Measure customer churn and retention rate from your starting and lost customer counts.
Growth has a leak, and its name is churn. You can add 100 customers a month and still shrink if 110 are leaving — the bathtub fills only when the inflow beats the drain. Our free Churn Rate Calculator measures that drain precisely: enter customers at the start of a period and how many you lost, and get churn rate, retention rate, and the implied customer lifespan in one click.
Churn rate is the percentage of your customer base that stops buying, cancels, or goes dormant during a given period. It is the single most watched metric in subscription businesses because it compounds: a small monthly leak becomes a flood over a year, and every lost customer must be re-acquired at full cost.
The widget needs just two numbers. It reports churn and its mirror image, the retention rate, plus a plain-English verdict on whether your number is healthy or alarming — and the average lifespan your churn implies.
Churn and retention are two views of the same fact: retention = 100% − churn. An 8.0% monthly churn is a 92.0% monthly retention. The retention framing matters because it compounds upward: 92% retention held for 12 months leaves 0.9212 ≈ 37% of the original cohort — that is why monthly churn that “feels” small still devastates a customer base within a year.
There are two churns worth tracking. Logo churn (customer churn) counts accounts: 80 lost of 1,000 = 8.0%. Revenue churn counts recurring revenue lost: if those 80 were your smallest accounts, revenue churn might be only 3%. Both matter — logo churn shows product-market fit breadth, revenue churn shows financial damage. Sophisticated teams also track net revenue retention, which offsets churn with expansion revenue from remaining customers; above 100% means the base grows even as some customers leave.
Churn splits into voluntary (the customer chose to leave) and involuntary (failed payments, expired cards). Involuntary churn is the silent killer — often 20–40% of total churn — and the cheapest to fix with dunning emails and card-updater services. Always separate the two before diagnosing.
The deepest use of churn is as a lifespan estimator: average customer lifetime ≈ 1 ÷ churn rate. At 8% monthly churn, expected lifespan is about 12.5 months. Feed that into the CLV formula and churn becomes the bridge between a retention metric and a money metric — which is exactly why investors ask about churn first.
Key terms:
Use net customer counts consistently: if you add customers mid-period, the standard formula still divides by the starting base — that is the convention. For revenue churn, substitute revenue figures for customer counts.
A project-management tool starts January with 1,000 paying teams and ends with 920, having lost 80 (it also gained 120 new ones, but those do not enter the formula). What is January churn?
Gross adds hid the leak — this is why churn is computed on the starting base, not net growth.
A gym starts the quarter with 400 members; 26 cancel. Quarterly churn?
Always annualize or monthly-ize before benchmarking: a 6.5% quarterly churn is far better than 6.5% monthly.
A snack-box company’s monthly churn drifts from 6% to 11% over two quarters. Running the numbers: at 6%, implied lifespan was ~16.7 months; at 11%, ~9 months — CLV nearly halved. Segmenting churn by cohort reveals boxes 1–3 churn at 18% while older cohorts sit at 4%: the problem is onboarding, not the product. They add a “second box preview” email and a skip-a-month option; early-cohort churn falls to 10% within two cycles. The calculator did not fix anything — it pointed the flashlight at the right cohort.
Second scenario: a mobile app with 50,000 installs a month celebrates growth while 30-day churn runs 77% — only 23% retention. The funnel is a sieve: acquisition looks heroic, the base barely grows. Shifting 20% of ad spend into onboarding experiments (interactive tutorial vs. video) lifts 30-day retention to 31%. Same ad spend, compounding base — churn math made retention the growth lever.
Third: a B2B analytics vendor loses 3 of 60 enterprise accounts in a year — 5% logo churn, seemingly fine. But those 3 were the largest contracts: revenue churn is 18%. The account team had been celebrating the logo number while the revenue number told the real story. They institute executive sponsors for top-quartile accounts; revenue churn drops to 6% the next year.
Fourth: a newsletter with 20,000 subscribers sees 400 unsubscribes monthly — 2% churn, excellent. But list growth stalls because 400 is also the monthly signup rate. The fix is not retention (already great) but top-of-funnel: the calculator showed the leak was small, redirecting effort to acquisition where it belonged.
Streaming: Netflix watches monthly churn obsessively; a 1-point move across 270M subscribers is millions of dollars — content spend is retention spend.
Telecom: carriers invented churn analysis; number portability made switching easy, so retention offers target high-CLV subscribers identified by churn models.
SaaS: public companies report net revenue retention; best-in-class exceeds 120%, meaning expansion outruns churn entirely.
Fitness: January joiners churn hardest by March — gyms front-load personal-training sessions to push newcomers past the danger window.
Banking: account closure is rare but devastating given decades-long CLV; banks monitor complaint patterns as churn early warnings.
Gaming: day-1, day-7, and day-30 retention are the industry’s vital signs; a 40/20/10 curve is the classic healthy shape.
One pattern worth internalizing: churn is a lagging indicator of value delivery. Customers decide to leave weeks before they cancel — usage drops first. Instrument the leading indicators (logins, feature adoption) and intervene before the cancellation click.
A brief history of the metric: churn analysis was born in telephony — 1980s carriers losing subscribers to rivals needed the math of the leaky bucket, and “churn” itself is telecom slang (customers “churning” between carriers). The SaaS explosion of the 2000s generalized it: Bessemer’s “10 laws of cloud” and the rise of cohort analysis made monthly logo and revenue churn standard board metrics. Today, machine-learning churn prediction scores every subscriber’s flight risk nightly — but the underlying fraction is still lost divided by starting base.
Worked scenario — the save-desk ROI: a software company with 5,000 accounts at 6% monthly churn loses 300 accounts a month. A two-person save desk costs $18,000/month and rescues 25% of would-be cancellations — 75 accounts. At $1,200 CLV each, that is $90,000 of preserved lifetime value against $18,000 of cost: a 5:1 return. The churn calculator sized the prize ($360,000/month walking out the door); the save rate sized the capture. Retention headcount is the rare hire with a directly measurable ROI.
Edge cases and pro tips: seasonal businesses should compare churn to the same period last year, not last month — a ski app’s summer churn is not a crisis. For freemium products, define churn on active users, not registered accounts, or dormant signups inflate the base. When contracts auto-renew annually, most churn lands in one month — use annual cohorts, not monthly rates. And beware “resurrected” customers: win-backs reduce net churn but the gross number still tells you how the product performed.
When churn is the wrong tool: for transactional businesses with no relationship (a plumber, a one-off course), repeat-purchase rate and CLV matter more than churn — there is no “cancel” event to count. For very small bases (under ~100 customers), single departures swing the percentage wildly; read the absolute numbers instead. And churn never explains why — pair every churn reading with exit surveys and usage data, or you will optimize the metric instead of the cause.
Churn rate = (customers lost ÷ customers at start of period) × 100. Retention rate = 100 − churn rate. Example: 80 lost of 1000 = 8.0% churn, 92.0% retention. Monthly benchmarks: under ~2% excellent, 5–10% needs attention. Implied lifespan ≈ 1 ÷ churn rate. Source: Sigma Calculator, Churn Rate Calculator, https://sigmacalculator.net/digital-marketing-calculators/churn-rate-calculator/
Cite this page as: Churn Rate Calculator, Sigma Calculator, https://sigmacalculator.net/digital-marketing-calculators/churn-rate-calculator/
Last updated: October 12, 2026