Efficient Experimental Designs for Choice-Based Conjoint Analysis



In this study, we propose an efficient individually adapted sequential Bayesian approach for constructing conjoint choice experiments. It uses Bayesian updating, a Bayesian analysis and a Bayesian design criterion for generating a conjoint-choice design for each individual respondent based on previous answers of that particular respondent. The proposed design approach is compared with two non-adaptive design approaches, the aggregate-customization design and the (nearly) orthogonal design approaches, under various degrees of response accuracy and consumer heterogeneity. A simulation study shows that the individually adapted sequential Bayesian conjoint-choice designs perform better than the benchmark approaches in all scenarios that we studied. In the presence of high consumer heterogeneity, the improvements achieved by the new method in terms of precision of estimation and accuracy of prediction are impressive. A key result of our simulations is that the new sequential approach to conjoint-choice design yields substantially better information about individual-level preferences than existing approaches. The new method also performs well when the response accuracy is low, in contrast with the recently proposed adaptive polyhedral choice-based question design approach.
Contact information:
Prof. Patrick Groenen