No More Chasing Waterfalls: A Measurement Study of the Header Bidding Ad-Ecosystem

TL;DR

This study introduces HBDetector, a real-time detection method for header bidding, revealing its adoption rate, major players, and network overhead through analysis of 35,000 top websites.

Contribution

The paper presents HBDetector, a novel methodology for detecting header bidding auctions and provides the first large-scale analysis of its implementation and performance in the wild.

Findings

14.28% of top websites use header bidding

Publishers tend to work with a few dominant demand partners

Header bidding latency can be up to 3 times higher than waterfall

Abstract

In recent years, Header Bidding (HB) has gained popularity among web publishers, challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, HB aims to give back to publishers control of their ad inventory, increase transparency, fairness and competition among advertisers, resulting in higher ad-slot prices. Although promising, little is known about how this ad protocol works: What are HB's possible implementations, who are the major players, and what is its network and UX overhead? To address these questions, we design and implement HBDetector: a novel methodology to detect HB auctions on a website at real time. By crawling 35,000 top Alexa websites, we collect and analyze a dataset of 800k auctions. We find that: (i) 14.28% of top websites utilize HB. (ii) Publishers prefer to collaborate with a few Demand Partners who also dominate the waterfall market. (iii) HB latency can be significantly higher (up to 3x in median case) than waterfall.