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Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

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  • Vera Miguéis

    ()

  • Dirk Poel

    ()

  • Ana Camanho

    ()

  • João Falcão e Cunha

    ()

Abstract

Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products’ first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products’ sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests. Copyright Springer-Verlag Berlin Heidelberg 2012

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File URL: http://hdl.handle.net/10.1007/s11634-012-0121-3
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Bibliographic Info

Article provided by Springer in its journal Advances in Data Analysis and Classification.

Volume (Year): 6 (2012)
Issue (Month): 4 (December)
Pages: 337-353

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Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:337-353

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Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634

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Related research

Keywords: Customer relationship management; Churn analysis; Retailing; Classification; Logistic regression; Random forests; 91;

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  1. Takanobu Nakahara & Katsutoshi Yada, 2012. "Analyzing consumers’ shopping behavior using RFID data and pattern mining," Advances in Data Analysis and Classification, Springer, vol. 6(4), pages 355-365, December.
  2. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
  3. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
  4. Dudyala Anil Kumar & V. Ravi, 2008. "Predicting credit card customer churn in banks using data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 4-28.
  5. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
  6. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
  7. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
  8. Nathan Novemsky & Ravi Dhar, 2005. "Goal Fulfillment and Goal Targets in Sequential Choice," Journal of Consumer Research, University of Chicago Press, vol. 32(3), pages 396-404, December.
  9. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
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