Reconsidering optimal experimental design for conjoint analysis

The quality of Conjoint Analysis estimations heavily depends on the alternatives presented in the experiment. An efficient selection of the experiment design matrix allows more information to be elicited about consumer preferences from a small number of questions, thus reducing experimental cost and respondent's fatigue. The statistical literature considers optimal design algorithms (Kiefer, 1959), and typically selects the same combination of stimuli more than once. However in the context of conjoint analysis, replications do not make sense for individual respondents. In this paper we present a general approach to compute optimal designs for conjoint experiments in a variety of scenarios and methodologies: continuous, discrete and mixed attributes types, customer panels with random effects, and quantile regression models. We do not compute good designs, but the best ones according to the size (determinant or trace) of the information matrix of the associated estimators without repeating profiles as in Kiefer's methodology. We handle efficient optimization algorithms to achieve our goal, avoiding the use of widespread ad-hoc intuitive rules.