Churn Prediction
Machine learning models that identify customers likely to stop using your product or cancel their subscription.
Definition
Churn prediction uses AI to analyze customer behavior patterns—login frequency, feature usage, support tickets, payment issues—and assign each customer a probability score for leaving.
These models identify at-risk customers weeks or months before they actually churn, enabling proactive retention efforts while there's still time to intervene.
Why It Matters
Acquiring new customers costs 5-25x more than retaining existing ones. Churn prediction lets you focus retention resources on customers who actually need intervention, rather than blanket campaigns that waste money on already-loyal users.
Companies using churn prediction typically reduce customer attrition by 15-30% through targeted outreach.
Examples in Practice
A SaaS company's AI identifies users who haven't logged in for 5 days and show declining feature adoption. Customer success reaches out with personalized training, reducing churn by 23%.
A subscription box service predicts cancellation risk based on skip frequency and review sentiment. High-risk customers receive exclusive offers, recovering 18% who would have cancelled.