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Predicting Mail-Order Repeat Buying: Which Variables Matter?

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  • D. VAN DEN POEL

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Abstract

In this study, we propose a customer-oriented conceptual model of segmentation variables for mail-order repeat buying behavior. We investigate (1) from a theoretical perspective what customer-related variables should be included in response models for modeling repeat purchasing, and (2) empirically validate how these variables perform for predictive purposes. We use binary logit modeling. Our results confirm that all three traditionally-used R(ecency), F(requency) and M(onetary value) variables are very important in predicting who is going to purchase during the next mailing period, with frequency being the most important one. In total, they account for 50 % of the ‘room for improvement’ in terms of AUC performance. However, next to the RFM variables, our findings suggest that at least three other variables significantly increase the predictive performance of the models: (1) credit usage, (2) length of relationship, and (3) general mail-order buying behavior. Depending on the context of the specific company use of these additional variables may translate into millions Euro of additional profit. Furthermore, we conclude that buying additional data from external sources is not economically justified when predicting repeat purchasing behavior.

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Bibliographic Info

Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 03/191.

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Length: 31 pages
Date of creation: Aug 2003
Date of revision:
Handle: RePEc:rug:rugwps:03/191

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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. De Cannière, Marie Hélène & De Pelsmacker, Patrick & Geuens, Maggie, 2009. "Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior," Journal of Business Research, Elsevier, vol. 62(1), pages 82-92, January.
  3. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
  4. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
  5. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.

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