A study on forecasting of consumer's choice using artificial neural network

Artificial neural network (ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers' choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model. Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models which need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers' choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample were compared with that of multiple discriminant analysis (MDA) and logistic regression (Logit) model. The performance of the ANN models were better than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models was improved.