Real-time Bidding for Online Advertising: Measurement and Analysis

TL;DR

This paper empirically analyzes real-time bidding in online advertising, revealing periodic patterns, the prevalence of first-price payments despite second-price auction claims, and highlighting the need for optimized bidding strategies considering temporal factors.

Contribution

It provides first-hand empirical insights into RTB systems, uncovering payment behaviors and temporal patterns, and identifies key areas for optimization in bidding strategies.

Findings

55.4% of costs are paid as first-price despite second-price auction claims

Periodic patterns observed in impressions, clicks, bids, and conversion rates

Current bidding strategies are suboptimal, requiring better algorithms considering temporal dynamics

Abstract

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.