practical levers include:

• Strengthening onboarding in the first 30 days, since a large share of voluntary churn in self-serve SaaS tends to concentrate in the earliest weeks of a subscription.
• Proactively flagging usage drop-off before a renewal date so customer success can intervene before a cancellation decision is made rather than after.
• Reviewing pricing and packaging when churn concentrates on a specific plan tier, which often signals a value or price mismatch rather than a general product issue.
• Implementing card retry logic and dunning emails to recover involuntary churn from failed payments before the subscription lapses.
• Encouraging annual billing where appropriate, which reduces the frequency of cancellation opportunities and often improves cash flow, though it can mask underlying dissatisfaction until renewal.

For a fuller treatment of tactics and how they map to different churn causes, see reducing churn using Stripe data. Tracking churn rate consistently, by segment and by cause, alongside the other core metrics in the SaaS metrics glossary, is what turns a single lagging number into a set of levers a team can actually act on.