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Customer Lifetime Value Models for Decision Support

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Abstract

We present and discuss the important business problem of estimating the effect of marketing activities on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We describe in detail how we build on this approach to estimate the effects of retention campaigns on Lifetime Value, and also discuss its application in other situations. Our solution has been successfully implemented by the Business Insight (BI) Professional Services.

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References

  • Cox, D.R. 1972. Regression models and life tables. Journal of the Royal Statistical Society, B34, 187–220.

    Google Scholar 

  • Friedman, J.H. 1997.On bias, variance, 0/1-loss and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1):55–77.

    Google Scholar 

  • Helsen, K. and Schmittlein, D.C. 1993. Analyzing duration times in marketing: Evidence for the effectiveness of Hazard rate models. Marketing Science, 11:395–414.

    Google Scholar 

  • Inger, A., Vatnik, N., Rosset, S., and Neumann, E. 2000. KDD-cup 2000 Question 1 Winner's report. SIGKDD Explorations, 2(2):94.

  • Kaplan, E.L. and Meier, R. 1958. Non-parametric estimation from incomplete observations. Journal of the American Statistical Association, 53:457–481.

    Google Scholar 

  • Mani, D.R., Drew, J., Betz, A., and Datta, P. 1999. Statistics and data mining techniques for lifetime value modeling. Proceedings of KDD-99, 94–103.

  • Murad, U. and Pinkas, G. 1999. Unsupervised Profiling for Identifying Superimposed Fraud. PKDD-99, 251–261.

  • Neumann, E., Vatnik, N., Rosset, S., Duenias, M., Sassoon, I., and Inger, A. 2000. KDD-cup 2000 Question 5 Winner's report. SIGKDD Explorations, 2(2):98.

  • Novo, J. 2001. Maximizing Marketing ROI with Customer Behavior Analysis, http://www.drilling-down.com.

  • Rosset, S. and Inger, A. 2000. KDD-cup 99: Knowledge discovery in a charitable organization's donor database. SIGKDD Explorations, 1(2):85–90.

    Google Scholar 

  • Rosset, S., Murad, U., Neumann, E., Idan, I., and Pinkas, G. 1999. Discovery of fraud rules for telecommunications—Challenges and solutions. Proceedings of KDD-99, 409–413.

  • Rosset, S., Neumann, E., Eick, U., Vatnik, N., and Idan, I. 2001. Evaluation of prediction models for marketing campaigns. Proceedings of KDD-2001, 456–461.

  • Venables, W.N. and Ripley, B.D. 1999. Modern Applied Statistics with S-PLUS, 3rd edition. Springer-Verlag.

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Rosset, S., Neumann, E., Eick, U. et al. Customer Lifetime Value Models for Decision Support. Data Mining and Knowledge Discovery 7, 321–339 (2003). https://doi.org/10.1023/A:1024036305874

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  • DOI: https://doi.org/10.1023/A:1024036305874

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