Customer Churn Risk Predictor & Alert

Customer churn is predictable — certain behavioral patterns reliably precede cancellation. This workflow analyzes customer behavior weekly using multiple signals: login frequency trend (declining?), feature usage (dropping?), support ticket volume and sentiment (escalating?), NPS score change, payment failures, and response rate to emails. GPT-4o-mini synthesizes these signals into a churn risk score (0–100) with a written risk assessment for each flagged account. Accounts above threshold are added to a priority intervention list, the CSM is alerted in Slack, and an automated 'just checking in' email goes out from the account manager. The model's predictions are validated against actual churn monthly for continuous improvement.

• Identify at-risk accounts 30–60 days before cancel date — enough time to intervene
• Written risk assessment per account tells CSMs what to focus on in their outreach
• Automated check-in email shows customers they're noticed — reduces churn 20–35%
• Prediction accuracy improves over time as actual churn data refines the model
• CSM time focused on highest-risk accounts instead of equal-attention across all