Accounting for Discrepancies Between Online and Offline Product Evaluations

Daria Dzyabura, Srikanth Jagabathula () and Eitan Muller ()
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Srikanth Jagabathula: Stern School of Business, New York University, New York, New York 10012; Harvard Business School, Harvard University, Boston, Massachusetts 02163
Eitan Muller: Stern School of Business, New York University, New York, New York 10012; Arison School of Business, Interdisciplinary Center (IDC) Herzliya, 46101 Herzliya, Israel

Marketing Science, 2019, vol. 38, issue 1, 88-106

Abstract: Despite the growth of online retail, the majority of products are still sold offline, and the “touch-and-feel” aspect of physically examining a product before purchase remains important to many consumers. In this paper, we demonstrate that large discrepancies can exist between how consumers evaluate products when examining them “live” versus based on online descriptions, even for a relatively familiar product (messenger bags) and for utilitarian features. Therefore, the use of online evaluations in market research may result in inaccurate predictions and potentially suboptimal decisions by the firm. Because eliciting preferences by conducting large-scale offline market research is costly, we propose fusing data from a large online study with data from a smaller set of participants who complete both an online and an offline study. We demonstrate our approach using conjoint studies on two sets of participants. The group who completed both online and offline studies allows us to calibrate the relationship between online and offline partworths. To obtain reliable parameter estimates, we propose two statistical methods: a hierarchical Bayesian approach and a k- nearest-neighbors approach. We demonstrate that the proposed approach achieves better out-of-sample predictive performance on individual choices (up to 25% improvement), as well as aggregate market shares (up to 33% improvement).

Keywords: conjoint analysis; omnichannel; machine learning; Bayesian; consumer choice (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:38:y:2019:i:1:p:88-106

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