A COMPARATIVE-ANALYSIS OF NEURAL NETWORKS AND STATISTICAL-METHODS FORPREDICTING CONSUMER CHOICE

This paper presents a definitive description of neural network methodo logy and provides an evaluation of its advantages and disadvantages re lative to statistical procedures. The development of this rich class o f models was inspired by the neural architecture of the human brain. T hese models mathematically emulate the neurophysical structure and dec ision making of the human brain, and, from a statistical perspective, are closely related to generalized linear models. Artificial neural ne tworks are, however, nonlinear and use a different estimation procedur e (feed forward and back propagation) than is used in traditional stat istical models (least squares or maximum likelihood). Additionally, ne ural network models do not require the same restrictive assumptions ab out the relationship between the independent variables and dependent v ariable(s). Consequently, these models have already been very successf ully applied in many diverse disciplines, including biology, psycholog y, statistics, mathematics, business, insurance, and computer science. We propose that neural networks will prove to be a valuable tool for marketers concerned with predicting consumer choice. We will demonstra te that neural networks provide superior predictions regarding consume r decision processes. In the context of modeling consumer judgment and decision making, for example, neural network models can offer signifi cant improvement over traditional statistical methods because of their ability to capture nonlinear relationships associated with the use of noncompensatory decision rules. Our analysis reveals that neural netw orks have great potential for improving model predictions in nonlinear decision contexts without sacrificing performance in linear decision contexts. This paper provides a detailed introduction to neural networ ks that is understandable to both the academic researcher and the prac titioner. This exposition is intended to provide both the intuition an d the rigorous mathematical models needed for successful applications. in particular, a step-by-step outline of: how to use the models is pr ovided along with a discussion of the strengths and weaknesses of the model. We also address the robustness of the neural network models and discuss how far wrong you might go using neural network models versus traditional statistical methods. Herein, we report the results of two studies. The first is a numerical simulation comparing the ability of neural networks with discriminant analysis and logistic regression at predicting choices made by decision rules that vary in complexity. Th is includes simulations involving two noncompensatory decision rules a nd one compensatory decision rule that involves attribute thresholds. In particular, we test a variant of the satisficing rule used by Johns on et al. (1989) that sets a lower bound threshold on all attribute va lues and a ''latitude of acceptance'' model that sets both a lower thr eshold and an upper threshold on attribute values, mimicking an ''idea l point'' model (Coombs and Avrunin 1977). We also test a compensatory rule that equally weights attributes and judges the acceptability of an alternative based on the sum of its attribute values. Thus, the sim ulations include both a linear environment, in which traditional stati stical models might be deemed appropriate, as well as a nonlinear envi ronment where statistical models might not be appropriate. The complex ity of the decision rules was varied to test for any potential degrada tion in model performance. For these simulated data it is shown that, in general, the neural network model outperforms the commonly used sta tistical procedures in terms of explained variance and out-of-sample p redictive accuracy. An empirical study bridging the behavioral and sta tistical lines of research was also conducted. Here we examine the pre dictive relationship between retail store image variables and consumer patronage behavior. A direct comparison between a neural network mode l and the more commonly encountered techniques oi discriminant analysi s and factor analysis followed by logistic regression is presented. Ag ain the results reveal that the neural network model outperformed the statistical procedures in terms of explained variance and out-of-sampl e predictive accuracy. We conclude that neural network models offer su perior predictive capabilities over traditional statistical methods in predicting consumer choice in nonlinear and linear settings.