Bayesian modeling in conjoint analysis

Abstract

Statistical analysis in marketing is largely influenced by the availability of various types of data. There is sudden increase in the number and types of information available to market researchers in the last decade. In such conditions, traditional statistical methods have limited ability to solve problems related to the expression of market uncertainty. The aim of this paper is to highlight the advantages of bayesian inference, as an alternative approach to classical inference. Multivariate statistic methods offer extremely powerful tools to achieve many goals of marketing research. One of these methods is the conjoint analysis, which provides a quantitative measure of the relative importance of product or service attributes in relation to the other attribute. The application of this method involves interviewing consumers, where they express their preferences, and statistical analysis provides numerical indicators of each attribute utility. One of the main objections to the method of discrete choice in the conjoint analysis is to use this method to estimate the utility only at the aggregate level and by expressing the average utility for all respondents in the survey. Application of hierarchical Bayesian models enables capturing of individual utility ratings for each attribute level.

Keywords

• marketing research
• bayesian inference
• conjoint analysis
• hierarchical bayesian models