Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

Amy Weng,Christopher Gan,Visit Limsombunc,Mike Clemes

doi:10.3844/jssp.2005.211.219

Abstract

Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN) to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN), a special class of neural networks and a MLFN with a logistic model on consumers' choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers' use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN) exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

Highlights

Quantitative analysis for forecasting in business and marketing, especially in consumer behavior and in the consumer decision-making process, has become more popular in business practices
The estimated coefficients indicate that service quality dimensions and user input factors have a positive impact on consumers’ likelihood to electronic banking. This implies the level of service quality in electronic, the independence and freedom associated with electronic banking and the enjoyment that could be derived from electronic banking will favourably influence consumers’ decision to use electronic banking
The logistic model can be considered as an accurate prediction model because the overall correct classification rates are high, above 90.00% for both in-sample and out-of-sample predictions

Summary

Introduction

Quantitative analysis for forecasting in business and marketing, especially in consumer behavior and in the consumer decision-making process (consumer choice model), has become more popular in business practices. The ability to understand and to accurately predict a consumer decision can lead to more effectively targeting products, cost effectiveness in marketing strategies, increasing sales and result in substantial improvement in the overall profitability of the firm. Conventional econometric models, such as discriminant analysis and logistic regression can predict consumers’ choices, but recently, there has been a growing interest in using ANN to analyze and the model consumer decision-making process. Neural networks have been generally applied to two different categories of problems - recognition problems and generalisation problems.

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Conclusion