IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v11y2023i3d10.1057_s41270-023-00237-3.html
   My bibliography  Save this article

Sponsored brands video rings up clicks and sales in the short and long run

Author

Listed:
  • Koen Pauwels

    (Amazon Ads)

  • Michael Peran

    (Amazon Ads)

  • Zee Shah

    (Amazon Ads)

  • German Schnaidt

    (Amazon Ads)

  • Dauwe Vercamer

    (Amazon Ads)

Abstract

Video ads are increasingly popular in digital marketing, but advertisers are unsure about how much they improve performance over static ads and which consumer response, such as unmuting or watching through the end, matters most. Using data from the online retail site Amazon.com, we apply causal inference methods to both a monthlong and yearlong time horizon and find support for our hypotheses. First, brands that invested in Sponsored Brands video (SBv) ads in addition to sponsored ads static ads had a 25% higher click-through rate (CTR) and 10% higher year-over-year sales growth. Second, individual consumer CTR depends on ad format (video vs. static), unmuting, and time watched. For audiences in 15 countries across North America, Europe, the Middle East, Asia, and Australia, we find a 17.7 times higher CTR on SBv versus static images, especially for unmuted versus muted SBv. Furthermore, the muted consumer CTR increases with the viewed video length, with a substantial increase at a viewed video length longer than 5 s. Surprisingly, the unmuted CTR remains over 3 times that of muted CTR at all viewed video lengths, showing only a CTR uptick when the video was completed. Thus, if the ad is not watched with sound for its full length (the best-case scenario), advertisers should strive for video ads that (1) are unmuted, even for a short time, or (2) play at least 5 s on mute.

Suggested Citation

  • Koen Pauwels & Michael Peran & Zee Shah & German Schnaidt & Dauwe Vercamer, 2023. "Sponsored brands video rings up clicks and sales in the short and long run," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 275-286, September.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:3:d:10.1057_s41270-023-00237-3
    DOI: 10.1057/s41270-023-00237-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-023-00237-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41270-023-00237-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hannes Datta & George Knox & Bart J. Bronnenberg, 2018. "Changing Their Tune: How Consumers’ Adoption of Online Streaming Affects Music Consumption and Discovery," Marketing Science, INFORMS, vol. 37(1), pages 5-21, January.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
    5. Richard L. Daft & Robert H. Lengel, 1986. "Organizational Information Requirements, Media Richness and Structural Design," Management Science, INFORMS, vol. 32(5), pages 554-571, May.
    6. Matthew McGranaghan & Jura Liaukonyte & Kenneth C. Wilbur, 2022. "How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift," Marketing Science, INFORMS, vol. 41(5), pages 873-895, September.
    7. Shehu, Edlira & Bijmolt, Tammo H.A. & Clement, Michel, 2016. "Effects of Likeability Dynamics on Consumers' Intention to Share Online Video Advertisements," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 27-43.
    8. Belanche, Daniel & Flavián, Carlos & Pérez-Rueda, Alfredo, 2017. "Understanding Interactive Online Advertising: Congruence and Product Involvement in Highly and Lowly Arousing, Skippable Video Ads," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 75-88.
    9. Marc Vanhuele & Shuba Srinivasan & Koen Pauwels, 2010. "Mindset Metrics in Market Response Models: An Integrative Approach," Post-Print hal-00528411, HAL.
    10. Pauwels, Koen & Erguncu, Selin & Yildirim, Gokhan, 2013. "Winning hearts, minds and sales: How marketing communication enters the purchase process in emerging and mature markets," International Journal of Research in Marketing, Elsevier, vol. 30(1), pages 57-68.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vivian Qin & Koen Pauwels & Bobby Zhou, 2024. "Data-driven budget allocation of retail media by ad product, funnel metric, and brand size," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 235-249, June.

    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. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Sridhar, Shrihari & Naik, Prasad A. & Kelkar, Ajay, 2017. "Metrics unreliability and marketing overspending," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 761-779.
    5. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    6. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    7. Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
    8. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    9. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    10. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    11. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    12. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    14. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    15. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    16. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    17. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    18. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    19. Ariadna García-Prado & Paula González & Yolanda F. Rebollo-Sanz, 2024. "Confinement policies: controlling contagion without compromising mental health," Working Papers 24.03, Universidad Pablo de Olavide, Department of Economics.
    20. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org.

    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:pal:jmarka:v:11:y:2023:i:3:d:10.1057_s41270-023-00237-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.