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International Letters of Social and Humanistic Sciences
Volume 61

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Segmentation of Life Insurance Customers Based on their Profile Using Fuzzy Clustering

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Abstract:

In the current competitive environment, companies will be able to adjust business strategies, they use market segmentation based on practical ways rather than using traditional approaches or incomplete and impractical mass marketing. In recent years, mining has gained attention and popularity in the business world. The goal of data mining projects is to convert the raw data into useful information. Clustering can also be used to explore differences in attitudes and intentions of the clients. In this study, we used fuzzy clustering on 1071 life insurance customers during March to October 2014. . Results show that the optimal number of clusters was 2 which were named as "investment" and "life safety". Some suggestions are presented to improve the performance of the insurance company.

Info:

Periodical:
International Letters of Social and Humanistic Sciences (Volume 61)
Pages:
17-24
Citation:
G. Jandaghi and Z. Moradpour, "Segmentation of Life Insurance Customers Based on their Profile Using Fuzzy Clustering", International Letters of Social and Humanistic Sciences, Vol. 61, pp. 17-24, 2015
Online since:
Oct 2015
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[1] Iyzadparast S. M., Farahi A., Fathnejad F. and Pourteimour B. (2011).

[2] Hanafizadeh P. and Rastkhyz Paydar N. (2012) Comparison of two methods of data mining segmentation car insurance customers by risk (Case Study: Insurance Company nations). Industrial Management Studies, 11(30): 77-97.

[3] Ghazanfari M., Malek Mohammadi S., Alizadeh S. and Fathollah M. (2009) Segmentation of customers in the export of garments based on clustering. Journal of Business Research, 14(56): 59-86.

[4] Karimi A. yatollah (2012) General insurance (second edition). Tehran, Islamic Republic of Iran Central Insurance, Insurance Institute.

[5] Bae S. M, Ha S. H., and Park S. Ch. (2005) A web-based system for analyzing the voices of call center customers in the service industry, Expert Systems with Applications, 28(1): 29-41.

DOI: https://doi.org/10.1016/j.eswa.2004.08.008

[6] Bose I. and Chen Xi. (2015) Detecting the Migration of Customers of Mobile Services Using Fuzzy Clustering, Information & Managment 52(2): 227-238.

DOI: https://doi.org/10.1016/j.im.2014.11.001

[7] Casabayó M., Agell N. and Sánchez-Hernández G. (2015) Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain". Expert Systems with Applications, 42(3): 1637-1643.

DOI: https://doi.org/10.1016/j.eswa.2014.09.044

[8] Grover N. (2014) A study of various Fuzzy Clustering Algorithms. International Journal of Engineering Research (IJER), 3(3): 177-181.

[9] Hiziroglu A. (2013) Soft computing applications in customer segmentation: State-of-art review and critique, Expert Systems with Applications, 40(16): 6491-6507.

DOI: https://doi.org/10.1016/j.eswa.2013.05.052

[10] Hong Ch. W. (2012) Using the Taguchi method for effective market segmentation, Expert Systems with Applications, 39(5): 5451-5459.

DOI: https://doi.org/10.1016/j.eswa.2011.11.040

[11] Mirza Hosseini H., Mahmoudi Maymand M., Karimii O. and Ahmadinejad M. (2013) Predicting the bank customer switching based on data mining technique, Spectrum: A Journal of Multidisciplinary Research, 2(10): 8-17.

[12] Liang Y. H. (2010) Integration of data mining technologies to analyze customer value for the automotive maintenance industry, Expert Systems with Applications, 37(12): 7489-7496.

DOI: https://doi.org/10.1016/j.eswa.2010.04.097

[13] Lim Ch. M., Kim Y. K. and Runyan R. (2013) Segmenting luxe-bargain shoppers using a fuzzy clustering method, International Journal of Retail & Distribution Management, 41(11/12): 848-868.

DOI: https://doi.org/10.1108/ijrdm-01-2013-0012

[14] Lin J. B., L. T. H. and Lee Y. G. (2012) Mining Important Association Rules on Different Customer Potential Value Segments for Life Insurance Database, Granular Computing (GrC), 2012 IEEE International Conference on Granular Computing, 283-288.

DOI: https://doi.org/10.1109/grc.2012.6468569

[15] RAMANATHAN, K.V. (2012) A study on policyholder's satisfaction with reference to Life Insurance Corporation of India, Thanjavur division, Doctor of Philosophy in Commerce, Bharathidasan University, Tiruchirappalli.

[16] Singh A. and Rana A. (2013) Mining of Customer data in an Automobile Industry using Clustering Techniques, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS). 5(3): 251-258.

[17] Sithic H. L. and Balasubramanian T. (2013) Survey of Insurance Fraud Detection Using Data Mining Techniques, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2(3): 62-65.

[18] Thomas B. and Nashipudimath M. (2012) Comparative Analysis Of Fuzzy Clustering Algorithms In Data Mining, International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), 1(7): 221-225.

[19] Umamaheswari K. and Janakiraman S. (2014) Role of Data mining in Insurance Industry, COMPUSOFT, An international journal of advanced computer technology, 3(6): 961-966.

[20] Wu R. Sh. and Chou P. H. (2011) Customer segmentation of multiple category data in e-commerce using a soft-clustering approach, Electronic Commerce Research and Applications, 10(3): 331-341.

DOI: https://doi.org/10.1016/j.elerap.2010.11.002

[21] Yan-li Z. and Jia Z. (2012) Research on Data Preprocessing In Credit Card Consuming Behavior Mining, Energy Procedia, 17: 638-643.

DOI: https://doi.org/10.1016/j.egypro.2012.02.147

[22] Yoo I., Alafaireet P., Marinov M., Pena-Hernandez K., Gopidi R., Chang J. F. and Hua L. (2012) Data Mining in Healthcare and Biomedicine: A Survey of the Literature, Journal of medical system, 36(4): 2431-2448.

DOI: https://doi.org/10.1007/s10916-011-9710-5
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