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Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing

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  • Stijn Viaene
  • Bart Baesens
  • Dirk Van den Poel
  • Guido Dedene
  • Jan Vanthienen

Abstract

In this paper, we try to validate existing theory on and develop additional insight into repeat‐purchase behavior in a direct marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) variables, using a neural network wrapper as our input pruning method. Results indicate that elimination of redundant and/or irrelevant inputs by means of the discussed input selection method allows us to significantly reduce model complexity without degrading the predictive generalization ability. It is precisely this issue that will enable us to infer some interesting marketing conclusions concerning the relative importance of the RFM predictor categories and their operationalizations. The empirical findings highlight the importance of a combined use of RFM variables in predicting repeat‐purchase behavior. However, the study also reveals the dominant role of the frequency category. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Stijn Viaene & Bart Baesens & Dirk Van den Poel & Guido Dedene & Jan Vanthienen, 2001. "Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 115-126, June.
    • Handle: RePEc:wly:isacfm:v:10:y:2001:i:2:p:115-126
    DOI: 10.1002/isaf.195
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    1. Dekimpe, Marnik G. & Degraeve, Zeger, 1997. "The attrition of volunteers," European Journal of Operational Research, Elsevier, vol. 98(1), pages 37-51, April.
    2. Piramuthu, Selwyn, 1999. "Financial credit-risk evaluation with neural and neurofuzzy systems," European Journal of Operational Research, Elsevier, vol. 112(2), pages 310-321, January.
    3. Frank M. Bass & Jerry Wind, 1995. "Introduction to the Special Issue: Empirical Generalizations in Marketing," Marketing Science, INFORMS, vol. 14(3_supplem), pages 1-5.
    4. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    5. Lacher, R. C. & Coats, Pamela K. & Sharma, Shanker C. & Fant, L. Franklin, 1995. "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Elsevier, vol. 85(1), pages 53-65, August.
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