A Lightweight MPC Bidding Framework for Brand Auction Ads

• URL: http://arxiv.org/abs/2603.07721v1
• Date: Sun, 08 Mar 2026 16:45:21 GMT
• Title: A Lightweight MPC Bidding Framework for Brand Auction Ads
• Abstract summary: We propose a lightweight Model Predictive Control framework designed for brand advertising campaigns.<n>Our approach utilizes online isotonic regression to construct monotonic bid-to-spend and bid-to-conversion models.
• Score: 2.4357431333415884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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