Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity

We propose an efficient individually adapted sequential Bayesian approach for constructing conjoint-choice experiments, which uses Bayesian updating, a Bayesian analysis, and a Bayesian design criterion to generate a conjoint-choice design for each individual respondent based on the previous answers of that particular respondent. The proposed design approach is compared with three non-adaptive design approaches, two aggregate-customization approaches (based on the conditional logit model and on a mixed logit model), and the (nearly) orthogonal design approach, 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 we investigated in terms of the efficient estimation of individual-level part-worths and the prediction of individual choices. In the presence of high consumer heterogeneity, the improvements are impressive. The new method also performs well when the response accuracy is low, in contrast with the recently proposed adaptive polyhedral approach. Furthermore, the new methodology yields precise population-level parameter estimates, even though the design criterion focuses on the individual-level parameters.