Churn Prediction

Overview

Churn prediction shifts retention from reactive ("they cancelled — now what?") to proactive ("this account shows 3 risk signals — intervene now"). Early intervention reduces churn by 20-40% compared to waiting for cancellation. This skill covers modeling leading indicators, building risk scores, and designing intervention playbooks.

Authoritative Foundations

• Gainsight — Customer health scores — Combine product usage, support signals, relationship depth, and sentiment into a weighted score with clear red, yellow, and green thresholds.
• Lincoln Murphy — Desired Outcome — Customers churn when they stop reaching their desired outcome; predict churn from outcome progress, not logins alone.
• Patrick Campbell (ProfitWell) — Voluntary vs involuntary churn — Model failed-payment churn separately; it has different leading indicators and fixes than dissatisfaction churn.