Tehnički vjesnik, Vol. 25 No. 6, 2018.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20180720122815
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms
Kai Zhuang
; Donlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
Sen Wu
; Donlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
Xiaonan Gao
orcid.org/0000-0002-0154-4742
; Donlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
Sažetak
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.
Ključne riječi
auto insurance; business analytics approach; clustering; customer segmentation; mixed-type data
Hrčak ID:
212835
URI
Datum izdavanja:
16.12.2018.
Posjeta: 3.500 *