Statistical and cognitive optimization of experimental designs in conjoint analysis

Conjoint analysis has become the most used technique for measuring preferences for new products to be launched in the market. Experimental design models are key elements for its use in market research. Suchmodels involve amatrix in which attributes and levels are combined, making product concepts that respondents then evaluate. Experimental design has emerged as a key element in conjoint analysis’ success because its application generates statistical and reliability implications for part-worth factor estimations and for the type of heuristics followed by respondents. This paper proposes a conceptualization of both statistical and cognitive efficiency criteria for experimental designs. A review of themost used statistical optimization criteria is presented, as well as a methodology for optimizing cognitive efficiency. Finally, we suggest a dynamic algorithm for optimizing the objective function in a sequential manner. © 2015 AEDEM. Published by Elsevier España, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).