A Simple Bayesian Procedure for Estimation in a Conjoint Model

Abstract

The authors propose a simple Bayesian approach which combines self-explicated data with conjoint data for estimating individual-level conjoint models. Analytical results show that, with typical conjoint data, improvement may be expected over the estimation and prediction results obtained with ordinary least squares (OLS). The expected improvement in prediction is confirmed by pilot empirical results.