This paper talks about various approaches and models on customer segmentation in the insurance industry and other related sectors. In today's business world, especially the customer-centered industry, the most critical task is to find the right customers and serve the customers the way that most suits them. In this paper, we put our focus on the insurance industry for several considerations. One insurance company can possess hundreds of different policies, so it is crucial for policy issuers to find suitable policies for different customers. Considering the complexity and variability of different policies, insurance companies view customer segmentation as necessary and the key point for companies to compete well. Therefore, we select the insurance industry to study the effect of data-driven approaches on customer segmentation. In the first part, we discussed the need for a new approach to classify the customers and several advantages of the data-driven approach over the traditional method. In the second part of the paper, segmentation approaches such as K-means clustering, hybrid clustering, rule mining, and decision tree are discussed respectively about their processes and features. In the third part, we talked about the two current customer segmentation applications that are widely used today. We also talked about the segmentation systems in determining the risk of transmission of COVID-19. In the last part, we conclude the paper with the comparison of different approaches we discussed.
| Published in | Journal of Finance and Accounting (Volume 9, Issue 6) |
| DOI | 10.11648/j.jfa.20210906.17 |
| Page(s) | 268-272 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Clustering algorithm, RFM Analysis, Decision Tree
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APA Style
Chen Wen, Ke Gao, Yuanzhi Xiao. (2021). Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors. Journal of Finance and Accounting, 9(6), 268-272. https://doi.org/10.11648/j.jfa.20210906.17
ACS Style
Chen Wen; Ke Gao; Yuanzhi Xiao. Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors. J. Finance Account. 2021, 9(6), 268-272. doi: 10.11648/j.jfa.20210906.17
AMA Style
Chen Wen, Ke Gao, Yuanzhi Xiao. Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors. J Finance Account. 2021;9(6):268-272. doi: 10.11648/j.jfa.20210906.17
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Download@article{10.11648/j.jfa.20210906.17,
author = {Chen Wen and Ke Gao and Yuanzhi Xiao},
title = {Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors},
journal = {Journal of Finance and Accounting},
volume = {9},
number = {6},
pages = {268-272},
doi = {10.11648/j.jfa.20210906.17},
url = {https://doi.org/10.11648/j.jfa.20210906.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20210906.17},
abstract = {This paper talks about various approaches and models on customer segmentation in the insurance industry and other related sectors. In today's business world, especially the customer-centered industry, the most critical task is to find the right customers and serve the customers the way that most suits them. In this paper, we put our focus on the insurance industry for several considerations. One insurance company can possess hundreds of different policies, so it is crucial for policy issuers to find suitable policies for different customers. Considering the complexity and variability of different policies, insurance companies view customer segmentation as necessary and the key point for companies to compete well. Therefore, we select the insurance industry to study the effect of data-driven approaches on customer segmentation. In the first part, we discussed the need for a new approach to classify the customers and several advantages of the data-driven approach over the traditional method. In the second part of the paper, segmentation approaches such as K-means clustering, hybrid clustering, rule mining, and decision tree are discussed respectively about their processes and features. In the third part, we talked about the two current customer segmentation applications that are widely used today. We also talked about the segmentation systems in determining the risk of transmission of COVID-19. In the last part, we conclude the paper with the comparison of different approaches we discussed.},
year = {2021}
}
TY - JOUR T1 - Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors AU - Chen Wen AU - Ke Gao AU - Yuanzhi Xiao Y1 - 2021/12/07 PY - 2021 N1 - https://doi.org/10.11648/j.jfa.20210906.17 DO - 10.11648/j.jfa.20210906.17 T2 - Journal of Finance and Accounting JF - Journal of Finance and Accounting JO - Journal of Finance and Accounting SP - 268 EP - 272 PB - Science Publishing Group SN - 2330-7323 UR - https://doi.org/10.11648/j.jfa.20210906.17 AB - This paper talks about various approaches and models on customer segmentation in the insurance industry and other related sectors. In today's business world, especially the customer-centered industry, the most critical task is to find the right customers and serve the customers the way that most suits them. In this paper, we put our focus on the insurance industry for several considerations. One insurance company can possess hundreds of different policies, so it is crucial for policy issuers to find suitable policies for different customers. Considering the complexity and variability of different policies, insurance companies view customer segmentation as necessary and the key point for companies to compete well. Therefore, we select the insurance industry to study the effect of data-driven approaches on customer segmentation. In the first part, we discussed the need for a new approach to classify the customers and several advantages of the data-driven approach over the traditional method. In the second part of the paper, segmentation approaches such as K-means clustering, hybrid clustering, rule mining, and decision tree are discussed respectively about their processes and features. In the third part, we talked about the two current customer segmentation applications that are widely used today. We also talked about the segmentation systems in determining the risk of transmission of COVID-19. In the last part, we conclude the paper with the comparison of different approaches we discussed. VL - 9 IS - 6 ER -
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