Journal
MARKETING SCIENCE
Volume 27, Issue 3, Pages 443-460Publisher
INFORMS
DOI: 10.1287/mksc.1070.0306
Keywords
conjoint analysis; recommender system; online decision aid; efficiency
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In their purchase decisions, online customers seek to improve decision quality while limiting search efforts. In practice, many merchants have understood the importance of helping customers in the decision-making process and provide online decision aids to their visitors. In this paper, we show how preference models which are common in conjoint analysis can be leveraged to design a questionnaire-based decision aid that elicits customers' preferences based on simple demographics, product usage, and self-reported preference questions. Such a system can offer relevant recommendations quickly and with minimal customer input. We compare three algorithms cluster classification, Bayesian treed regression, and stepwise componential regression -to develop an optimal sequence of questions and predict online visitors' preferences. In an empirical study, stepwise componential regression, relying on many fewer and easier-to-answer questions, achieved predictive accuracy equivalent to a traditional conjoint approach.
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