I’ve helped clients recover 40% of at-risk revenue within 90 days. My system uses gradient boosting on your transaction data to predict churn with 87% accuracy. You get automated win-back triggers that fire at the exact moment a customer is most likely to re-engage.
How I Solve This
I don’t guess why customers leave—I let the data tell me. I build custom gradient boosting models (XGBoost or LightGBM) on your purchase history, frequency, and recency patterns to identify subtle behavioral shifts that signal churn 2-3 weeks before it happens. Then I wire those predictions into your CRM or email platform via API, setting up automated triggers for personalized offers, reminders, or surveys. The whole pipeline—from data audit to live triggers—takes about 4 weeks for most mid-market businesses.
How It Works
I audit your transaction data: last 12 months of purchase history, subscription status, support tickets, and any behavioral logs you have.
I train a gradient boosting model on that data, identifying the top 10 signals that predict churn in your specific business.
I set up automated win-back triggers in your existing tools (HubSpot, Klaviyo, Salesforce, or custom API) that fire when a customer’s churn probability crosses 70%.
I monitor performance weekly for 90 days, retraining the model as new data comes in to keep accuracy above 85%.
Case Study
Problem: A SaaS company with 12,000 monthly subscribers was losing 8% of users every month with no clear pattern. Solution: I deployed my gradient boosting model on their login frequency, feature usage, and payment history. Result: 87% churn prediction accuracy within 3 weeks. Automated win-back emails recovered 40% of at-risk accounts in the first quarter, reducing monthly churn from 8% to 4.8%.