Implementing neural networks for decision support in direct marketing

Innovative methods of artificial intelligence such as artificial neural networks (ANNs) have been increasingly adopted to predict consumer responses to direct marketing. However, appropriate learning algorithms, evaluation criteria, and validation procedures are necessary for effective implementation of neural networks to provide decision support to managers. This study compares the performance of Bayesian neural networks with that of logistic regression and the backpropagation method in modelling consumer responses. The results of a tenfold stratified cross-validation suggest that although the three methods perform equally well under the error rate, Bayesian neural networks generate higher statistics for the Area under the Receiver Operating Characteristic Curve (AUROC) and cumulative lifts. The findings suggest that researchers should adopt effective learning algorithms, relevant evaluation criteria and appropriate validation procedures for neural networks to model consumer responses and solve marketing problems facing today's businesses.