arXiv:2606.10187v1 Announce Type: cross Abstract: We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental response, and member-experience load. Instead of calibrating a generic forecast residual, the framework measures forecast error by its largest impact on the policies that could actually be deployed. The main theorem shows that the proposed score is the smallest valid uncertainty measure that uniformly protects all deployable pacing policies. Geometrically, it is the suppor
The increasing complexity and scale of online advertising, particularly streaming advertising, necessitate more sophisticated AI-driven decision-making frameworks to optimize campaign performance.
This development improves the reliability and efficiency of AI in managing critical business functions like advertising budgets, potentially leading to better ROI for advertisers and publishers.
The method for evaluating forecast error in advertising pacing shifted from generic residuals to impact on deployable policies, offering a more robust and decision-calibrated approach.
- · Online advertisers
- · Adtech companies
- · Platforms with streaming content
- · AI/ML researchers in ad optimization
- · Competitors using less advanced pacing models
- · Ad campaigns with high uncertainty tolerance
Improved financial efficiency and predictability in digital advertising campaigns due to more accurate pacing.
Increased adoption of AI-driven, uncertainty-aware systems across other complex decision-making domains beyond advertising.
Enhanced automation in marketing and sales operations leading to leaner teams and greater reliance on algorithmic management.
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Read at arXiv cs.LG