Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces

Abstract

Most online sales worldwide take place in marketplaces that connect sellers and buyers. The presence of numerous third-party sellers leads to a proliferation of listings for each product, making it difficult for customers to choose between the available options. Online marketplaces adopt algorithmic tools to curate how the different listings for a product are presented to customers. This paper focuses on one such tool, the Buybox, that algorithmically chooses one option to be presented prominently to customers as a default option. We leveraged the staggered introduction of the Buybox within a prominent product category in a leading online marketplace to study how the Buybox impacts marketplace dynamics. Our findings indicate that adopting Buybox results in a substantial increase in marketplace orders and visits. Implementing Buybox reduces the frictions customers and sellers face. On the customer side, we find a reduction of search frictions, evidenced by an increase in conversion rates and a higher impact of Buybox on the mobile channel, which has significantly higher search frictions than desktop channel. On the seller side, the number of sellers offering a product increases following the implementation of Buybox. Customers benefit from lower prices and higher average quality levels when competition in Buyboxes is high. After the introduction of the Buybox, the marketplace also becomes more concentrated. Our paper contributes to the burgeoning literature on the role of algorithms in platforms by examining how algorithmic curation impacts the participants of the marketplace as well as the marketplace dynamics.

Keywords: algorithms, algorithmic curation, online marketplaces, empirical operations, Buybox