Prediction of Health Insurance Cross-Selling Through Binary Logical Regression and Decision Tree Model

Abstract

Cross-selling is typically a marketing strategy used to identify a customer's various wants and then sell a range of related services or products by attending to those demands. Cross-marketing refers to a variety of tactics and procedures used to encourage cross-selling. The most crucial role in conventional banking and insurance is played by cross-selling. In most nations, auto insurance has traditionally played a significant role in the insurance market. This paper is based on information from all of a medical insurance company's clients and their automobiles throughout the course of a certain year. The binary logistic regression model and decision tree model are used to examine the customer's interest in purchasing auto insurance in the dichotomy, and the two techniques are compared to identify the most accurate prediction method. Through reasonable analysis of independent variables and dependent variables, the prediction accuracy of the binary logical regression model is 87.7% and that of the decision tree model is 87.6%. The accuracy of the two is similar.