AI Training for Online Entrepreneurs: An Experiment with Two Million New Sellers on an E-commerce Platform

We conduct a year-long experiment among new sellers on a large e-commerce platform. The treatment group receives access to a free and customized entrepreneur training program, in which an AI algorithm assigns training materials to sellers based on their real-time operations data. With a 24% take-up rate, new sellers that are eligible for training see 1.7% higher revenue on average (6.6% ATT), driven largely by higher traffic and enhanced marketing activities. To dissect the mechanisms of this effect and the economic surplus generated, we develop a model of platform ranking as well as consumers' visit and purchase choices. We then use search records to construct a panel dataset of consumer-seller pairs across consideration sets. With exhaustive fixed effects, we show that training raises new sellers' product value, boosting consumers' visit and purchase rates simultaneously. But the effect is small compared to an entry barrier driven by search friction, which is unchanged for treated sellers. Lastly, model estimation shows that although only 0.25% of products in consumers' consideration sets are from treated new sellers, removing the program would reduce consumer surplus by 0.07%.