Mining sales data using a neural network model of market response

Abstract

Modeling aggregate market response is a core issue in marketing research. In this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, Multiplicative Competitive Interaction (MCI) models. Forecasts are compared in two separate studies: (1) the Information Resources Inc. (IRI) coffee dataset from Marion, IN and (2) the A. C. Nielsen catsup dataset from Sioux Falls, SD. Our results suggest neural networks are a useful substitute for MCI models when there are too few observations available to estimate a fully-extended MCI model. Implications are discussed.