An Intelligent Method to Identify Effective Factors in Customers' Dissatisfaction in the Insurance Industry using Ensemble Learning

Balani, Kamran; Sadr, Hossein; Edalatpanah, Ahmad; Manteghipour, Mahnaz; Nazari, Mojdeh

doi:10.22091/jemsc.2024.9138.1170

An Intelligent Method to Identify Effective Factors in Customers' Dissatisfaction in the Insurance Industry using Ensemble Learning

Document Type : Original Article

Authors

¹ Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran

² Department of Computer Engineering, Rasht branch, Islamic Azad University, Guilan, Iran

³ Department of Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran

⁴ Data mining desk leader, Insurance Research Center, Tehran, Iran

⁵ shahid beheshti tehran

10.22091/jemsc.2024.9138.1170

Abstract

Given the competitive market of the insurance industry, customer retention is one of the most important goals of insurance brokers. As a matter of fact, attracting a new customer as well as establishing a relationship with the insurer and gaining his trust requires a lot of money. However, the cost of attracting new customers is much more than retaining existing customers. Accordingly, marketing strategies have shifted from product-oriented and many companies have turned to customer relationship management.
Companies and organizations have found that retaining their current customers as their most valuable asset is highly important. Therefore, the strategy of insurance companies is to first retain existing customers and then attract new customers. In this regard, identifying the effective factors in customer turnover is essential. In this paper, data mining methods are used to predict the factors affecting customer dissatisfaction. Based on the empirical results, it has been determined that the customer attraction channel, purchase history and place of insurer are important factors affecting customers dissatisfaction in the insurance industry, respectively.

Keywords