(An) application of neural networks to the prediction of consumers choice behavior

Abstract

The inter-dependency in evaluating and selecting portfolio of multiple items means that concurrent or subsequent choices are dependent on the fact that the most prefered items have already been selected. There are previous studies on "behaves in the evaluation and selection of the inter-dependent choice of portfolio of multiple items" in marketing area. These studies have common thing that they address the subject by developing multiattribute preference model, which can be various by the method that treats the interaction between the items consisting the portfolio, and that the use the nortion of satiation in terms of each attribute or not. In this paper, we attempt to provide a new tool for predicting consumer behavior by adopting neural network approach. In order to carry out the experiment, we adopt the credit portfolio selection behavior which is one of the most suitable items to the Lancasterian tenet. For a comparative analysis of the predictability, the hit ratio of the proposed neural network is compared with those of previous models, the attribute satiation model, the flexible attribute overlap vector model and the fixed attribute overlap vector model. The result show that the proposed model is superior to the previous models in the prediction power and relatively easier to apply in practice than the previous models, for being free from the load to adopt what scheme of interation among items making up a portfolio and being able to save the cost to attain the additional information of attribute overlap.