Saderat Bank customer classification using decision tree Based on customer value
Subject Areas : Business ManagementHosein Beyorani 1 , Mahram Azimi 2
1 - Assistant Professor and Faculty Member of Tabriz University
2 - Graduated from the Master of Mathematical Statistics, Tabriz University
Keywords: Data mining, customer lifetime value, Decision tree, Customer Relationship Management, Customer value,
Abstract :
Customer satisfaction in today's business environment has gained importance. Many companies to increase profits and customer satisfaction are focused on customer value. Customer relationship Management (CRM) is tools to enhance customer relationships has emerged as the principal competing companies. Successful customer relationship management in companies start from identifying customer value beacuse Customer value provides important information for the development and management. Techniques such as data mining has led to the development of customer relationship management in new competition areas so that companies can be profitable in business competition. Through data mining -the discovery of hidden knowledge database- organizations can identify valuable customer and predict their future behavior and take useful and knowledge-based decisions. The purpose of this research is to gain the effective features to select valuable customer that can classify customers based on population characteristics and other variables relating to transactions in classes very low profit, low profit, high profit and very high profit. In this study the influence of demographic characteristics including age, education and job level also affect the degree of branch, bank branch location and number of transactions on customer value will be checked. Dependent variable in this research is customer value that is classified into four categories. Statistical population is the all customers have an active checking account with Bank Saderat Iran in Tabriz city. To review the case, the CHAID decision tree data mining algorithms were used.Results showed that the variables age, education level of customer and bank branches have no significant effect on customer value and the number of customer transactions with bank has most effective in identifying the class of customer.
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- Jin, K., R and Traiandis, C., H. (1997), “Management on Unmanageable”, Tehran, Institute of Education and Research of Defense Industries.
- Khaki, G. (2008), “Research method in management”, Baztab publication.
- Dwyer,F.R.(1997),Customer lifetime valuation to support marketing decision making. Journal of Interactive Marketing, 11(4),6–13.
- Hawkes, V. A. (2000), The heart of the matter: The challenge of customer lifetime value. CRM Forum Resources, 1-10.
- Kim, J (2000), e-CRM for e-Business,Gurm.
- Verhoef, P. C.,Donkers, B. (2001),Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32,189–199.
- Newton’s telecom dictionary, Harry newton, CMP Books. http://www.cmpbooks.com
- Zeithaml, V., Rust, R. T., Lemon, K. N. (2001), The customer pyramid: Creating and serving profitable customers. California Management Review, 42(4), 118–142.
- Chrise, R. Juen-Cheng, W., Dawid, C, Y. (2002), Data mining techniques for customer relationship management. Technology in society,483-502.
- Kim, J., Suh, E., Hwang, H. (2003), A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 17(2), 5–19
- Hyunseok, H., Taesoo, J., Euiho, S. (2004), An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry.
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- Huang, M. J., Chen, M. Y., Lee, S. C. (2007), Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications, 32(3), 856–867.
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- Hung, C., Tsai, C. F. (2008), Marketing segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Application, 34(1), 780–787.
- Benoit, D. F., Van de Poel, D. (2009),Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in the financial services. Expert Systems with Applications, 36, 1045–10484.
- Liang, Y. H. (2010), Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications. doi:10.1016/j.eswa.2010.04. 097
- Soman, k. p. Diwaker, S., Ajay, v. (2010), Insight into Data mining theory and practice -book. PHI Learning.
- Shui, H., Shui, X., Stephen, L., Leung. (2011), Segmentation of telecom customers based on customer value by decision tree model.
