Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms

• Published in: Tehnički vjesnik Vol. 25; no. 6; pp. 1783 - 1791
• Main Authors: Zhuang, Kai, Wu, Sen, Gao, Xiaonan
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.12.2018; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Algorithms; Analytics; auto insurance; Automobile insurance; business analytics approach; Business competition; Clustering; customer segmentation; Customers; Decision making; Insurance; mixed-type data; Prototypes; Segmentation
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20180720122815

Customer segmentation is critical for auto insurance companies to gain competitive advantage by mining useful customer related information. While some efforts have been made for customer segmentation to support auto insurance decision making, their customer segmentation results tend to be affected by the characteristics of the algorithm used and lack multiple validation from multiple algorithms. To this end, we propose an auto insurance business analytics approach that segments customers by using three mixed-type data clustering algorithms including k-prototypes, improved k-prototypes and similarity-based agglomerative clustering. The customer segmentation results of these algorithms can complement and reinforce each other and demonstrate as much information as possible to support decision-making. To confirm its practical value, the proposed approach extracts seven rules for an auto insurance company that may support the company to make customer related decisions and develop insurance products.