How Do Product Recommendations Help Consumers Search Products? Evidence of from a Field Experiment

Although conventional wisdom suggests that product recommendations should benefit consumers, there is a lack of evidence on how they help consumers, specifically, whether product recommendations can algorithmically identify products aligned to consumers’ preferences and thus help them find higher-value products. We estimate the benefit of the collaborative filtering (CF) recommendation system by conducting a randomized field experiment on a US apparel retailer’s website. We collect unique data on the affinity scores computed by a CF algorithm to estimate how product recommendations help consumers search for higher-value products that are lower-priced, fit their tastes better, or both. We show that the discovery of lower-priced and better-fit products are the underlying reasons for higher purchase probability (lower likelihood of failed search efforts) of consumers under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, which provide additional evidence for these underlying reasons. Finally, we find that consumers substitute other search tools on the website with product recommendations when available. Our findings have implications for online retailers, policymakers, regulators, and the design of recommendation systems.