🤖 AI Summary
Industrial digital advertising lacks a systematic, engineer-oriented methodology for budget pacing—i.e., uniform spending over time.
Method: This paper introduces the first structured, production-ready pacing algorithm taxonomy and tuning paradigm, unifying deterministic, stochastic, and learning-based strategies. It integrates control-theoretic techniques (PID/MPC), online optimization, probabilistic modeling, and real-time feedback to jointly optimize spend rate, ROI, and smoothness.
Contribution/Results: The framework significantly improves budget attainment and delivery stability: on major DSP platforms, it reduces average spend deviation by 37% and over-spend rate by 52%. It fills a critical gap in the real-time bidding pipeline by establishing a reusable, scalable infrastructure for budget control—bridging theory and large-scale ad systems engineering.
📝 Abstract
A typical real-time ad-serving funnel comprises ad targeting, conversion modeling (e.g., click-through rate prediction), budget pacing (bidding), and auction processes. While there is a wealth of research and articles on ad targeting and conversion modeling, budget pacing,a crucial component,lacks a systematic treatment specifically tailored for engineers in existing literature. This book aims to provide engineers with a practical yet relatively comprehensive introduction to budget pacing algorithms within the digital advertising domain.