Learning to Price against a Budget and ROI Constrained Buyer

Understand

Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e.

• ad value) while respecting both budget and return-on-investment (ROI) constraints for efficient utilization of limited monetary resources.
• Facing such a constrained buyer who aims to learn her optimal strategy to acquire impressions, we study from a seller's perspective how to learn and price ad impressions through repeated posted price mechanisms to maximize revenue.
• For this two-sided learning setup, we propose a learning algorithm for the seller that utilizes an episodic binary-search procedure to identify a revenue-optimal selling price.