Artificial Neural Networks and Discrete Choice Models

Abstract

The performance of artificial neural networks was compared with the performance of discrete choice models in predicting the purchase of products with weak involvement. A comprehensive literature review on the main paradigms of artificial neural networks was carried out, namely variants of the back-propagation algorithm, radial basis function, and genetic computing. Within the class of discrete choice models, the authors restricted the comparison to the multinomial logit model and the mixed logit. The performance of the models was measured in a database of grocery purchases in supermarkets. Artificial neural networks outperformed discrete choice models in predicting sales in supermarkets, and both types of models demonstrated strong predictive power. As a result, both can be reliably used in marketing to estimate individual or collective probabilities of supermarket product purchases.