IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2008.12132.html
   My bibliography  Save this paper

How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness

Author

Listed:
  • Christina Uhl
  • Nadia Abou Nabout
  • Klaus Miller

Abstract

A large share of all online display advertisements (ads) are never seen by a human. For instance, an ad could appear below the page fold, where a user never scrolls. Yet, an ad is essentially ineffective if it is not at least somewhat viewable. Ad viewability - which refers to the pixel percentage-in-view and the exposure duration of an online display ad - has recently garnered great interest among digital advertisers and publishers. However, we know very little about the impact of ad viewability on advertising effectiveness. We work to close this gap by analyzing a large-scale observational data set with more than 350,000 ad impressions similar to the data sets that are typically available to digital advertisers and publishers. This analysis reveals that longer exposure durations (>10 seconds) and 100% visible pixels do not appear to be optimal in generating view-throughs. The highest view-through rates seem to be generated with relatively lower pixel/second-combinations of 50%/1, 50%/5, 75%/1, and 75%/5. However, this analysis does not account for user behavior that may be correlated with or even drive ad viewability and may therefore result in endogeneity issues. Consequently, we manipulated ad viewability in a randomized online experiment for a major European news website, finding the highest ad recognition rates among relatively higher pixel/second-combinations of 75%/10, 100%/5 and 100%/10. Everything below 75\% or 5 seconds performs worse. Yet, we find that it may be sufficient to have either a long exposure duration or high pixel percentage-in-view to reach high advertising effectiveness. Our results provide guidance to advertisers enabling them to establish target viewability rates more appropriately and to publishers who wish to differentiate their viewability products.

Suggested Citation

  • Christina Uhl & Nadia Abou Nabout & Klaus Miller, 2020. "How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness," Papers 2008.12132, arXiv.org.
  • Handle: RePEc:arx:papers:2008.12132
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2008.12132
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Navdeep S. Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    2. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    3. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    4. Navdeep Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    5. Alexander Bleier & Maik Eisenbeiss, 2015. "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where," Marketing Science, INFORMS, vol. 34(5), pages 669-688, September.
    6. repec:nas:journl:v:115:y:2018:p:e5254-e5255 is not listed on IDEAS
    7. Navdeep S. Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    8. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    9. Xiang Fang & Surendra Singh & Rohini Ahluwalia, 2007. "An Examination of Different Explanations for the Mere Exposure Effect," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(1), pages 97-103, March.
    10. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
    11. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    12. Rik Pieters & Michel Wedel, 2012. "Ad Gist: Ad Communication in a Single Eye Fixation," Marketing Science, INFORMS, vol. 31(1), pages 59-73, January.
    13. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Min Tian & Paul R. Hoban & Neeraj Arora, 2024. "What Cookie-Based Advertising Effectiveness Fails to Measure," Marketing Science, INFORMS, vol. 43(2), pages 407-418, March.
    2. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.
    3. Johannes Hermle & Giorgio Martini, 2022. "Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split," Papers 2207.00206, arXiv.org.
    4. Kurt P. Munz & Minah H. Jung & Adam L. Alter, 2020. "Name Similarity Encourages Generosity: A Field Experiment in Email Personalization," Marketing Science, INFORMS, vol. 39(6), pages 1071-1091, November.
    5. Garrett A. Johnson & Randall A. Lewis & David H. Reiley, 2017. "When Less Is More: Data and Power in Advertising Experiments," Marketing Science, INFORMS, vol. 36(1), pages 43-53, January.
    6. Thomas W. Frick & Rodrigo Belo & Rahul Telang, 2023. "Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment," Management Science, INFORMS, vol. 69(3), pages 1665-1686, March.
    7. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    8. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    9. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    10. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.
    11. Chen He & Tobias J. Klein, 2023. "Advertising as a Reminder: Evidence from the Dutch State Lottery," Marketing Science, INFORMS, vol. 42(5), pages 892-909, September.
    12. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    13. Stephan Seiler & Song Yao & Wenbo Wang, 2017. "Does Online Word of Mouth Increase Demand? (And How?) Evidence from a Natural Experiment," Marketing Science, INFORMS, vol. 36(6), pages 838-861, November.
    14. Shun-Yang Lee & Julian Runge & Daniel Yoo & Yakov Bart & Anett Gyurak & J. W. Schneider, 2023. "COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?," Papers 2307.09035, arXiv.org, revised Jan 2024.
    15. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    16. Navdeep S. Sahni & Dan Zou & Pradeep K. Chintagunta, 2017. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Management Science, INFORMS, vol. 63(8), pages 2688-2705, August.
    17. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    18. Kirthi Kalyanam & John McAteer & Jonathan Marek & James Hodges & Lifeng Lin, 2018. "Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 1-42, March.
    19. Ruichang Lu & Qiaowei Shen & Tenghui Wang & Xiaojun Zhang, 2022. "Frenemies: Corporate Advertising Under Common Ownership," Management Science, INFORMS, vol. 68(6), pages 4645-4669, June.
    20. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2008.12132. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.