Reactivation Analysis
CLIENT & PROBLEM STATEMENT
- The project aimed to reactivate inactive Direct plan customers by identifying dormant customers and suggesting the best buying options for Flexi Cap, Mid Cap, Small Cap, and Top 100 schemes.
- The challenge was to identify these dormant customers effectively and recommend suitable investment options tailored to their needs.
APPROACH
- G -Square Used XGBoost (Extreme Gradient Boosting) for model building due to its ability to handle large datasets and deliver high accuracy..
- Built individual models for Flexi Cap, Mid Cap, Small Cap, and Top 100 schemes, focusing on specific product behavior..
- Evaluated models using metrics such as Gini coefficient,sentivity,specificity.
SOLUTION & OUTPUT
- This project successfully reactivated a significant portion of dormant direct plan customers by leveraging machine learning models, particularly XGBoost.
- The targeted recommendation approach not only revived inactive accounts but also enhanced the overall customer experience, leading to sustainable engagement with the platform.
- Achieved a 15% reactivation rate within the first three months, exceeding initial expectations.