Real-Time Budget Allocation Algorithms for AdTech Campaigns: Challenges and Solutions

Authors: Pradeep Bhosale

DOI: https://doi.org/10.5281/zenodo.14615456

Short DOI: https://doi.org/g8x3pk

Country: USA

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Abstract: As real-time bidding (RTB) and programmatic platforms continue to transform digital advertising, budget allocation in AdTech has emerged as a multi-faceted challenge. Advertisers must ensure that limited campaign budgets are spent optimally across diverse channels, user segments, and inventory sources, all within strict time constraints. Traditional static or heuristic-driven approaches can fail to adapt quickly to fluctuating auction dynamics and performance metrics. This paper delves into real-time budget allocation algorithms for AdTech campaigns, focusing on how they handle both the technical complexities of large-scale, millisecond-latency decisions and the business logic of maximizing return on ad spend (ROAS) or meeting other performance objectives.
We begin by introducing the AdTech ecosystem highlighting supply- and demand-side platforms, data management layers, and real-time bidding flows. We then discuss common budget allocation models, from rule-based pacing to multi-armed bandits and reinforcement learning frameworks. Along the way, we illustrate challenges including data latency, partial observability, and pricing volatility and explore potential solutions, such as robust feedback loops, incremental learning, and advanced pacing controls. The paper includes architecture diagrams, algorithmic pseudocode, performance benchmarks, and real-world case studies, culminating in guidelines for adopting or refining real-time budget optimization systems in production AdTech settings.

Keywords: AdTech, Budget Allocation, Real-Time Bidding (RTB), Programmatic Advertising, Multi-Armed Bandits, Reinforcement Learning, Pacing Algorithms, Scalability, Performance, ROAS

Paper Id: 231998

Published On: 2021-01-06

Published In: Volume 9, Issue 1, January-February 2021

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