AI Churn Prediction Engine

Built a machine learning engine that analyzes usage patterns, support interactions, billing history, and account health scores to predict churn risk up to 30 days in advance. The system triggers automated retention workflows — personalized offers, check-in calls from success teams, and feature re-engagement campaigns. Reduced churn by 35% across deployment and achieved 94% prediction accuracy. The model ingests data from multiple sources: product usage analytics (session frequency, feature adoption rates, API call volume), support ticket metadata (ticket volume, resolution time, sentiment), billing data (payment history, plan changes, invoice disputes), and account-level attributes (team size, industry, contract length). Feature engineering pipelines transform raw events into predictive signals. An ensemble of gradient-boosted trees and deep neural networks produces daily churn probability scores per account. Accounts crossing configurable risk thresholds trigger automated workflows: low-risk accounts receive automated re-engagement tips, medium-risk accounts get personalized offers or feature recommendations, high-risk accounts trigger alerts to customer success managers with root cause analysis and suggested intervention strategies. A feedback loop captures intervention outcomes to continuously improve prediction accuracy over time.