Optimal Real-Time Bidding Strategies

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 modeled by a Poisson process and the \textit{price to beat} for each auction is modeled by a random variable following almost any probability distribution. We show that the optimal bids are characterized by a Hamilton-Jacobi-Bellman equation, and that almost-closed form solutions can be found by using a fluid limit. Numerical examples are also carried out.