Data and Algorithms: Strategic Disclosure of Competitiveness on Platforms Through Marketplace Analytics

The data boom in e-commerce has spurred artificial intelligence (AI)-powered marketplace analytics, but platforms hold the data reins. Some adopt open data-access policies with third-party analytics providers (e.g., permitting data-scraping or application programming interface sharing), whereas others are restrictive. We ask why and when an e-commerce platform—capable of designing its own analytics to control sellers’ actions—may benefit from open data-access policies to accommodate competing third-party analytics services, despite the potential drawbacks of weakening its data advantages and control. We analyze two intertwined decisions an e-commerce platform can make when designing analytics to predict market competitiveness and assist sellers’ pricing decisions involving (1) data-access policy and (2) algorithm design. We find that platforms may use over-optimistic algorithms (by increasing the likelihood of generating low-competition signals) in their own analytics to boost commissions. Because sellers do not trust the platform to act in their best interest, they may be reluctant to adopt a platform’s analytics, resulting in a lose–lose situation and prompting the platform to allow data access to third-party providers. Overall, platforms gain from open data-access strategies in markets with moderately strong or weak competition. Finally, privacy legislation aimed at curtailing platforms’ data-sharing practices may inadvertently hurt consumers.