Churn to LTV Impact Calculator

The calculation itself

Lifetime value is inversely proportional to churn: LTV = monthly contribution ÷ churn rate. A change in churn therefore moves lifetime value non-linearly.

New LTV ÷ old LTV = old churn ÷ new churn, which means halving churn doubles lifetime value regardless of where you started.

The absolute gain from a one-point improvement is far larger at low churn rates than at high ones.

Running the numbers

At $21.75 of monthly contribution: 6% churn gives $362.50 of LTV, 5% gives $435, 4% gives $543.75, 3% gives $725, 2% gives $1,087.50.

A one-point improvement from 6% to 5% adds $72.50. The same one point from 3% to 2% adds $362.50, five times as much.

That inverts the usual expectation that the easy wins come first. In churn terms, the wins get larger as the rate falls.

It also means a business at 8% churn improving to 7% gains far less than the effort suggests, which is worth knowing before committing to a retention programme at high churn levels.

What gets missed

The relationship assumes constant churn, and the reciprocal formula breaks down badly where churn is front-loaded. A cohort-based lifetime value will move differently from what the formula predicts.

It also treats all churn as equivalent when reducing involuntary churn from failed payments is operationally easy and reducing voluntary churn is not.

What to do next

Use it to size the prize before committing to retention work. At 4% churn, reaching 3% is worth $181 per subscriber acquired: across 2,000 subscribers, $362,000 of lifetime value.

Then check which portion of churn is involuntary, since that part is usually the cheapest to fix and its reduction produces the same non-linear gain.

Where the non-linearity comes from

Lifetime is the reciprocal of churn, so as churn approaches zero lifetime approaches infinity. Small absolute reductions at low rates produce large increases in expected tenure.

Practically, this is why mature subscription businesses invest so heavily in retention at rates that already look excellent, and why the same investment at high churn produces disappointing returns.

It also argues for fixing the structural causes of high churn: product fit, onboarding, pricing, before optimising the margins of a retention programme. The programme's returns improve as the underlying rate falls, so the order of work matters.

Modelling the effect of a specific intervention: better onboarding, a pause option, dunning, against its cost turns the non-linear relationship into a business case rather than an observation.

Because the gain scales with the existing base as well as future acquisitions, retention improvements have an immediate effect that acquisition improvements do not.

Because the gain applies to the existing base as well as future acquisitions, a churn improvement produces a step change in lifetime value rather than a gradual one.

Expressing the improvement in total lifetime value across the whole base, rather than per subscriber, is what makes the case at the size that gets attention.

Running the calculation before committing to a retention initiative sets a budget for it, since the lifetime value gained defines what the work is worth spending.