Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Abstract

Customer segmentation and profiling has become an important marketing strategy in most businesses as a preparation for better customer services as well as enhancing customer relationship management. This study presents the segmentation and classification technique for insurance industry via data mining approaches: K-Modes Clustering and Decision Tree Classifier. Data from an insurance company were gathered. Decision Tree Algorithm was applied for customer profile classification comparing two methods which are Entropy and Gini. K-Modes Clustering segmentized the customers into three prominent groups which are “Potential High-Value Customers”, “Low Value Customers” and “Disinterested Customers”. Decision Tree with Gini model with 10-fold cross validation was found as the best fit model with average accuracy of 81.30%. This segmentation would help marketing team of insurance company to strategize their marketing plans based on different group of customers by formulating different approaches to maximize customer values. Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies.