A Lightweight MPC Bidding Framework for Brand Auction Ads

TL;DR

This paper introduces a lightweight, online MPC framework for brand ad bidding that leverages stable engagement patterns and simple models to enhance efficiency and cost control in real-time advertising.

Contribution

It presents a novel, computationally efficient MPC approach using isotonic regression for brand ad bidding, tailored to exploit unique brand ad characteristics.

Findings

Significantly improves spend efficiency over baselines

Operates with low computational overhead

Easily deployable in real-world platforms

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.