Churn prevention is detecting risk, choosing the intervention with the highest incremental effect, and proving it worked against a holdout. How the loop runs.
Frequently asked
Related resources
Solutions
See it in the product
The same loops this note describes run 24/7 against your customer base. Watch the workspace decide, experiment and execute 1:1.
Vocabulary
Definitions an answer engine can quote, each one a page of its own.
Compare
Neutral reads on the categories buyers evaluate against a decision layer.
All comparisonsChurn prevention is the practice of detecting the risk that a customer will leave, choosing the intervention with the highest expected incremental effect on that specific customer, and running it early enough to change the outcome. It is a decisioning discipline, not a campaign: the value comes from picking the right action per customer, not from contacting everyone who looks at risk.
Most teams already have a risk score somewhere. Far fewer have a loop that turns that score into an action, holds part of the population back to prove the action worked, and retires the treatments that do nothing. That loop is what this article describes.
Between 20 and 40 percent of cancellations in subscription businesses are failed payments, not decisions. Smart retries by decline code, account updater and pre-expiry card refresh cost no margin and need no model. Ship them, rebaseline, then work on intent churn. The operational detail is in dunning best practices.
Two accounts at 0.7 risk need different things: one never activated the feature they bought the product for, the other is paying for capacity they stopped using. An onboarding nudge helps the first and insults the second. Segment treatments by cause, always.
The window where a save is cheap is weeks before the cancel intent forms. By the time someone reaches the cancellation flow, the only levers left are expensive ones. Deflection still matters, and cancellation flow design earns its keep, but it is the last line, not the programme.
Rank offers by uplift-weighted margin. In most bases, a third of save offers land on customers with negative uplift: the offer reminds them that leaving was an option. Uplift modelling is the only way to see that, and it needs experiment data, which is why measurement is part of the design and not a phase at the end.
Save offers decay. A winback voucher that produced 6 points of incremental retention in quarter one is often at 2 by quarter three, as the population it works on is exhausted. Re-read every live treatment monthly against its holdout and kill the flat ones.
Three failure modes account for most disappointing programmes. The first is treating the score as the product: a dashboard of risk with nobody accountable for the action. The second is one treatment for everybody, usually a discount, which converts a retention problem into a pricing problem. The third is measuring saves without a counterfactual, which makes it impossible to stop doing the things that do not work.
Markin detects the risk signal, states the hypothesis, ranks the candidate actions by expected incremental margin and holds back a control group automatically. See Retention decisioning or the streaming view.
