Customer Segmentation for Life Insurance in Iran Using K-means Clustering

Document Type : Research Paper

Authors

1 Insurance Research Centre

2 Department of Mathematics, Faculty of Mathematics, Statistics & Computer Science, Semnan University, Iran

Abstract

Concerning life insurance, penetration rate is one of the main goal of every developed insurance industry. In this sense systematic marketing is a significant component in strategic plan of insurance companies. To achieve the goal insurers need to group their client into different groups in which some common features are shared and people demonstrate a similar pattern. This paper utilizes K-means clustering as an unsupervised learning algorithm in order to divide customers into number of clusters. The clusters are constructed based on two independent variables namely; car and life insurance premiums. Then the descriptive statistics of other determining features are provided with which the most willing group in purchasing life insurance is presented.

Keywords

[1] Bayer, J., ”Customer segmentation in the telecommunications industry.” Journal of Database marketing & customer strategy management, 17(3-4) (2010), pp.247-256.
[2] Bahiraei, A., Abbasi, B., Omidi, F., Hamzah, N. A., and Yaakub, A. H., ”Continuous time portfolio optimization”.
International Journal of Nonlinear Analysis and Applications, 6 (2)(2014 ) pp. 103-112.
[3] Bahiraie, A., Azhar, A. K. M., and Ibrahim, N. A., ”A new dynamic geometric approach for empirical analysis
of financial ratios and bankruptcy.” Journal of Industrial & Management Optimization, 7(4) (2011), 947-965.
[4] Choi, S.S., Cha, S.H. and Tappert, C.C., ”A survey of binary similarity and distance measures”. Journal of
Systemics, Cybernetics and Informatics, 8(1) (2010), pp.43-48.
[5] Chiang, W.Y., ”Applying data mining for online CRM marketing strategy.” British Food Journal(2018).
[6] dos Santos, T.R. and Z´arate, L.E., ”Categorical data clustering: What similarity measure to recommend?.”
Expert Systems with Applications, 42(3) (2015), pp.1247-1260.
[7] Everitt, B.S., Landau, S. and Leese, M., ”Cluster Analysis,” 4th edition. Edward Amold, New York I, 993, (2001).
[8] Green, P.E. and Rao, V.R., ”A note on proximity measures and cluster analysis”, (1969).
[9] Han, J., Pei, J. and Kamber, M., ”Data mining: concepts and techniques”. Elsevier, (2011).
[10] Hastie, T., Tibshirani, R. and Friedman, J., ” Unsupervised learning. In The elements of statistical learning.”
Springer, New York, NY. (2009) pp. 485-585.
[11] Hamka, F., Bouwman, H., De Reuver, M. and Kroesen, M., ”Mobile customer segmentation based on smartphone
measurement.” Telematics and Informatics, 31(2) (2014), pp.220-227.
[12] Hol´y, V., Sokol, O. and Cern´y, M., ”Clustering retail products based on customer behaviour. Applied Soft ˇ
Computing, 60 (2017), pp.752-762.
[13] Irani, J., Pise, N. and Phatak, M., ”Clustering techniques and the similarity measures used in clustering: A
survey”. International journal of computer applications, 134(7) (2016), pp.9-14.
[14] Linden, G., Smith, B. and York, J., ”Amazon. com recommendations: Item-to-item collaborative filtering.” IEEE
Internet computing, 7(1) (2003), pp.76-80.
[15] Linoff, G.S. and Berry, M.J., ”Data mining techniques: for marketing, sales, and customer relationship management.” John Wiley & Sons, (2011).
[16] Moro, S., Laureano, R. and Cortez, P., ”Using data mining for bank direct marketing: An application of the
crisp-dm methodology”, (2011).[17] Mori, U., Mendiburu, A. and Lozano, J.A., ” Similarity measure selection for clustering time series databases.”
IEEE Transactions on Knowledge and Data Engineering, 28(1) (2015), pp.181-195.
[18] Parsell, R.D., Wang, J. and Kapoor, C., Microsoft Corp, ”Customer segmentation.” U.S. Patent Application,
13/716,234, (2014).
[19] Patidar, A.K., Agrawal, J. and Mishra, N., ”Analysis of different similarity measure functions and their impacts
on shared nearest neighbor clustering approach.” International Journal of Computer Applications, 40(16) (2012),
pp.1-5.
[20] Peker, S., Kocyigit, A. and Eren, P.E., ”LRFMP model for customer segmentation in the grocery retail industry:
a case study.” Marketing Intelligence & Plannin (2017).
[21] Shaw, M.J., Subramaniam, C., Tan, G.W. and Welge, M.E., ”Knowledge management and data mining for
marketing.” Decision support systems, 31(1)(2001), pp.127-137.
[22] Teichert, T., Shehu, E. and von Wartburg, I., ”Customer segmentation revisited: The case of the airline industry.”
Transportation Research Part A: Policy and Practice, 42(1) (2008), pp.227-242.
[23] Torres, G.J., Basnet, R.B., Sung, A.H., Mukkamala, S. and Ribeiro, B.M., ” A similarity measure for clustering
and its applications.” Int J Electr Comput Syst Eng, 3(3) (2009), pp.164-170.
[24] Tsai, C.F., Hu, Y.H. and Lu, Y.H., ”Customer segmentation issues and strategies for an automobile dealership
with two clustering techniques.” Expert Systems, 32(1) (2015), pp.65-76.
[25] Weinstein, A.T., ”Market segmentation: Using demographics, psychographics and other niche marketing techniques to predict customer behavior.” Probus Publishing Co. (1994).
[26] Xu, R. and Wunsch, D., ”Clustering.” John Wiley & Sons, 10(2008).
Volume 12, Special Issue
December 2021
Pages 633-642
  • Receive Date: 06 January 2021
  • Revise Date: 15 February 2021
  • Accept Date: 28 February 2021