One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant attributes. Most of these approaches rely on the post-processing of the estimated preferences to establish the importance of such variables. This paper presents new techniques that simultaneously identify consumer preferences and the most relevant attributes. The proposed approaches have two appealing characteristics. Firstly, they are grounded on a support vector machine formulation that has proved important predictive ability in operations management and marketing contexts and secondly they obtain a more parsimonious representation of consumer preferences than traditional models. We report the results of an extensive simulation study that shows that unlike existing methods, our approach can accurately recover the model parameters as well as the relevant attributes. Additionally, we use two conjoint choice experiments whose results show that the proposed techniques have better fit and predictive accuracy than traditional methods and that they additionally provide an improved understanding of customer preferences. 
Advanced conjoint analysis using feature selection via support vector machines