Dear All,

You are cordially invited to an onsite academic seminar to be delivered by Prof. Yi Liu on June 7 (Friday). Details could be found below.

Seminar Information

Time and Date: 10:30 am - 12:00 pm, June 7, 2024 (Friday)

Venue: Room 603, Administration Building
Speaker: Prof. Yi Liu (University of Wisconsin-Madison)

Topic: Data and Algorithm: Designing Marketplace Analytics for Platform Sellers

Zoom Access

Link: https://cuhk-edu-cn.zoom.us/j/8913862860?pwd=WDdDbFBRbW9hSTVyTTRTancvbmI0dz09

Meeting ID: 891 386 2860

Passcode: 123456

About the Speaker

Yi Liu is an Assistant Professor of Marketing at the Wisconsin School of Business, UW-Madison. His major research interests lie in building theoretical models about technology (e.g., artificial intelligence) and online platforms to study their impact on customers and firms. His recent research has been published in top journals including Marketing Science and Management Science. His work about content moderation on social media platforms is covered by media such as Knowledge at Wharton and is cited by several practitioners. He has also developed and taught a new course, Technology Product Marketing, for undergraduate and MBA students at UW-Madison. He received his Ph.D. in Marketing from the Wharton School, University of Pennsylvania.

Abstract

The rise of e-commerce and the abundance of data have spurred AI-powered marketplace analytics, such as competitive intelligence and automated pricing, enabling sellers to make informed, data-driven decisions. Third-party providers (e.g., Jungle Scout) compete with platforms themselves (such as Amazon’s brand analytics) in offering marketplace analytics. Yet platforms have different attitudes toward third-party analytics providers and adopt various strategies in sharing data with them: some are restrictive while others adopt an open data-access policy (e.g., permitting data scraping or API sharing). In this paper, we ask why and when an e-commerce platform may benefit from an open data-access policy to accommodate competing third-party analytics service, despite the platform’s inherent advantages in data access and the capability to design the algorithm behind its own analytics service for better control over sellers’ actions. We answer this question by simultaneously analyzing the two intertwined decisions – (1) data-sharing policy and (2) algorithm design – for an e-commerce platform when designing its analytics service that predicts market competition levels to assist sellers’ pricing decision. We find that platforms may use an over-optimistic algorithm (by downplaying competition) in their own analytics to increase the total revenue on the platform. This may lead to sellers’ reluctance to adopt the platform’s analytics. When market competition is moderate, this can result in a lose-lose situation, prompting the platform to allow data access for third-party analytics providers. However, in highly competitive markets, it benefits both the platform to mislead sellers into believing the market is good, and the sellers, to be deluded into this belief. Overall, platforms only gain from an open data-access strategy in markets with moderately strong or weak competition. Finally, our analysis implies that privacy legislation like GDPR, aimed at curtailing platforms’ data-sharing practices, may inadvertently hurt consumer surplus.