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The Reach and Persuasiveness of Viral Video Ads

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  • Catherine E. Tucker

    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02142; and National Bureau of Economic Research, Cambridge, Massachusetts, 02138)

Abstract

Many video ads are designed to go viral so that the total number of views they receive depends on customers sharing the ads with their friends. This paper explores the relationship between the number of views and how persuasive the ad is at convincing consumers to purchase or to adopt a favorable attitude towards the product. The analysis combines data on the total views of 400 video ads, and crowd-sourced measurement of advertising persuasiveness among 24,000 survey responses. Persuasiveness is measured by randomly exposing half of these consumers to a video ad and half to a similar placebo video ad, and then surveying their attitudes towards the focal product. Relative ad persuasiveness is on average 10% lower for every one million views that the video ad achieves. The exceptions to this pattern were ads that generated views and large numbers of comments, and video ads that attracted comments that mentioned the product by name. Evidence suggests that such ads remained effective because they attracted views due to humor rather than because they were outrageous.

Suggested Citation

  • Catherine E. Tucker, 2015. "The Reach and Persuasiveness of Viral Video Ads," Marketing Science, INFORMS, vol. 34(2), pages 281-296, March.
    • Handle: RePEc:inm:ormksc:v:34:y:2015:i:2:p:281-296
    DOI: 10.1287/mksc.2014.0874
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    References listed on IDEAS

    as
    1. Randall Lewis & David Reiley, 2014. "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 235-266, September.
    2. Anindya Ghose & Sang Pil Han, 2011. "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet," Management Science, INFORMS, vol. 57(9), pages 1671-1691, September.
    3. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
    4. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    5. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    6. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    7. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    8. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    9. Amalia R. Miller & Catherine Tucker, 2013. "Active Social Media Management: The Case of Health Care," Information Systems Research, INFORMS, vol. 24(1), pages 52-70, March.
    10. Yuxin Chen & Yogesh V. Joshi & Jagmohan S. Raju & Z. John Zhang, 2009. "A Theory of Combative Advertising," Marketing Science, INFORMS, vol. 28(1), pages 1-19, 01-02.
    11. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    12. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    13. Morwitz, Vicki G. & Steckel, Joel H. & Gupta, Alok, 2007. "When do purchase intentions predict sales?," International Journal of Forecasting, Elsevier, vol. 23(3), pages 347-364.
    14. Goldfarb, Avi & Prince, Jeff, 2008. "Internet adoption and usage patterns are different: Implications for the digital divide," Information Economics and Policy, Elsevier, vol. 20(1), pages 2-15, March.
    15. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    16. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
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