research-article

How much can behavioral targeting help online advertising?

Authors:

Zheng ChenAuthors Info & Claims

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

Pages 261 - 270

https://doi.org/10.1145/1526709.1526745

Published: 20 April 2009 Publication History

Abstract

Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.

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Cited By

Hu LSun XYu HChung S(2024)Seize the Opportunity of Targeted Marketing Under the Platform Membership MechanismIEEE Transactions on Engineering Management10.1109/TEM.2022.316939271(1969-1983)Online publication date: 2024
https://doi.org/10.1109/TEM.2022.3169392
Mocanu BTapu R(2024)SemanticAd: A Multimodal Contextual Advertisement Framework for Online Video Streaming PlatformsIEEE Access10.1109/ACCESS.2024.339592212(63142-63155)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395922
Miller KSkiera B(2024)Economic consequences of online tracking restrictions: Evidence from cookiesInternational Journal of Research in Marketing10.1016/j.ijresmar.2023.10.00141:2(241-264)Online publication date: Jun-2024
https://doi.org/10.1016/j.ijresmar.2023.10.001
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Index Terms

How much can behavioral targeting help online advertising?
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Published In

cover image ACM Conferences

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

1280 pages

ISBN:9781605584874

DOI:10.1145/1526709

General Chairs:
Juan Quemada
DIT-UPM
,
Gonzalo León
DIT-UPM
,
Program Chairs:
Yoelle Maarek
Google Inc., Israel
,
Wolfgang Nejdl
L3S and Hannover University

Copyright © 2009 IW3C2 org.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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WWW '09

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WWW '09: The 18th International World Wide Web Conference

April 20 - 24, 2009

Madrid, Spain

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Hu LSun XYu HChung S(2024)Seize the Opportunity of Targeted Marketing Under the Platform Membership MechanismIEEE Transactions on Engineering Management10.1109/TEM.2022.316939271(1969-1983)Online publication date: 2024
https://doi.org/10.1109/TEM.2022.3169392
Mocanu BTapu R(2024)SemanticAd: A Multimodal Contextual Advertisement Framework for Online Video Streaming PlatformsIEEE Access10.1109/ACCESS.2024.339592212(63142-63155)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395922
Miller KSkiera B(2024)Economic consequences of online tracking restrictions: Evidence from cookiesInternational Journal of Research in Marketing10.1016/j.ijresmar.2023.10.00141:2(241-264)Online publication date: Jun-2024
https://doi.org/10.1016/j.ijresmar.2023.10.001
Ahmadi IAbou Nabout NSkiera BMaleki EFladenhofer J(2024)Overwhelming targeting options: Selecting audience segments for online advertisingInternational Journal of Research in Marketing10.1016/j.ijresmar.2023.08.00441:1(24-40)Online publication date: Mar-2024
https://doi.org/10.1016/j.ijresmar.2023.08.004
Zhao Y(2023)Research on the Influence of Social Media Platforms on Consumers’ Purchase IntentionBCP Business & Management10.54691/bcpbm.v37i.353937(13-18)Online publication date: 1-Feb-2023
https://doi.org/10.54691/bcpbm.v37i.3539
Maria M(2023)The Improvement of Retargeting by Big Data: A Decision Support that Threatens the Brand Image?European Journal of Marketing and Economics10.26417/511ybh24h4:1(31-44)Online publication date: 7-Mar-2023
https://doi.org/10.26417/511ybh24h
Wang PJiang LYang J(2023)The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad PublisherJournal of Marketing Research10.1177/0022243723117184861:1(70-91)Online publication date: 23-Jun-2023
https://doi.org/10.1177/00222437231171848
Sharma TKaushik SYu YAhmed SWang Y(2023)User Perceptions and Experiences of Targeted Ads on Social Media Platforms: Learning from Bangladesh and IndiaProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581498(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581498
Li Y(2023)Influence of different types of advertising on the advertising conversion rate in college studentsSHS Web of Conferences10.1051/shsconf/202317802002178(02002)Online publication date: 23-Oct-2023
https://doi.org/10.1051/shsconf/202317802002
Dini Kounoudes AKapitsaki GKatakis I(2023)Enhancing user awareness on inferences obtained from fitness trackers dataUser Modeling and User-Adapted Interaction10.1007/s11257-022-09353-833:4(967-1014)Online publication date: 17-Jan-2023
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