Conjoint analysis for complex services using clusterwise Hierarchical Bayes procedures

Abstract

Conjoint analysis is a widely used method in marketing research. Some problems occur when conjoint analysis is used for complex services where the perception of and the preference for attributes and levels considerably varies among individuals. Clustering and clusterwise estimation procedures as well as Hierarchical Bayes (HB) estimation can help to model this perceptual uncertainty and preference heterogeneity. In this paper we analyze the advantages of clustering and clusterwise HB as well as combined estimation procedures of collected preference data for complex services and therefore extend the analysis of Sentis and Li (2002).