Advanced conjoint analysis using feature selection via support vector machines
Author
dc.contributor.author
Maldonado, Sebastián
Author
dc.contributor.author
Montoya Moreira, Ricardo
Author
dc.contributor.author
Weber, Richard
Admission date
dc.date.accessioned
2017-03-27T19:37:32Z
Available date
dc.date.available
2017-03-27T19:37:32Z
Publication date
dc.date.issued
2015
Cita de ítem
dc.identifier.citation
European Journal of Operational Research 241 (2015) 564–574
es_ES
Identifier
dc.identifier.other
10.1016/j.ejor.2014.09.051
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/143318
Abstract
dc.description.abstract
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.
es_ES
Patrocinador
dc.description.sponsorship
FONDECYT
11121196
1140831
11110173
Complex Engineering Systems Institute
ICM: P-05-004-F
CONICYT: FBO16