IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v17y2019i3d10.1007_s11129-019-09211-9.html
   My bibliography  Save this article

Can your advertising really buy earned impressions? The effect of brand advertising on word of mouth

Author

Listed:
  • Mitchell J. Lovett

    (University of Rochester)

  • Renana Peres

    (Hebrew University of Jerusalem)

  • Linli Xu

    (University of Minnesota)

Abstract

Paid media expenditures could potentially increase earned media exposures such as social media posts and other word-of-mouth (WOM). However, academic research on the effect of advertising on WOM is scarce and shows mixed results. We examine the relationship between monthly Internet and TV advertising expenditures and WOM for 538 U.S. national brands across 16 categories over 6.5 years. We find that the average implied advertising elasticity on total WOM is small: 0.019 for TV, and 0.014 for Internet. On the online WOM (measured volume of brand chatter on blogs, user-forums, and Twitter), we find average monthly effects of 0.008 for TV and 0.01 for Internet advertising. Even the categories that have the strongest implied elasticities are only as large as 0.05. Despite this small average effect, we do find that advertising in certain events may produce more desirable amounts of WOM. Specifically, using a synthetic control approach, we find that being a Super Bowl advertiser causes a moderate increase in total WOM that lasts 1 month. The effect on online WOM is larger, but lasts for only 3 days. We discuss the implications of these findings for managing advertising and WOM.

Suggested Citation

  • Mitchell J. Lovett & Renana Peres & Linli Xu, 2019. "Can your advertising really buy earned impressions? The effect of brand advertising on word of mouth," Quantitative Marketing and Economics (QME), Springer, vol. 17(3), pages 215-255, September.
    • Handle: RePEc:kap:qmktec:v:17:y:2019:i:3:d:10.1007_s11129-019-09211-9
    DOI: 10.1007/s11129-019-09211-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-019-09211-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11129-019-09211-9?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. Ralf van der Lans & Gerrit van Bruggen & Jehoshua Eliashberg & Berend Wierenga, 2010. "A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth," Marketing Science, INFORMS, vol. 29(2), pages 348-365, 03-04.
    2. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    3. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    4. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    5. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    6. Mitchell J. Lovett & Richard Staelin, 2016. "The Role of Paid, Earned, and Owned Media in Building Entertainment Brands: Reminding, Informing, and Enhancing Enjoyment," Marketing Science, INFORMS, vol. 35(1), pages 142-157, January.
    7. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    8. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    9. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    10. 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.
    11. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    12. Holbrook, Morris B & Batra, Rajeev, 1987. "Assessing the Role of Emotions as Mediators of Consumer Responses to Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 404-420, December.
    13. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    14. Hogan, John E. & Lemon, Katherine N. & Libai, Barak, 2004. "Quantifying the Ripple: Word-of-Mouth and Advertising Effectiveness," Journal of Advertising Research, Cambridge University Press, vol. 44(3), pages 271-280, September.
    15. Jushan Bai, 2013. "Fixed‐Effects Dynamic Panel Models, a Factor Analytical Method," Econometrica, Econometric Society, vol. 81(1), pages 285-314, January.
    16. Acemoglu, Daron & Johnson, Simon & Kermani, Amir & Kwak, James & Mitton, Todd, 2016. "The value of connections in turbulent times: Evidence from the United States," Journal of Financial Economics, Elsevier, vol. 121(2), pages 368-391.
    17. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.
    18. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    19. Onishi, Hiroshi & Manchanda, Puneet, 2012. "Marketing activity, blogging and sales," International Journal of Research in Marketing, Elsevier, vol. 29(3), pages 221-234.
    20. Shyam Gopinath & Pradeep K. Chintagunta & Sriram Venkataraman, 2013. "Blogs, Advertising, and Local-Market Movie Box Office Performance," Management Science, INFORMS, vol. 59(12), pages 2635-2654, December.
    21. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    22. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    23. Feng, Jie & Papatla, Purushottam, 2011. "Advertising: Stimulant or Suppressant of Online Word of Mouth?," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 75-84.
    24. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    25. Olney, Thomas J & Holbrook, Morris B & Batra, Rajeev, 1991. "Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 440-453, March.
    26. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    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. Abouk, Rahi & Jalali, Nima & Papatla, Purushottam, 2024. "Can tweets be word of mouth that changes risky behaviors?," Journal of Business Research, Elsevier, vol. 174(C).
    2. Peter Konhäusner & Marius Thielmann & Veronica Câmpian & Dan-Cristian Dabija, 2021. "Crowdfunding for Independent Print Media: E-Commerce, Marketing, and Business Development," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
    3. Brett R. Gordon & Mitchell J. Lovett & Bowen Luo & James C. Reeder, 2023. "Disentangling the Effects of Ad Tone on Voter Turnout and Candidate Choice in Presidential Elections," Management Science, INFORMS, vol. 69(1), pages 220-243, January.
    4. Dinesh Puranam & Vrinda Kadiyali & Vishal Narayan, 2021. "The Impact of Increase in Minimum Wages on Consumer Perceptions of Service: A Transformer Model of Online Restaurant Reviews," Marketing Science, INFORMS, vol. 40(5), pages 985-1004, September.
    5. Sarah Moshary & Bradley T. Shapiro & Jihong Song, 2020. "How and When to Use the Political Cycle to Identify Advertising Effects," NBER Working Papers 27349, National Bureau of Economic Research, Inc.
    6. Sarah Moshary & Bradley T. Shapiro & Jihong Song, 2021. "How and When to Use the Political Cycle to Identify Advertising Effects," Marketing Science, INFORMS, vol. 40(2), pages 283-304, March.
    7. Thomas J. Weinandy & Kuanchin Chen & Susan Pozo & Michael J. Ryan, 2024. "Twitter-patter: how social media drives foot traffic to retail stores," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 551-569, September.

    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. Joaquin Sanchez & Carmen Abril & Michael Haenlein, 2020. "Competitive spillover elasticities of electronic word of mouth: an application to the soft drink industry," Journal of the Academy of Marketing Science, Springer, vol. 48(2), pages 270-287, March.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    3. Keegan Harris & Anish Agarwal & Chara Podimata & Zhiwei Steven Wu, 2022. "Strategyproof Decision-Making in Panel Data Settings and Beyond," Papers 2211.14236, arXiv.org, revised Dec 2023.
    4. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    5. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    6. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    7. Jan Bruha & Jaromir Tonner, 2018. "An Exchange Rate Floor as an Instrument of Monetary Policy: An Ex-Post Assessment of the Czech Experience," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 68(6), pages 537-549, December.
    8. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    9. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    10. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    11. Bruno Ferman, 2021. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1764-1772, October.
    12. Dallas Dotter & Duncan Chaplin & Maria Bartlett, "undated". "Impacts of School Reforms in Washington, DC on Student Achievement," Mathematica Policy Research Reports 44e95d7566434a21b8d57f951, Mathematica Policy Research.
    13. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    14. Anatoli Colicev & Ashwin Malshe & Koen Pauwels, 2018. "Social Media and Customer-Based Brand Equity: An Empirical Investigation in Retail Industry," Administrative Sciences, MDPI, vol. 8(3), pages 1-16, September.
    15. Taylor K. Odle, 2022. "Free to Spend? Institutional Autonomy and Expenditures on Executive Compensation, Faculty Salaries, and Research Activities," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(1), pages 1-32, February.
    16. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    17. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2021. "The Augmented Synthetic Control Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1789-1803, October.
    18. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    19. Kim, Ho & Hanssens, Dominique M., 2017. "Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 57-74.
    20. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.

    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:kap:qmktec:v:17:y:2019:i:3:d:10.1007_s11129-019-09211-9. 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.springer.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.