The data boom in e-commerce has spurred 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 API-sharing) while 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 ecommerce platform can make when designing analytics to predict market competition to assist sellers’ pricing decisions involving (1) data-access policy and (2) algorithm design. We find that platforms may use over-optimistic algorithms (downplaying competition) in their own analytics. This may make sellers 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. In highly competitive markets, however, it benefits the platform to mislead sellers into believing the market is good and the sellers to be deluded into this belief. 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.
Note: Research papers posted on SSRN, including any findings, may differ from the final version chosen for publication in academic journals.