Find your churn rate — the share of customers you lost in a period — and your retention rate, the number that quietly decides whether a subscription business grows.
Average lifespan (1 ÷ churn) is an approximation that assumes a steady churn rate. Track churn on a consistent period (monthly or annual). Estimate only.
Churn is the share of customers who leave in a period. Its mirror is retention. At 5% monthly churn the average customer stays about 20 months; halving churn roughly doubles that lifespan — and the lifetime value with it.
Churn = customers lost ÷ customers at start · Avg. lifespan ≈ 1 ÷ churn
Churn is the share of customers who left during a period. It is arithmetically simple and strategically decisive, because in a subscription business churn is the denominator of almost everything else: lifetime value, payback period, and how fast new sales have to arrive just to stand still. Its mirror is retention, and the two always sum to 100% over the same period and the same base.
Churn = customers lost ÷ customers at start · Average lifespan ≈ 1 ÷ churn
The assumption hiding in that second formula is that the rate is constant. It rarely is. Real cohorts churn hardest in the first weeks and then flatten, so a single blended rate overstates the loss among long-tenured customers and understates it among new ones. The lifespan figure is therefore a planning approximation, not a forecast of how long any particular customer stays.
The calculator opens with 1,000 customers at the start of the period and 50 lost. Divide: 50 ÷ 1,000 = 0.05, so churn is 5% and retention is 1 − 0.05 = 95%. Customers kept is 1,000 − 50 = 950. Average lifespan is 1 ÷ 0.05 = 20 periods. If that period is a month, the average customer stays about twenty months.
Now the part the single division hides. Churn compounds, it does not add. Twelve consecutive months at 5% does not leave 40% of the cohort; it leaves 0.95 raised to the twelfth power, which is 54.0% — about 540 of the original thousand, not 400. The naive annualisation is wrong by 140 customers on a base of a thousand. And halving churn does not improve lifespan by half: at 2.5% the lifespan becomes 1 ÷ 0.025 = 40 periods, double. That non-linearity is why retention work compounds and discounting does not.
Benchmarks here need caution, because published ranges vary widely by price point, contract length and segment; low single-digit monthly churn is the figure most often cited as healthy for business software, while consumer subscriptions routinely run far higher and are built to. Rather than chase a band, watch three things in your own data: the trend across periods, the gap between customer churn and revenue churn, and the shape of the curve by cohort age. If revenue churn is materially worse than customer churn you are losing your larger accounts, which is a different and more urgent problem than losing your smallest. If revenue churn is better, the customers who stay are expanding, and net revenue retention may be above 100% even while customers leave.
One structural caution: this calculator uses the simple start-of-period base. If you added a lot of customers mid-period, some of the ones who left were people who never appeared in that starting figure, and a mid-period-average base gives a different answer. Pick one convention and hold it, because switching between them creates a trend that is entirely an artefact of the method.
The first is multiplying a monthly rate by twelve, which we just showed is wrong by a wide margin. The second is mixing periods across a single chart — one month annualised sitting next to an actual annual figure. The third is counting only cancellations and ignoring failed payments; involuntary churn from expired cards is a real and largely fixable slice of the total, and treating it as a product problem sends the fix to the wrong team. The fourth is averaging away the cohorts: a rate that looks stable can be a worsening new-customer curve masked by a loyal older base. The fifth is celebrating a drop in churn that came from a drop in new customers — fewer new joiners means fewer early-life cancellations, and the rate improves while the business shrinks.
A single rate cannot show you a curve. The next step is a cohort table: customers grouped by the month they joined, tracked across subsequent months, so you can see whether retention is improving for people who arrived recently. Add revenue churn beside customer churn, split voluntary from involuntary, and record a reason code at cancellation. That is a spreadsheet job rather than a calculator job — our Small Business Bookkeeping template carries the customer and revenue ledger those cohorts are built from.
Lower is always better; healthy SaaS often targets low single-digit monthly churn. Compare against your own trend and your sector.
This measures customer churn (accounts lost). Revenue churn weights by how much each account paid — losing big accounts hurts more.
Related: customer lifetime value calculator · MRR & ARR calculator · customer acquisition cost calculator · all free calculators.
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It uses standard formulas and recalculates the moment you change a value, so the math is exact for the inputs you enter. Treat the result as a reliable planning estimate; for decisions with legal or tax consequences, confirm the figures with a qualified professional.
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Last reviewed: September 2026. Free to use, no signup — Churn Rate Calculator by ToolWise Digital.
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