Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning

Dongling Huang () and Lan Luo ()
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Dongling Huang: David Nazarian College of Business and Economics, California State University, Northridge, Northridge, California 91330
Lan Luo: Marshall School of Business, University of Southern California, Los Angeles, California 90089

Marketing Science, 2016, vol. 35, issue 3, 445-464

Abstract: As technology advances, new products (e.g., digital cameras, computer tablets, etc.) have become increasingly more complex. Researchers often face considerable challenges in understanding consumers’ preferences for such products. This paper proposes an adaptive decompositional framework to elicit consumers’ preferences for complex products. The proposed method starts with collaborative-filtered initial part-worths, followed by an adaptive question selection process that uses a fuzzy support vector machine active learning algorithm to adaptively refine the individual-specific preference estimate after each question. Our empirical and synthetic studies suggest that the proposed method performs well for product categories equipped with as many as 70 to 100 attribute levels, which is typically considered prohibitive for decompositional preference elicitation methods. In addition, we demonstrate that the proposed method provides a natural remedy for a long-standing challenge in adaptive question design by gauging the possibility of response errors on the fly and incorporating the results into the survey design. This research also explores in a live setting how responses from previous respondents may be used to facilitate active learning of the focal respondent’s product preferences. Overall, the proposed approach offers new capabilities that complement existing preference elicitation methods, particularly in the context of complex products.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0946 .

Keywords: new product development; support vector machines; machine learning; active learning; adaptive questions; conjoint analysis (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (19)

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