Keeping a customer is cheaper than winning one. This tutorial walks through building a churn model that flags at-risk accounts in time to act.
Binate AI
January 21, 2026
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Churn prediction is a supervised model that estimates the probability a customer will stop using your product within a defined window. The output is not just a score — it is a ranked list of at-risk accounts and the drivers behind each risk, so teams can intervene.
Define churn precisely: no activity in 30 days? cancelled subscription? The prediction window (e.g., "will churn in the next 60 days") must match how fast your team can act. A perfect prediction you can't act on is useless.
Action Checklist
0/5Engagement trend (declining usage), support friction (tickets, negative sentiment), billing signals, and time-since-last-key-action are strong predictors. A gradient-boosted tree on these beats most deep models for tabular churn.
A subtle mistake invalidates churn models.
Quick Quiz
We build retention models that flag at-risk accounts in time to save them.
See our ML workDefine churn precisely, engineer trend features, split by time, and ship a ranked at-risk list your team can actually act on.
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