The Hidden Signals That Predict Customer Churn (90% Accuracy)

Most businesses wait until customers leave to realize there was a problem. That's backwards thinking that costs millions.

We worked with a major device insurance provider where machine learning identified 89% of churning customers before they left. The company went from bleeding revenue to proactively saving at-risk accounts.

The game-changer wasn't just knowing who would leave – it was knowing when. Survival analysis showed us Customer A might churn in 30 days while Customer B (same risk score) wouldn't leave for 90 days. That timeline changed everything.

Here's what separates winners from losers in retention:

• Stop treating churn as inevitable. It's predictable.

• Your behavioral data contains early warning signals you're ignoring.

• Classification models tell you "who." Survival analysis tells you "when."

• Proactive intervention beats reactive damage control every time.

The insurance provider now identifies high-risk customers monthly and intervenes before they reach the door. Just saving 10-15% of flagged customers translates to millions in protected revenue.

Your churn problem isn't a customer problem – it's a data problem. And data problems have solutions.

Read the full case study → [CASE STUDY]

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