arXiv:2510.15238v2 Announce Type: replace-cross Abstract: Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms vary across channels in ranking rules (pure eCPM vs. UE-augmented scoring), pricing formats (first- vs. second-price), and bidding conventions (uniform vs. non-uniform), while advertisers impose shared campaign-level constraints. We propose HOB, which makes marginal cost (MC) computable and alignable across heterogeneous channels, especially for first-price auctions (FPA) with organic-paid coexisten
The increasing complexity of digital advertising and the drive for efficiency in large-scale campaigns necessitate more sophisticated, AI-driven bidding strategies that can adapt to diverse auction environments.
This development allows advertisers to optimize their ad spend more effectively across various platforms, leading to better returns on investment and potentially reshaping the competitive landscape of digital advertising.
Digital advertising campaigns can now leverage a holistically optimized, AI-driven bidding strategy that accounts for heterogeneous auction mechanisms, improving efficiency and performance across channels.
- · Advertisers with complex campaigns
- · Ad-tech companies offering optimization tools
- · AI/ML researchers in ad optimization
- · Advertising platforms with less sophisticated bidding APIs
- · Agencies relying on manual optimization
- · Advertisers with limited data science capabilities
Increased efficiency and ROI for large-scale digital advertising campaigns.
Greater competition among advertising platforms to support such advanced bidding strategies, potentially leading to standardization or new API developments.
Further consolidation of ad spend towards platforms that offer granular control and data, potentially disadvantaging smaller or less transparent ad networks.
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Read at arXiv cs.LG