Fintech
Churn Analytics
The business problem and approach
Developed a predictive churn model for a financial marketplace client, flagging risks of disengagement three months in advance. Extracted key risk drivers using SHAP values, equipping the talent operations team with proactive insights to intervene and strengthen recurring client-freelancer relationships.
Implementation highlights
Built advanced machine learning model to predict churn risk, Identified and explained top three risky features with SHAP for actionable response, Delivered talent ops dashboards to prioritize high-risk engagements
Delivery and outcomes
12% reduction in client churn
Technologies used
- Python
- Machine Learning
- SHAP
- SQL
- Data Visualization
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