Keyword-Level Bayesian Online Bid Optimization for Sponsored Search Advertising

Kaito Majima (), Kosuke Kawakami (), Kota Ishizuka () and Kazuhide Nakata ()
Additional contact information
Kaito Majima: Tokyo Institute of Technology
Kosuke Kawakami: Tokyo Institute of Technology
Kota Ishizuka: negocia, Inc.
Kazuhide Nakata: Tokyo Institute of Technology

SN Operations Research Forum, 2024, vol. 5, issue 2, 1-32

Abstract: Abstract Bid price optimization in online advertising is a challenging task due to its high uncertainty. In this paper, we propose a bid price optimization algorithm focused on keyword-level bidding for pay-per-click sponsored search ads, which is a realistic setting for many firms. There are three characteristics of this setting: “The setting targets the optimization of bids for each keyword in pay-per-click sponsored search advertising”, “The only information available to advertisers is the number of impressions, clicks, conversions, and advertising cost for each keyword”, and “Advertisers bid daily and set monthly budgets on a campaign basis”. Our algorithm first predicts the performance of keywords as a distribution by modeling the relationship between ad metrics through a Bayesian network and performing Bayesian inference. Then, it outputs the bid price by means of a bandit algorithm and online optimization. This approach enables online optimization that considers uncertainty from the limited information available to advertisers. We conducted simulations using real data and confirmed the effectiveness of the proposed method for both open-source data and data provided by negocia, Inc., which provides an automated Internet advertising management system.

Keywords: Online advertising; Bid price optimization; Bayesian inference; Bandit algorithm; Online optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-024-00322-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00322-y

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-024-00322-y

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().