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.

翻译：品牌广告在建立长期消费者认知度和忠诚度方面发挥着关键作用，使其成为数字平台广告主的核心目标。尽管实时竞价已得到广泛研究，但专门针对品牌竞价广告特性进行优化的算法文献仍然有限。本文提出一种专为品牌广告活动设计的轻量级模型预测控制框架，该框架利用品牌广告的固有属性——如稳定的用户参与模式和快速反馈循环——以简化建模并提升效率。我们的方法采用在线保序回归技术，直接从流式数据构建单调的“出价-花费”与“出价-转化”模型，无需依赖复杂的机器学习模型。该算法完全在线运行且计算开销低，具备高度的实际部署可行性。仿真实验表明，相较于基线策略，本方法能显著提升花费效率与成本控制能力，为现代品牌广告平台提供了可扩展且易于实施的解决方案。