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Virality, Network Effects and Advertising Effectiveness

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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 achieving this endogenous reach and the effectiveness of the ad at persuading a consumer to purchase or adopt a favorable attitude towards a product. The analysis combines data on the real-life virality of 400 video ads, and crowd-sourced measurement of advertising effectiveness among 24,000 consumers. We measure effectiveness 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 drops on average by 10\% for every one million views the ad had received. Taking into account the advantages of increased reach, this means that there was a decline in overall advertising effectiveness at 3-4 million views. Importantly, ads that generated both views \emph{and} online engagement in the form of comments did not suffer from the same negative relationship. We show that such ads retained their efficacy because they achieved virality due to humor or visual appeal rather than because they were provocative or outrageous.

Suggested Citation

  • Catherine Tucker, 2011. "Virality, Network Effects and Advertising Effectiveness," Working Papers 11-06, NET Institute.
  • Handle: RePEc:net:wpaper:1106
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    References listed on IDEAS

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    1. 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.
    2. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    3. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    4. Hauser, John R & Wernerfelt, Birger, 1990. " An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Oxford University Press, vol. 16(4), pages 393-408, March.
    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. Doraszelski, Ulrich & Draganska, Michaela & Clark, C. Robert, 2007. "Information or Persuasion? An Empirical Investigation of the Effect of Advertising on Brand Awareness and Perceived Quality using Panel Data," Research Papers 1971, Stanford University, Graduate School of Business.
    7. 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.
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    More about this item

    Keywords

    Social Networks; Video; Online Advertising;

    JEL classification:

    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • M38 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Government Policy and Regulation

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