A Lightweight MPC Bidding Framework for Brand Auction Ads
This work addresses the problem of optimizing brand auction ads for advertisers by providing a scalable and easily implementable solution, which is an incremental improvement over existing real-time bidding literature.
This paper proposes a lightweight Model Predictive Control (MPC) framework for brand advertising campaigns, which leverages stable user engagement and fast feedback loops to simplify modeling. The framework uses online isotonic regression to build bid-to-spend and bid-to-conversion models from streaming data, resulting in significant improvements in spend efficiency and cost control compared to baseline strategies.
Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real-time bidding has been extensively studied, there is limited literature on algorithms specifically tailored for brand auction ads that fully leverage their unique characteristics. In this paper, we propose a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads -- such as stable user engagement patterns and fast feedback loops -- to simplify modeling and improve efficiency. Our approach utilizes online isotonic regression to construct monotonic bid-to-spend and bid-to-conversion models directly from streaming data, eliminating the need for complex machine learning models. The algorithm operates fully online with low computational overhead, making it highly practical for real-world deployment. Simulation results demonstrate that our approach significantly improves spend efficiency and cost control compared to baseline strategies, providing a scalable and easily implementable solution for modern brand advertising platforms.