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Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors

Received: 2 November 2021     Accepted: 6 December 2021     Published: 7 December 2021
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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.

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), 2021. Published by Science Publishing Group

Keywords

Clustering algorithm, RFM Analysis, Decision Tree

References
[1] R. Venkatesan, “Cluster Analysis for Segmentation,” University of Virginia Darden School, vol. 38, no. 91, pp. 92–98, 2007.
[2] Khajvand, M. and Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
[3] Ravasan, A. Z., & Mansouri, T. (2015). A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector. International Journal of Information Systems in the Service Sector (IJISSS), 7 (2), 71-86. http://doi.org/10.4018/ijisss.2015040105.
[4] Goonetilleke, T. O. and Caldera, H. A. (2013) “Mining Life Insurance Data for Customer Attrition Analysis” Journal of Industrial and Intelligent Information 1 (1).
[5] Hassouna, M., Tarhini, A., Elyas, T. and AbouTrab, M. S., (2016). Customer Churn in Mobile Markets A Comparison of Techniques. arXiv preprint arXiv: 1607.07792.
[6] Jandaghi, G., & Moradpour, Z. (2015). Segmentation of life insurance customers based on their profile using fuzzy clustering. International Letters of Social and Humanistic Sciences, 61, 17-24. https://doi.org/10.18052/www.scipress.com/ILSHS.61.17.
[7] Wafa Qadadeh& Sherief Abdallah (2018). Customer Segmentation in the Insurance Company (TIC) Dataset. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/.
[8] E. Y. L. Nandapala, K. P. N. Jayasena and R. M. K. T. Rathnayaka, "Behavior Segmentationbased Micro-Segmentation Approach for Health Insurance Industry," 2020 2nd International Conference on Advancements in Computing (ICAC), 2020, pp. 333-338, doi: 10.1109/ICAC51239.2020.9357282.
[9] Namvar, M., Gholamian, M. R. and KhakAbi, S. (2010). A two phase clustering method for intelligent customer segmentation. In Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on (pp. 215-219). IEEE.
[10] Dalla Pozza, I., Brochado, A., Texier, L. and Najar, D. (2018), "Multichannel segmentationin the after-sales stage in the insurance industry", International Journal of Bank Marketing, Vol. 36 No. 6, pp. 1055-1072. https://doi.org/10.1108/IJBM-11-2016-0174.
[11] Alt, M. A., Săplăcan, Z., Benedek, B. and Nagy, B. Z. (2021), "Digital touchpoints and multichannel segmentation approach in the life insurance industry", International Journal of Retail & Distribution Management, Vol. 49 No. 5, pp. 652-677. https://doi.org/10.1108/IJRDM-02-2020-0040.
[12] Kaveh Khalili-Damghani, Farshid Abdi, Shaghayegh Abolmakarem, “Hybrid soft computingapproach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries”, Applied Soft Computing, Volume 73, 2018, Pages 816-828, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2018.09.001.
[13] D. L. Davies and D. W. Bouldin, "A Cluster Separation Measure," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, pp. 224-227, April 1979, doi: 10.1109/TPAMI.1979.4766909.
[14] Ozdenerol, E., &Seboly, J. (2021). Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems. International journal of environmental research and public health, 18 (9), 4826. https://doi.org/10.3390/ijerph18094826.
[15] E. Engl and S. K. Sgaier, “Smarter micro-targeting to improve global health outcomes: scaling cluster segmentation on novel types of data for precision public health,” in 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada, 2018.
Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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    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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Author Information
  • Olin Business School, Washington University in St. Louis, St. Louis, USA

  • School of Economics, Beijing University, Beijing, China

  • College of Liberal Arts, Texas A&M University, College Station, USA

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