Wrapped feature selection for neural networks in direct marketing.

Abstract

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;