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International Conference on Passive and Active Network Measurement

PAM 2021: Passive and Active Measurement pp 564–580Cite as

A Study of the Partnership Between Advertisers and Publishers

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12671))

Abstract

Ad networks (e.g., Google Ads and Facebook Ads), advertisers, publishers (websites and mobile apps), and users are the main participants in the online advertising ecosystem. Ad networks dominate the advertising landscape in terms of determining how to pair advertisers with publishers and what ads are shown to a user. Previous works have studied the issues surrounding how ad networks tailor ads to a user (i.e., the ad targeting mechanisms) extensively and mainly from the perspective of users. However, it is largely unknown regarding the practices of how ad networks match between advertisers and publishers.

In this paper, we present a measurement study of the practices of how ad networks pair advertisers with publishers as well as advertisers’ preference on ad networks from the perspective of advertisers. To do this, we manage to harvest a unique advertising-related dataset from a leading digital market intelligence platform. We conducted paired comparison analysis, i.e., analyzing advertisers and publishers in pairs, to examine whether they are significantly similar or dissimilar to each other. We also investigate if advertisers in different categories have different preferences on ad networks, whether an advertiser partners with only one ad network for its ad campaign, and how much traffic that its ad campaign could bring about to its site. Specifically, we found that about a third of advertisers have their ads mostly displayed on publishers with the same category as themselves. In addition, most advertisers partner with multiple ad networks at the same time for their ad campaigns. We also found that the Adult, Romance & Relationships, and Gambling websites rely on advertising to attract visitors more than other advertiser categories. Our study produces insightful findings which provide advertisers more visibility into the complex advertising ecosystem so that they could make better decisions when launching ad campaigns.

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Notes

  1. 1.

    We cannot utilize the browser automation tool Selenium to crawl SimilarWeb pages due to the latter’s web scraping prevention measures.

  2. 2.

    https://www.jianguoyun.com/p/Da1yvg4QuKftCBjMpNsD.

  3. 3.

    Online traffic intelligence co SimilarWeb eyes Nasdaq: https://bit.ly/3iAegxl.

  4. 4.

    Several hundreds of domains in our original domain list were removed, since SimilarWeb did not manage to obtain their corresponding publisher information. Actually, it is common and normal that SimilarWeb may not return complete data for each data field we described before.

  5. 5.

    As explained before, the category information is provided by SimilarWeb.

  6. 6.

    Domain name registration’s statistics: https://domainnamestat.com/statistics/overview.

  7. 7.

    Note that 17.5% is already a very large ratio, given the quite small ratio of Gambling publishers among all publishers on the Internet. In our dataset, only 1.4% domains fall into the Gambling category.

  8. 8.

    In here, Fig. 8 is not used to illustrate numerical proportion. We just borrow the form of pie chart for better illustration.

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Acknowledgment

We would like to thank our shepherd Patricia Callejo and anonymous reviewers for their insightful and detailed comments. The co-author Haitao Xu is the contact author of this paper.

Author information

Authors and Affiliations

  1. Shantou University, Shantou, China

    Wenrui Ma

  2. Zhejiang University, Hangzhou, China

    Haitao Xu

Authors
  1. Wenrui Ma
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  2. Haitao Xu
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Corresponding author

Correspondence to Haitao Xu .

Editor information

Editors and Affiliations

  1. Brandenburg University of Technology (BTU), Cottbus, Germany

    Oliver Hohlfeld

  2. Telefonica Research, Barcelona, Spain

    Andra Lutu

  3. Iribe Center, University of Maryland, College Park, MD, USA

    Dave Levin

6 Appendix

6 Appendix

Fig. 9.
figure 9

Distribution of 26,456 domains by category.

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Fig. 10.
figure 10

Distribution of 26,456 domains by country.

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Figure 9 depicts the distribution of the domains in the dataset by category. Figure 10 shows the top 20 origin countries of the domains. Figure 11 shows the average proportion of the incoming traffic that arises from advertising by category.

Fig. 11.
figure 11

The average proportion of the incoming traffic that arises from advertising, for advertisers under each category.

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Ma, W., Xu, H. (2021). A Study of the Partnership Between Advertisers and Publishers. In: Hohlfeld, O., Lutu, A., Levin, D. (eds) Passive and Active Measurement. PAM 2021. Lecture Notes in Computer Science(), vol 12671. Springer, Cham. https://doi.org/10.1007/978-3-030-72582-2_33

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  • DOI: https://doi.org/10.1007/978-3-030-72582-2_33

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  • Online ISBN: 978-3-030-72582-2

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