Neural networks for the analysis and forecasting of advertising and promotion impact

Allocating advertising expenses and forecasting total sales levels are the key issues in retailing, especially when many products are covered and significant cross-effects among products are likely. Various statistical and econometric methods could be applied for such analyses. We explore how well neural networks can be used in analyzing the effects of advertising and promotion on sales in this article. The results reveal that the predictive quality of neural networks depends on the different frequency of data observed, i.e. daily or weekly data models, and the specific learning algorithms used. The study also shows that neural networks are capable of capturing the nonlinear aspects of complex relationships in non-stationary data. By performing sensitivity analysis, neural networks can potentially single out important input variables, thereby making it useful for scenario development and practical use. Copyright © 1998 John Wiley & Sons, Ltd.