Insurance Cross-selling Prediction
Predict insurance customer response towards cross-selling offers using python.SOURCE CODE
Sandbox 2.0 is an Insurance Company based in India. The insurance products offered by this company are health insurance and vehicle insurance. In the fourth quarter of 2020, there was a decrease in the Monthly Recurring Revenue (MRR) in vehicle insurance products. Business Team wants to do cross selling on Health Insurance customer who has a vehicle by selling Vehicle Insurance.
Increase number of customer, revenue growth, cost efficiency.
Predictive Analysis: build a model that could predict health insurance customer response towards cross-selling offers.
Historical data of health insurance customer: demography, health insurance premium, vehicle owned, response towards cross-selling.
We did experiments on features, such as:
- Feature encoding: one hot encoding, label encoding, ordinal encoding, and target encoding.
- Outliers treatment: log transformation, IQR method.
- Class imbalanced: oversampling SMOTE 50:50
- Train-test split: 7:3
- Scaling: standardization
This is a supervised learning problem, to be precise, a binary classification problem. These are some algorithm we have tried:
- Random Forest
- Decision Tree
- Logistic Regression
After some experiments, we evaluated the model using ROC-AUC score due to its imbalanced class, we decided XGBoost as the chosen model for further step due to its performance on test set (not overfitting).
Result: Insights and Recommendation
We found some insights in the features, but this 2 are the most interesting.
West Bengal has the highest conversion rate, business team could focus more on this area to increase customer growth.
Exclusive/captive agent has the highest conversion rate, business team could optimize the use of this channel.
Sales and Marketing:
- Focusing on West Bengal Region to increase customer growth.
- Focusing on Captive Agents to approach our customer segment.
- Utilizing the facts of accident rate, age victim and economic impact to emphasize the importance of insurance on ads.
- Giving promotions/coupon for the first 1.000 customers as part of acquiring process, such as free towing, fuel voucher, etc.
- Providing special training for captive agents in persuading customers to recommend our service to their relatives (advocation).
- Once customers are acquired, maintain their loyalty by delivering best-in-class customer experiences.
- During conversation between captive agent and potential customers, try to dig feedback for future improvement.