Abstract
One of the important tasks in customer relationship management is to find out the future profitability of individual and/or groups of customers. Data mining-based approaches only provide coarse-grained customer segmentation. It is also hard to obtain a high-precision structure model purely by using regression methods. This paper proposes a data-driven segmentation function that provides a precise regression model on top of the segmentation from a data mining approach. For a new customer, a structure model constructed from profit contribution data of current customers is adopted to assess the profitability. For an existing customer, external information such as stock value performance is taken into the regression model as well as historical trend prediction on the profit contribution. In addition, this paper shows how the proposed approach works and how it improves the customer profitability analysis through experiments on the sample data.
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References
Thomas, J., Reinartz, W., Kumar, V.: Getting The Most out of All Your Customers. Harvard Business Review, 117–123 (2004)
Zeithaml, V.A., Roland, T.R., et al.: The Customer Pyramid: Creating and Serving Profitable Customers. California Management Review 42(4), 118–142 (2001)
Kaplan, R.S., Narayanan, V.G.: Measuring and Managing Customer Profitability. Journal of Cost Management, 5–15 (September/October 2001)
Sherman, G.: How to Unlock Customer Profitability, http://www.crm2day.com/library/EEpkyZukEkLmhoVtRi.php
Pfeier, P.E., Bang, H.: Non-parametric Estimation of Mean Customer Lifetime Value. Journal of Interactive Marketing 19(4), 48–66 (2005)
Cabena, P., Choi, H.H., Kim, I.S., Otsuka, S., Reinschmidt, J., Saarenvirta, G.: Intelligent Miner for Data Applications Guide. IBM Redbook SG24-5252-00 (1999)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Michaud, P.: Clustering techniques. Future Generation Computer Systems 13(2), 135–147 (1997)
Huang, Y.M., Hung, C.M., Jiau, H.C.: Evaluation of Neural Networks and Data Mining Methods on a Credit Assessment Task for Class Imbalance Problem. Nonlinear Analysis: Real World Applications (to be published)
Venkatesan, R., Kumar, V.: A Customer Lifetime Value Framework for Customer Selec-tion and Resource Allocation Strategy. Journal of Marketing 68, 105–125 (2004)
Pfeifer, P.E., Haskins, M.E., Conroy, R.M.: Customer Lifetime Value, Customer Profit-ability, and the Treatment of Acquisition Spending. Journal of Managerial Issues XVII(1), 11–25 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Tian, C., Ding, W., Cao, R., Wang, M. (2006). Customer Future Profitability Assessment: A Data-Driven Segmentation Function Approach. In: Lee, J., Shim, J., Lee, Sg., Bussler, C., Shim, S. (eds) Data Engineering Issues in E-Commerce and Services. DEECS 2006. Lecture Notes in Computer Science, vol 4055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780397_3
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DOI: https://doi.org/10.1007/11780397_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35440-6
Online ISBN: 978-3-540-35441-3
eBook Packages: Computer ScienceComputer Science (R0)