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.