Identify Valuable Customers of Taavon Insurance in Field of Life Insurance with Data Mining Approach
DOI:
https://doi.org/10.24200/jrset.vol4iss01pp1-10Abstract
Today the most crucial tasks of mentioned organizations is to recognize and attract customers and make a distinction between different groups of customers and ranking them and trying to keep the customers. To do this, the use of customer relationship management, and measurement of customer value is helpful. The main objective of this paper is segmentation insurance customers based on the factors affecting the value of our customers. In line with this objective, all data from 2311 life insurance customers are extracted from Taavon Insurance Company in a period of one year and according to the specification of insurance policy it was clustered by use of K-Means algorithm. Based on the results, customers were divided into five clusters. According to the insurance experts view, chosen specifications were weighted. After calculating the CLV, customers were ranked. Each cluster got specific name like, “Golden Customer”, “Valuable Customer”, “Favorable Customer”, “Less Favorable Value Customer” and “Less Valuable Customer”. By discovery of association rules based on the features of insured people, golden customers cluster for study of insurer’s behavior was analyzed at first step. By use of algorithm, decision tree for life insurance data was classified based on insured feature in order to predict position of every new entrance customer in each cluster. This research can be used to develop marketing plans and develop and offer products and services for each group of customers.References
Berger, P. D. and Nasr, N. I. Customer lifetime value: Marketing models and applications. [Journal of Interactive Marketing, 1998, 12 (1), pp. 17-30. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1094996898702506.
Berry M.J.A., Linoff G. S. Data Mining Techniques for marketing, sales, and customer relationship management, second edition, wiley, 2004.
Blattberg, R. C. and Deighton, J. Manage marketing by the customer equity test. Harvard business review, 1996, 74 (4), pp. 136-44.
Blattberg, R., Getz, G., and Thomas, J. Customer equity: Building and managing relationships as valuable assets. Harvard Business School Press, p. 2001.
Blocker, C. P. & Flint, D. J. Customer segments as moving targets: Integrating customer value dynamism into segment instability logic. Industrial Marketing Management, 2007, 36, 810-822.
Carpenter, P. Customer Lifetime Value: Do the Math. Marketing Computers, 1995.
Chapman, P. et al. Crisp-dm, step-by-step data mining guide, 2000.
Dwyer, F. P. Customer Lifetime Valuation to Support Marketing Decision Making. 1997, 11 (4), pp. 6-13.
Ghazanfari, M; Alizadeh, S. and Teimourpour, B. “Data Mining and Knowledge Discovery”, Iran Science and Industry University Publications.
Greenberg, P. ''CRM: Customer Relationship Management'', Capturing and Keeping Customer in internet real Time, at the Speed of Light;; McGraw-Hill co, 2002.
Gupta, S. et al. Modeling Customer Lifetime Value. Journal of Service Research, 2006, 9 (2), pp. 139-155. Available at: http://jsr.sagepub.com/cgi/doi/10.1177/1094670506293810 [Accessed August 19, 2013].
Han, J. and Kamber, M. Data Mining: Concepts and Techniques, Elsevier, 2006.
Hawkes, V. A. The Heart of the Matter: The Challenge of Customer Lifetime Value, 2000.
Hwang, Hyunseok, Jung, Taesoo and Suh, Euiho. An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Systems with Applications, 2004, 26 (2), pp. 181-188. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0957417403001337 [Accessed August 16, 2013].
Jutla, D. et al. Enabling and Measuring Electronic Customer Relationship Management Readiness. 34 th Hawaii International Confrence on System Sciences, 2001, 00 (c), pp. 1-10.
Kantardzic, M., Djulbegovic, B. & Hamdan, H. A data-mining approach to improving Polycythemia Vera diagnosis. Computers & industrial engineering, 2002, 43, 765-773.
Kim, J. e-CRM for e-Business, Gurm, 2000.
Kim, Jonghyeok, Suh, Euiho and Hwang, Hyunseok. A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 2003, 17 (2), pp. 5-19. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1094996803701315 [Accessed August 10, 2013].
Kim, G., Kim, A. & Sohn, S. Y. Conjoint analysis for luxury brand outlet malls in Korea with consideration of customer lifetime value. Expert Systems with Applications, 2009, 36, 922-932.
Kim, S. et al. Customer segmentation and strategy development based on customer lifetime value: A case study. 2006, 31,
Lewis, M. Customer Relationship Management: Maximizing Customer Lifetime Value: Wiley Eneyclopedia of Operations Research and management Science, 2010, pp. 1-11.
Liang, Y. H. Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications, 2010, 37, 7489-7496.
Liu, D.-R. and Shih, Y.-Y. Integrating AHP and data mining for product reeommendation based on customer lifetime value. Information and Management, 2005, 42 (3), pp. 387-400. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0378720604000394 [Accessed August 19,2013].
Peppard, J. Customer relationship management (CRM) in financial services. European Management Journal, 2000, 18, 312-327.
Peppers, D., Rogers, M., and Dorf, R. Is your company ready for one-to-one marketing. Harvard Business Review 1999, 77, 151-160.
Reichheld, F.F. and Sasser, W.E. Zero defections: quality comes to services. Harvard business review, 1990, 68 (5), pp. 105-11.
Rygielski, C., Wang, J.-C. & Yen, D.C. Data mining techniques for customer relationship management. Technology in society, 2002, 24, 483-502.
Saniei Abadeh, M; Mahmoudi, S. and Taherpour, M. “Applied Data Mining”, Niaz Danesh Publica-tions, 2nd Edition, 2014.
Tan, P., Steinbach, M. and Kumar, V. Introduction to Data Mining, Pearson Education.2005, pp. 101-107.
