The average length of time a customer stays subscribed before cancelling, most commonly estimated as one divided by your customer churn rate.
Customer lifetime = 1 ÷ customer churn rate (monthly churn gives lifetime in months, annual churn gives lifetime in years)
A Stripe-billed SaaS starts the month with 600 customers and 15 of them cancel.
Monthly churn = 15 ÷ 600 = 2.5%. Lifetime = 1 ÷ 0.025 = 40 months — roughly 3 years and 4 months.
Customer lifetime is almost never measured directly — you would have to wait years for every customer in a cohort to churn before knowing the true average. Instead it is inferred from churn: if a fixed percentage of customers cancels each month, the average customer survives the reciprocal of that rate. It is the bridge metric that turns a churn percentage into the time and revenue dimension inside LTV.
The reciprocal formula assumes churn is constant over a customer's life, which it never is. Real SaaS churn is front-loaded: heaviest in the first one to three months, then declining as the customers who remain are the ones who found value. That means 1 ÷ churn understates the lifetime of mature cohorts, and one bad month can make projected lifetime look catastrophically short. With 12+ months of data, cohort retention curves are truer.
The most common mistake is mixing time units. Dividing 1 by an annual churn rate gives lifetime in years; dividing by a monthly rate gives months. Blend the two and every LTV and CAC-payback figure downstream is silently corrupted. A close second is using revenue churn instead of customer churn — revenue churn answers how long a pound of MRR survives, not how long a customer does, and expansion can mask customers quietly leaving.
Lifetime is hyper-sensitive at low churn because the denominator is small: 2% monthly churn implies a 50-month lifetime, 1% implies 100 months. Halving churn doubles lifetime, and therefore doubles LTV at the same ARPU, which is why churn reduction compounds harder than almost any other lever. A blended whole-business lifetime means little if monthly and annual plans churn at very different rates; segment before you trust the number.
Customer lifetime is the multiplier hiding inside LTV: monthly ARPU × lifetime in months is the simplest honest LTV estimate, so every error in lifetime flows straight into LTV, LTV:CAC, and how much you can afford to spend acquiring a customer. It also makes churn tangible — "3% monthly churn" sounds survivable until you read it as "the average customer is gone in 33 months".
ChartMogul's SaaS benchmark data puts median monthly customer churn at 6.5% for companies under $300k ARR (an implied lifetime of about 15 months) and 3.7% at $1–3M ARR (about 27 months); best-in-class businesses below 2% monthly imply lifetimes of over four years.
Divide 1 by your customer churn rate, keeping the time units consistent. A 2.5% monthly churn rate gives 1 ÷ 0.025 = 40 months; a 20% annual churn rate gives 1 ÷ 0.20 = 5 years. It is an estimate that assumes churn stays constant, so treat it as a starting point rather than gospel.
It depends on your price point and market. ChartMogul's benchmarks imply roughly 15 months for early-stage, low-ARPA SaaS and around 27 months at $1–3M ARR, while best-in-class businesses with under 2% monthly churn keep customers for more than four years. Higher-priced B2B products almost always see longer lifetimes than low-cost consumer subscriptions.
Customer lifetime is a duration — how long the average customer stays subscribed. Customer lifetime value is the money earned over that duration, typically lifetime × ARPU, often gross-margin adjusted. You need the first to calculate the second. See our LTV definition.
Because it assumes every customer is equally likely to cancel in any given period. In reality churn is front-loaded — highest in the first few months, lower among long-tenured customers — so the formula tends to understate the lifetime of customers who survive onboarding. Cohort retention analysis gives a more accurate view once you have enough history.
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