Topic:The Informational Role of Sponsored Advertising on Online Retail Marketplaces
Speaker:Fei Long, Columbia Business School
Time:Friday, 02 November, 13:30-15:00
Location:Room 216, Guanghua Building 2
E-commerce platforms, such as Amazon, Alibaba and Flipkart, have transformed the retail sector by matching sellers and consumers at an unprecedented scale. These platforms operate their internal search engines to help buyers find relevant products from a huge number of sellers, and also allow sellers to advertise to consumers, via ad auctions, for positions in the search listing. Determining an optimal ranking of products in response to a search query is a challenging problem for the platform because sellers have certain private information about products that the platform does not have. However, sellers’ bids in ad auctions reveal information about their types, which can be used to refine the ranking of products in the organic listing. While doing so may help consumers find an appropriate product match (information effect) thereby also helping sellers and the platform, it may negatively impact sellers’ profits (competition effect) leading to reduced seller participation. Consequently, it is not clear how search engine design will affect the platform’s advertising revenues, commission rates and sellers’ pricing decisions, all of which directly affect the platform’s performance. I explore how a platform’s internal search engine should be designed, by modeling this ecosystem as a Bayesian game with private information, where a product’s match with consumers is a priori only known to the seller. The platform can use the sellers’ bids for sponsored advertising to estimate this information and improve the ordering of its organic listings (strategic listing). This strategy is compared against the benchmark case of ranking organic links independently from sellers’ bids (independent listing). We find that while the strategic case tends to dominate, it also has a major impact on the platform’s choice of commission as well as on sellers’ pricing and participation. In particular, we find that strategic listing of organic links reduces price competition between sellers but increases advertising competition between them, thereby also reducing seller participation. These effects are stronger when consumers’ search cost becomes higher, or when the product fits the consumer’s need with an extreme probability. We also prescribe the optimal commission rate that a platform should charge, by balancing between the revenue from commissions and the revenue from advertising.
Fei Long is PhD student at Columbia Business School. Her research interests lie in understanding important and emerging phenomena in marketing, typically those driven by the digital economy, using both theoretical modeling and data-driven approaches (primarily statistical machine learning). Her dissertation research focuses on e-commerce platforms and digital advertising. In addition, she is also interested in topics in agency theory and salesforce compensation. Prior to joining Columbia, Fei received her M.S. degree in Operations Research from Columbia University, and received her B.S. degree in Electronics Engineering from Tsinghua University. She has previously worked as a data scientist at Helix Partners (a hedge fund) and Hewlett-Packard.
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