Reserve price failure rate prediction with header bidding in display advertising

Achir Kalra, Cristian Borcea, Chong Wang, Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The revenue of online display advertising in the U.S. is projected to be 7.9 billion U.S. dollars by 2022. One main way of display advertising is through real-time bidding (RTB). In RTB, an ad exchange runs a second price auction among multiple advertisers to sell each ad impression. Publishers usually set up a reserve price, the lowest price acceptable for an ad impression. If there are bids higher than the reserve price, then the revenue is the higher price between the reserve price and the second highest bid; otherwise, the revenue is zero. Thus, a higher reserve price can potentially increase the revenue, but with higher risks associated. In this paper, we study the problem of estimating the failure rate of a reserve price, i.e., the probability that a reserve price fails to be outbid. The solution to this problem have managerial implications to publishers to set appropriate reserve prices in order to minimizes the risks and optimize the expected revenue. This problem is highly challenging since most publishers do not know the historical highest bidding prices offered by RTB advertisers. To address this problem, we develop a parametric survival model for reserve price failure rate prediction. The model is further improved by considering user and page interactions, and header bidding information. The experimental results demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2819-2827
Number of pages9
ISBN (Electronic)9781450362016
DOIs
StatePublished - Jul 25 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
CountryUnited States
CityAnchorage
Period8/4/198/8/19

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Computational Advertising
  • Header Bidding
  • Survival Analysis

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