Automated Targeted Bidding for Sponsored Ads on E-Commerce Platforms.

This article analyzes the effects of dynamic bidding—an automated targeted bidding system optimizing sponsored ad bids based on predicted consumer purchase likelihood—on seller competition, platform revenue, and consumer outcomes on e-commerce platforms with horizontally differentiated sellers. It finds that dynamic bidding induces sellers to strategically raise prices to limit ad competition, which can paradoxically lower sponsored ad conversion rates despite more precise targeting. For the platform, dynamic bidding often reduces ad revenue but increases sales commission revenue, making its profitability dependent on a sufficiently high commission rate and the precision of consumer information. The study also shows that a pay-per-conversion auction scheme may fail to improve outcomes due to adverse seller pricing incentives, and that the presence of consumers who are not solely ad-driven can amplify dynamic bidding’s effect in softening price competition, benefiting the platform but potentially reducing seller gains. Overall, dynamic bidding reshapes the interplay between pricing and advertising in e-commerce, with nuanced implications for all market participants depending on market differentiation, information precision, and platform policies.