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Wrapped feature selection for neural networks in direct marketing.

By Stijn Viaene, Bart Baesens, D Van den Poel, Guido Dedene and Jan Vanthienen


In this paper, we try to validate existing theory on and develop additional insight into repeat purchasing behaviour 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) features, using a wrapped feature selection method in a neural network context. Results indicate that elimination of redundant/irrelevant features by means of the discussed feature selection method, allows to significantly reduce model complexity without degrading generalisation ability. It is precisely this issue that will allow to infer some very interesting marketing conclusions concerning the relative importance of the RFM-predictor categories. The empirical findings highlight the importance of a combined use of all three RFM variables in predicting repeat purchase behaviour. However, the study also reveals the dominant role of the frequency variable. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.Marketing; Networks; Selection; Theory; Purchasing; Case studies; Studies; Model; Variables; Yield; Classification; Neural networks;

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  1. (1988). A direct mail customer purchase model.
  2. (1992). Bayesian interpolation.
  3. (1998). Continuous predictive modeling: a comparative analysis.
  4. (1998). Database marketing modeling for financial services using hazard rate models.
  5. (1994). Direct marketing: strategy, planning, execution, 3rd edition,
  6. (1997). Gauss-Newton approximation to bayesian learning.
  7. (1986). Indices of discrimination or diagnostic accuracy. Their ROCs and implied models.
  8. (1995). Introduction to the special issue: empirical generalisations in marketing.
  9. (1994). Irrelevant Features and the Subset Selection Problem.
  10. Issues and problems in applying neural computing to target marketing.
  11. (1989). Multilayer Feedforward Networks are Universal Approximators.
  12. (1992). Neural Networks and the bias/variance dilemma.
  13. Note on Generalisation, Regularization and Architecture Selection in Nonlinear Learning Systems.
  14. (1997). On Bias, Variance,
  15. (1995). Optimal selection for direct mail.
  16. Partial Retraining: A New Approach to Input Relevance Determination.
  17. (1977). Picking them by their batting averages' recencyfrequency-monetary method of controlling circulation, Manual release 2103, Direct MaillMarketing Association,
  18. (1992). Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction.
  19. (1998). Quantitative approaches for profit maximization in direct marketing.
  20. Quantitative database methods. The Direct Marketing Handbook,
  21. (1995). Realize Your Customers' Full Profit Potential.
  22. (1996). Relationship Marketing,
  23. (1999). Response Modeling for Database Marketing using Binary Classification.
  24. (1993). Target selection for direct marketing.
  25. (1997). The attrition of volunteers,
  26. (1990). Zero Defections: Quality comes to Service.