Optimal Real-Time Bidding Strategies
Joaquin Fernandez-Tapia, Olivier Gu\'eant, Jean-Michel Lasry

TL;DR
This paper develops a stochastic optimal control model for real-time bidding in online advertising, providing a mathematical framework to optimize bidding strategies for maximizing KPIs under budget constraints.
Contribution
It introduces a novel stochastic control approach using Hamilton-Jacobi-Bellman equations for RTB, with near-closed form solutions and numerical validation.
Findings
Optimal bidding strategies characterized by HJB equations
Near-closed form solutions via fluid limit approximation
Numerical examples demonstrate practical effectiveness
Abstract
The ad-trading desks of media-buying agencies are increasingly relying on complex algorithms for purchasing advertising inventory. In particular, Real-Time Bidding (RTB) algorithms respond to many auctions -- usually Vickrey auctions -- throughout the day for buying ad-inventory with the aim of maximizing one or several key performance indicators (KPI). The optimization problems faced by companies building bidding strategies are new and interesting for the community of applied mathematicians. In this article, we introduce a stochastic optimal control model that addresses the question of the optimal bidding strategy in various realistic contexts: the maximization of the inventory bought with a given amount of cash in the framework of audience strategies, the maximization of the number of conversions/acquisitions with a given amount of cash, etc. In our model, the sequence of auctions is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsConsumer Market Behavior and Pricing · Auction Theory and Applications · Supply Chain and Inventory Management
