Churn Prediction & Retention Engine
Built a gradient boosting model that identifies at-risk accounts 60 days before churn. Integrated with CRM to trigger automated retention workflows, saving $2.1M in ARR.
Challenge
A fintech platform was losing mid-market accounts with little warning. Retention plays were reactive and inconsistent across account managers.
Approach
We engineered churn features from product usage, billing, and support signals, trained an XGBoost model, and connected high-risk scores to Salesforce playbooks and automated outreach.
Outcomes
- 94% prediction accuracy on held-out cohorts
- At-risk accounts flagged ~60 days before churn
- $2.1M ARR protected in the first year
- Retention playbooks standardized across the AM team
Stack
XGBoostPythonSnowflakeSalesforce
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