AI Customer Churn Prediction

Overview

A B2B SaaS company with 4,000 accounts was losing customers at an alarming rate but couldn't figure out why until after they'd already cancelled. Exit surveys were unreliable and too late to act on. We built a churn prediction system that monitors every customer signal — login frequency, feature adoption, support ticket sentiment, billing changes, and engagement with success touchpoints — and generates a churn risk score updated daily. When a customer crosses the risk threshold, the system automatically triggers a tailored retention playbook: personalized outreach from the CSM, targeted feature training, or executive escalation depending on account value.

The Challenge

Churn signals are subtle and vary by customer segment. A drop in logins means something different for a daily-active-user product vs. a monthly reporting tool. The model needed to learn segment-specific patterns, handle class imbalance (most customers don't churn in any given month), and generate predictions early enough to be actionable. Integration with the existing CS workflow was critical — predictions are useless if nobody acts on them.

Our Approach

We built a gradient-boosted model trained on 3 years of historical customer data including 200+ features spanning product usage, support interactions, billing patterns, and NPS responses. The model updates daily and feeds risk scores into the CRM dashboard. We designed automated playbooks that trigger based on risk level and account tier — from automated check-in emails for low-risk accounts to executive intervention for high-value at-risk accounts. A/B testing tracks which interventions are most effective per segment.

Key Features

• Daily churn risk scoring for every account
• Segment-specific prediction models
• Automated retention playbook triggering
• CSM dashboard with prioritized at-risk list
• Churn reason classification from signals
• Intervention effectiveness tracking
• Executive alerts for high-value at-risk accounts

Results

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