Churn Prediction

Churn prediction solves a fundamental business problem: you don't know which customers are about to leave until they're already gone. By then, it's too late to save them. Traditional approaches rely on gut feel or reactive support tickets. Churn prediction flips this on its head by being proactive.

Think of it like a credit card company that notices you haven't used your card in three months, your spending patterns have dropped, and you've started using a competitor's card more often. An AI model spots these signals before a human would and flags your account as high-risk for cancellation. The company can then send you a targeted offer or reach out with personalized value before you decide to close the account.

In practice, churn prediction works by training an AI model on historical customer data—things like login frequency, feature usage, support ticket sentiment, payment delays, or engagement metrics. The model learns which combinations of behaviors precede customer departures. Then it scores your current customer base, ranking them by cancellation risk. A SaaS platform might discover that customers who haven't logged in for 14 days and haven't used the core feature in 30 days have a 67% churn rate within 60 days. Marketing and customer success teams can then segment these high-risk customers and deploy targeted retention campaigns.

The practical implication for buying AI tools is that churn prediction requires clean, historical customer data and clear definition of what "churn" means for your business. Some vendors bundle this as part of customer data platforms or CDP integrations; others offer it as standalone predictive analytics. You'll want to evaluate whether the tool can integrate with your CRM and marketing automation platform, and whether it provides actionable segments you can actually use for campaigns—not just a risk score sitting in a dashboard.