TY - GEN
T1 - Untangling Header Bidding Lore
T2 - 21st International Conference on Passive and Active Measurement, PAM 2020
AU - Aqeel, Waqar
AU - Bhattacherjee, Debopam
AU - Chandrasekaran, Balakrishnan
AU - Godfrey, P. Brighten
AU - Laughlin, Gregory
AU - Maggs, Bruce
AU - Singla, Ankit
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Header bidding (HB) is a relatively new online advertising technology that allows a content publisher to conduct a client-side (i.e., from within the end-user’s browser), real-time auction for selling ad slots on a web page. We developed a new browser extension for Chrome and Firefox to observe this in-browser auction process from the user’s perspective. We use real end-user measurements from 393,400 HB auctions to (a) quantify the ad revenue from HB auctions, (b) estimate latency overheads when integrating with ad exchanges and discuss their implications for ad revenue, and (c) break down the time spent in soliciting bids from ad exchanges into various factors and highlight areas for improvement. For the users in our study, we find that HB increases ad revenue for web sites by 28% compared to that in real-time bidding as reported in a prior work. We also find that the latency overheads in HB can be easily reduced or eliminated and outline a few solutions, and pitch the HB platform as an opportunity for privacy-preserving advertising.
AB - Header bidding (HB) is a relatively new online advertising technology that allows a content publisher to conduct a client-side (i.e., from within the end-user’s browser), real-time auction for selling ad slots on a web page. We developed a new browser extension for Chrome and Firefox to observe this in-browser auction process from the user’s perspective. We use real end-user measurements from 393,400 HB auctions to (a) quantify the ad revenue from HB auctions, (b) estimate latency overheads when integrating with ad exchanges and discuss their implications for ad revenue, and (c) break down the time spent in soliciting bids from ad exchanges into various factors and highlight areas for improvement. For the users in our study, we find that HB increases ad revenue for web sites by 28% compared to that in real-time bidding as reported in a prior work. We also find that the latency overheads in HB can be easily reduced or eliminated and outline a few solutions, and pitch the HB platform as an opportunity for privacy-preserving advertising.
UR - http://www.scopus.com/inward/record.url?scp=85082988795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082988795&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44081-7_17
DO - 10.1007/978-3-030-44081-7_17
M3 - Conference contribution
AN - SCOPUS:85082988795
SN - 9783030440800
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 280
EP - 297
BT - Passive and Active Measurement - 21st International Conference, PAM 2020, Proceedings
A2 - Sperotto, Anna
A2 - Dainotti, Alberto
A2 - Stiller, Burkhard
PB - Springer
Y2 - 30 March 2020 through 31 March 2020
ER -