Individually Adapted Sequential Conjoint-Choice Designs in the Presence of Consumer Heterogeneity


Speaker


Abstract

 

Modeling discrete choices from a heterogeneous group of respondents is usually done via the panel mixed logit model. A precise estimation of that model requires high-quality data. We propose an efficient individually adapted sequential approach for constructing conjoint choice experiments that guarantees the highest possible data quality for a given experimental budget. The approach 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 a variety of scenarios. 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:
Dr. S. Puntoni
Email