Hybrid Approach to Choice-Based Conjoint Analysis

Abstract

Conjoint analysis is a popular tool for analysing consumer preferences in market research which has undergone rapid development throughout history. It is now generally agreed that choice-based conjoint (CBC) has a stronger theoretical background than traditional conjoint methods and that it mimics the real decision-making process of consumers more closely. When hierarchical Bayesian models allowed robust estimation of consumer preferences from sparse data available from choice-based conjoint tasks, formerly popular self-explicated or hybrid approaches lost their popularity. In this article, it is shown that hybrid approaches can be a useful alternative to pure CBC design. A hybrid approach to CBC that combines self-explicated questions on attribute levels with individualised choice tasks is suggested and illustrated on a real example and its efficiency is compared to traditional CBC and adaptive CBC. The results of the study support the hypothesis that this approach can be beneficial under certain circumstances and yield higher model fit while keeping the questionnaire length and respondent fatigue at an acceptable level.