Retargeted vs. Generic Product Recommendations: When is it Valuable to Present Retargeted Recommendations?

Xiang (Shawn) Wan (), Anuj Kumar () and Xitong Li ()
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Xiang (Shawn) Wan: Leavey School of Business, Santa Clara University, Santa Clara, California 95053
Anuj Kumar: Warrington College of Business, University of Florida, Gainesville, Florida 32611
Xitong Li: Department of Information Systems and Operations Management, HEC Paris, 78351 Jouy-en-Josas, France

Information Systems Research, 2024, vol. 35, issue 3, 1403-1421

Abstract: Although the effects of algorithmic product recommendations on product sales are understood, the differential effects of retargeted recommendations (recommended products a user has previously viewed) versus generic recommendations (recommended products a user has not previously viewed) are unclear. We conduct a field experiment to empirically examine the relative effect of retargeted versus generic recommendations on product sales at different stages of users’ purchase funnel. The product recommendations can affect sales by influencing the number of product impressions and their conversion rates (purchase probability conditional on impression). We separately estimate the effect of retargeted and generic recommendations on product impressions and conversion rates. We find that (i) generic recommendations increase conversion rates only in the early purchase funnel stage, but retargeted recommendations do not affect conversion rates, and (ii) both recommendations result in a higher number of impressions of recommended products. Overall, retargeted (generic) recommendations result in higher recommended and total product sales in the late (early) purchase funnel stage. We also conducted a controlled experiment on Amazon MTurk to unveil that retargeting (showing previously viewed products to users) drives the effect of retargeted recommendations. Our counterfactual simulations show that the retailer can obtain up to three percent higher product sales by applying our findings to the existing recommendation systems. Our research has implications for online retailers and the design of algorithmic product recommendation systems.

Keywords: algorithmic product recommendations; recommendation systems; collaborative filtering; retargeted recommendations; purchase funnel; field experiment; randomized online experiment (search for similar items in EconPapers)
Date: 2024
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