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Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

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Author Info

  • Baesens, Bart
  • Verstraeten, Geert
  • Van den Poel, Dirk
  • Egmont-Petersen, Michael
  • Van Kenhove, Patrick
  • Vanthienen, Jan

Abstract

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer's future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian netw

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 156 (2004)
Issue (Month): 2 (July)
Pages: 508-523

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Handle: RePEc:eee:ejores:v:156:y:2004:i:2:p:508-523

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Web page: http://www.elsevier.com/locate/eor

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Cited by:
  1. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
  2. J.-J. Jonker & N. Piersma & D. Van Den Poel, 2003. "Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/214, Ghent University, Faculty of Economics and Business Administration.
  3. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
  4. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
  5. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
  6. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
  7. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
  8. Ülengin, Füsun & Önsel, Şule & Aktas, Emel & Kabak, Özgür & Özaydın, Özay, 2014. "A decision support methodology to enhance the competitiveness of the Turkish automotive industry," European Journal of Operational Research, Elsevier, vol. 234(3), pages 789-801.
  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.
  10. Wu, Wei-Wen & Lan, Lawrence W. & Lee, Yu-Ting, 2012. "Exploring the critical pillars and causal relations within the NRI: An innovative approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 230-238.
  11. M. Neyt & J. Albrecht & B. Clarysse & V. Cocquyt, 2003. "The Cost-Effectiveness of Herceptin® in a Standard Cost Model for Breast-Cancer Treatment in a Belgian University Hospital," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/180, Ghent University, Faculty of Economics and Business Administration.

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