Credit Card Offer Optimization
Data-driven system to personalize credit card offers and maximize approval efficiency
Situation
A financial institution struggled with low credit-card offer response rates and inefficient targeting strategies.
Task
Design a data-driven system to personalize offers and maximize approval efficiency while maintaining compliance standards.
Action
Business & Strategy
Segmented customers using K-Means clustering based on behavior and demographics. Collaborated with marketing teams to align segments with business objectives and compliance requirements.
Technical Implementation
Built logistic regression and gradient boosting models in Python (scikit-learn, XGBoost). Created interactive dashboards in Tableau for real-time performance monitoring. Implemented automated reporting pipeline reducing manual work by 70%.
Results
Business Impact
Conversion increased by 19%, approval efficiency improved by 12%. Campaign ROI improved significantly with better-targeted offers.
Technical Achievement
Achieved ROC-AUC of 0.91. Reporting automation cut manual work by 70%, enabling weekly iteration cycles instead of monthly.