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

We collaborate with a large e-commerce platform and conduct a year-long experiment among new sellers on the platform. The treatment group receives access to a free and customized entrepreneur training program, in which an AI algorithm dynamically assigns training materials to sellers based on their 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), largely driven by higher traffic and enhanced marketing activities. To investigate consumer-side benefit while boosting statistical power, we construct a panel dataset of consumer-seller pairs across consideration sets that are observed in search sessions. Using exhaustive controls and fixed effects, we show that training also improves new-seller conversion, which is not driven by selection through heightened entry barrier but due to a direct effect on quality. We then estimate an empirical model to capture unobserved consumer preference heterogeneity and compute welfare. 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%.