Bidding With Budgets: Data-Driven Auto-Bidding Algorithms in Digital Advertising

A groundbreaking study conducted by Yale University researchers has revealed new insights into the world of digital advertising, shedding light on the intricacies of data-driven auto-bidding algorithms. The study, which has been peer-reviewed, focuses on the allocation of sponsored search, product, and display advertisements in digital auctions. The research, funded by the National Science Foundation and the Office of Naval Research, provides a comprehensive analysis of the equilibrium properties of increasingly sophisticated auto-bidding algorithms. The findings have significant implications for the digital advertising industry, highlighting the importance of maximizing platform revenue while ensuring advertiser participation.

Key Takeaways:

• The study reveals that auto-bidding algorithms in digital advertising are largely driven by platform-provided data, with a significant impact on the allocation of sponsored advertisements.
• The researchers analyzed the equilibrium bidding behavior of individual advertisers controlling auto-bidding algorithms through budget choices, as well as the interaction when all bidders use budget-controlled bidding algorithms.
• The study concludes that a specific bidding algorithm maximizes platform revenue while ensuring all advertisers continue to participate.
• The research was funded by the National Science Foundation (NSF) and the Office of Naval Research, with peer review conducted by the International Journal of Industrial Organization (Elsevier).
• Additional authors of the study include Nicholas Wu and Alessandro Bonatti, in collaboration with Dirk Bergemann from Yale University.
• The study highlights the importance of understanding the equilibrium properties of auto-bidding algorithms in digital advertising, with implications for maximizing platform revenue and ensuring advertiser participation.

Statistics:

• The study focused on the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms.
• The research analyzed the interaction of all bidders using budget-controlled bidding algorithms.
• The study concluded that a specific bidding algorithm maximizes platform revenue while ensuring all advertisers continue to participate.

Sources:

• NewsRx. Findings from Yale University Has Provided New Data on Information Technology (Bidding With Budgets: Data-driven Bid Algorithms In Digital Advertising). Marketing Weekly News. November 1, 2025; p 56.
• International Journal of Industrial Organization. Bidding With Budgets: Data-driven Bid Algorithms In Digital Advertising. 2025; 102.