HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Auction Mechanisms with Organic Traffic

2025-10-16

AI Summary

E-commerce advertising platforms are transitioning from uniform second-price auctions (SPAs) to hybrid SPA/first-price auction (FPA) mechanisms, while advertisers increasingly adopt cross-channel (including organic traffic) full-funnel automated bidding to optimize objectives such as MaxReturn or TargetROAS. This heterogeneity introduces fundamental challenges in incentive compatibility and cross-mechanism bid optimization. Method: We propose the Marginal Cost Alignment (MCA) framework—the first theoretically optimal bidding strategy for heterogeneous auction environments—unifying SPA/FPA payment rules and modeling organic traffic’s synergistic effects, while integrating multi-objective optimization with efficient numerical solvers. Contribution/Results: Offline experiments and large-scale online A/B tests demonstrate that MCA significantly improves total sales (+3.2%) and campaign efficiency (ROI +4.7%), outperforming state-of-the-art baselines. Crucially, it preserves incentive compatibility while overcoming the long-standing bottleneck in cross-mechanism bidding optimization.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The E-commerce advertising platforms typically sell commercial traffic through either second-price auction (SPA) or first-price auction (FPA). SPA was historically prevalent due to its dominant strategy incentive-compatible (DSIC) for bidders with quasi-linear utilities, especially when budgets are not a binding constraint, while FPA has gained more prominence for offering higher revenue potential to publishers and avoiding the possibility for discriminatory treatment in personalized reserve prices. Meanwhile, on the demand side, advertisers are increasingly adopting platform-wide marketing solutions akin to QuanZhanTui, shifting from spending budgets solely on commercial traffic to bidding on the entire traffic for the purpose of maximizing overall sales. For automated bidding systems, such a trend poses a critical challenge: determining optimal strategies across heterogeneous auction channels to fulfill diverse advertiser objectives, such as maximizing return (MaxReturn) or meeting target return on ad spend (TargetROAS). To overcome this challenge, this work makes two key contributions. First, we derive an efficient solution for optimal bidding under FPA channels, which takes into account the presence of organic traffic - traffic can be won for free. Second, we introduce a marginal cost alignment (MCA) strategy that provably secures bidding efficiency across heterogeneous auction mechanisms. To validate performance of our developed framework, we conduct comprehensive offline experiments on public datasets and large-scale online A/B testing, which demonstrate consistent improvements over existing methods.

Problem

• Optimizing bidding across heterogeneous auction mechanisms like FPA and SPA
• Maximizing advertiser objectives such as MaxReturn and TargetROAS efficiently
• Developing unified bidding strategy accounting for organic traffic presence

Innovation

• Efficient bidding solution for first-price auctions
• Marginal cost alignment across auction mechanisms
• Incorporates organic traffic in optimization strategy