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A Lightweight MPC Bidding Framework for Brand Auction Ads

Yuanlong Chen, Bowen Zhu, Bing Xia, Yichuan Wang

TL;DR

This paper proposes a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads to simplify modeling and improve efficiency.

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.

A Lightweight MPC Bidding Framework for Brand Auction Ads

TL;DR

This paper proposes a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads to simplify modeling and improve efficiency.

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.
Paper Structure (16 sections, 16 equations, 2 figures, 1 table, 3 algorithms)

This paper contains 16 sections, 16 equations, 2 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Lightweight MPC Framework
  4. Problem Formulation
  5. MPC Framework
  6. Lightweight Bid-to-$X$ Modeling
  7. Extension
  8. Evaluation
  9. Offline Simulations
  10. Benchmarks
  11. Experiment Set-up
  12. Result Analysis
  13. Large Scale Online A/B Test
  14. Remarks
  15. Conclusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Sampled path of bid of the tested algorithms. Bid is rescaled to [0, 1]
  • Figure 2: CPV versus initial bid value for each algorithm. MPC is highly robust to initialization, while PID and DOGD suffer as the cold-start bid deviates from the true optimum.