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Deal effect curve and promotional models: Using machine learning and bootstrap resampling test

Abstract:

Promotional sales have become in recent years a paramount issue in the marketing strategies of many companies, specially in the current economic situation. Empirical models of consumer promotional behavior, mostly based on machine learning methods, are becoming more usual than theoretical models, given the complexity of the promotional interactions and the availability of electronic recordings. However, the performance description and comparison among promotion models are usually made in terms of absolute and empirical values, which is a limited handling of the information. Here we first propose to use a simple nonparametric statistical tool, the paired bootstrap resampling, for establishing clear cut-off test based comparisons among methods for machine learning based promotional models, by simply taking into account the estimated statistical distribution of the actual risk. The method is used to determine the existence of actual statistically significant differences in the performance of different machine design issues for multilayer perceptron based marketing models, in a real database of everyday goods (milk products). Our results show that paired bootstrap resampling is a simple and effective procedure for promotional modeling using machine learning techniques.