Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids

Abstract

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.