Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms
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 b...
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
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Online Access: | https://hrcak.srce.hr/file/311142 |
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doaj-657ee9553bbd4a988982a764da87cfe62020-11-25T01:14:49ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek Tehnički Vjesnik1330-36511848-63392018-01-0125617831791Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering AlgorithmsKai Zhuang0Sen Wu1Xiaonan Gao2Donlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaDonlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaDonlinks School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, ChinaCustomer 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.https://hrcak.srce.hr/file/311142auto insurancebusiness analytics approachclusteringcustomer segmentationmixed-type data |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kai Zhuang Sen Wu Xiaonan Gao |
spellingShingle |
Kai Zhuang Sen Wu Xiaonan Gao Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms Tehnički Vjesnik auto insurance business analytics approach clustering customer segmentation mixed-type data |
author_facet |
Kai Zhuang Sen Wu Xiaonan Gao |
author_sort |
Kai Zhuang |
title |
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms |
title_short |
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms |
title_full |
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms |
title_fullStr |
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms |
title_full_unstemmed |
Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms |
title_sort |
auto insurance business analytics approach for customer segmentation using multiple mixed-type data clustering algorithms |
publisher |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
series |
Tehnički Vjesnik |
issn |
1330-3651 1848-6339 |
publishDate |
2018-01-01 |
description |
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. |
topic |
auto insurance business analytics approach clustering customer segmentation mixed-type data |
url |
https://hrcak.srce.hr/file/311142 |
work_keys_str_mv |
AT kaizhuang autoinsurancebusinessanalyticsapproachforcustomersegmentationusingmultiplemixedtypedataclusteringalgorithms AT senwu autoinsurancebusinessanalyticsapproachforcustomersegmentationusingmultiplemixedtypedataclusteringalgorithms AT xiaonangao autoinsurancebusinessanalyticsapproachforcustomersegmentationusingmultiplemixedtypedataclusteringalgorithms |
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1725156342197387264 |