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

Author

Listed:
  • 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 probstudy. 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 network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally b
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
  • Handle: RePEc:eee:ejores:v:156:y:2004:i:2:p:508-523
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    References listed on IDEAS

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    1. Knox, Simon, 1998. "Loyalty-based segmentation and the customer development process," European Management Journal, Elsevier, vol. 16(6), pages 729-737, December.
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. Lee, Changyong & Song, Bomi & Park, Yongtae, 2015. "An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 285-301.
    6. 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.
    7. 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.
    8. 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.
    9. repec:pal:jorsoc:v:59:y:2008:i:9:d:10.1057_palgrave.jors.2602597 is not listed on IDEAS
    10. 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.
    11. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    12. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    13. Jonker, J-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. 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.
    15. repec:eee:joreco:v:16:y:2009:i:4:p:271-280 is not listed on IDEAS
    16. repec:pal:jorsoc:v:61:y:2010:i:1:d:10.1057_jors.2009.104 is not listed on IDEAS
    17. 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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