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SaaS/North America Node
SaaS Churn Prediction
Strategic Mission
Identify at-risk SaaS users with 90% accuracy before they cancel their subscriptions.
The Challenge
A high-growth North American SaaS company was losing millions to churn but only identifying at-risk users after the cancellation request.
The Engineering
A behavioral ML model that analyzes product usage patterns and support tickets to predict churn 30 days in advance.
Technical Stack & Infrastructure
Gradient Boosted Decision Trees (XGBoost)
Integration with Segment, Intercom, and Salesforce
Automated retention campaign triggers
Predictive lifetime value (LTV) modeling
Verified Performance Delta
1
25% reduction in annual churn rate2
Saved $4.2M in annual recurring revenue (ARR)3
90% precision in identifying churn riskSolution Inquiry
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