# Real-time Bidding for Online Advertising: Measurement and Analysis

arXiv: https://arxiv.org/abs/1306.6542 · HTML: https://mlsanity.com/paper/1306.6542/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2013.

## Built on by (most cited first, 30 of 32)

- [CausalPFN: Amortized Causal Effect Estimation via In-Context Learning](https://mlsanity.com/paper/2506.07918/) (2025)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Agent Exchange: Shaping the Future of AI Agent Economics](https://mlsanity.com/paper/2507.03904/) (2025)
- [ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning](https://mlsanity.com/paper/2206.05240/) (2022)
- [Challenges and approaches to privacy preserving post-click conversion prediction](https://mlsanity.com/paper/2201.12666/) (2022)
- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://mlsanity.com/paper/2310.09426/) (2023)
- [Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbots](https://mlsanity.com/paper/2409.15436/) (2024)
- [Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach](https://mlsanity.com/paper/2111.08840/) (2021)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising](https://mlsanity.com/paper/2110.13587/) (2021)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)](https://mlsanity.com/paper/2412.11434/) (2024)
- [A Real-World Implementation of Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2202.13868/) (2022)
- [Impression Allocation and Policy Search in Display Advertising](https://mlsanity.com/paper/2203.07073/) (2022)
- [Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape](https://mlsanity.com/paper/2302.05762/) (2023)
- [BAT: Benchmark for Auto-bidding Task](https://mlsanity.com/paper/2505.08485/) (2025)
- [Expert-Guided Diffusion Planner for Auto-Bidding](https://mlsanity.com/paper/2508.08687/) (2025)
- [Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising](https://mlsanity.com/paper/2212.12533/) (2022)
- [Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF](https://mlsanity.com/paper/2309.08621/) (2023)
- [Advertising in AI systems: Society must be vigilant](https://mlsanity.com/paper/2505.18425/) (2025)
- [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026)
- [Error-Aware Reverse Auction Mechanism for Large Language Model Routing](https://mlsanity.com/paper/2608.12719/) (2026)
- [A Unified Framework for Campaign Performance Forecasting in Online Display Advertising](https://mlsanity.com/paper/2202.11877/) (2022)
- [Class-aware and Augmentation-free Contrastive Learning from Label Proportion](https://mlsanity.com/paper/2408.06743/) (2024)
- [Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding](https://mlsanity.com/paper/2412.19064/) (2024)
- [HALO: Hindsight-Augmented Learning for Online Auto-Bidding](https://mlsanity.com/paper/2508.03267/) (2025)
- [KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits](https://mlsanity.com/paper/2601.05257/) (2025)
- [Generative Auto-Bidding with Unified Modeling and Exploration](https://mlsanity.com/paper/2605.19457/) (2026)
- [Constrained Auto-Bidding via Generative Response Modeling](https://mlsanity.com/paper/2605.27811/) (2026)

## Abstract (arXiv)

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.