Churn Prediction

How it works

Traditional churn prediction relies on product usage data — login frequency, feature adoption, contract renewal date proximity. Support-data churn prediction adds a signal layer that product analytics miss: the emotional and experiential trajectory of a customer's relationship with the brand.

A customer who contacts support three times in a month with negative sentiment and submits a 2-star CSAT is far more likely to churn than their product usage data suggests. By the time their usage drops (the lagging indicator), they've already decided to leave. Support-data churn prediction catches the leading indicators.

The most actionable churn predictions combine multiple signals: sentiment trend (worsening over time), escalation count, CSAT trajectory, and topic flags (billing disputes, competitor mentions, cancellation language). Each signal adds predictive weight.

When you'll encounter it

Churn prediction in a support context appears in CRM integrations, customer health score discussions, and customer success workflows. You'll encounter it when building post-support sequences in Klaviyo or HubSpot that trigger on negative sentiment or low CSAT.