Advertising and brand attitudes: Evidence from 575 brands over five years

Abstract

Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables (perceived quality, perceived value and recent satisfaction) and three types of advertising (national traditional, local traditional and digital). The data represent ten million brand attitude surveys and $264 billion spent on ads by 575 regular advertisers over a five-year period, approximately 37% of all ad spend measured between 2008 and 2012. Inclusion of brand/quarter fixed effects and industry/week fixed effects brings parameter estimates closer to expectations without major reductions in estimation precision. The findings indicate that (i) national traditional ads increase perceived quality, perceived value, and recent satisfaction; (ii) local traditional ads increase perceived quality and perceived value; (iii) digital ads increase perceived value; and (iv) competitor ad effects are generally negative.

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Notes

  1. 1.

    The same labels are also applied to advertising content, which typically reflects the goals of the ad campaign, but is regrettably unobserved in our dataset.

  2. 2.

    https://cmosurvey.org/wp-content/uploads/sites/15/2018/02/The_CMO_Survey-Topline_Report-Feb-2018-1.pdf, accessed March 2018.

  3. 3.

    Advertising experiments are scarce in general; see, e.g., Rao and Simonov (2018).

  4. 4.

    Digital advertising delivery facilitates experimentation and the measurement of individual-level response data, but the advertising medium is beset by several widespread problems that complicate experimental analysis, including ad (non-)viewability (IAB 2015), a high incidence of ad blocking by default (Shiller et al. 2018), non-human traffic (WhiteOps 2016), and advertising blindness (e.g., Owens et al. 2014). It remains unclear whether such display advertising results apply to other media.

  5. 5.

    https://www.ama.org/publications/MarketingNews/Documents/2017-top-50-gold-report-article.pdf, accessed March 2018.

  6. 6.

    https://www.facebook.com/business/learn/facebook-brand-polling, accessed March 2018.

  7. 7.

    The reporting incentives are mixed. A media outlet could exaggerate its ad price to offer perceived discounts in negotiations with advertisers. Or, a media outlet might underreport its ad price to attract interested advertisers. Actual ad prices in traditional media are typically set in confidential bilateral negotiations and may reflect price discrimination or quantity discounts. Digital advertising prices are typically set in complex, rapidly changing spot auction markets within or between ad networks, demand-side platforms and supply-side platforms.

  8. 8.

    https://www.wsj.com/articles/SB10001424052702304192704577406394017764460, https://www.wsj.com/articles/p-g-cuts-more-than-100-million-in-largely-ineffective-digital-ads-1501191104, accessed March 2018.

  9. 9.

    See, for example, http://pages.stern.nyu.edu/~atakos/studentevents/3-28-12MeasuringROMISlideDeck.pdf, or https://en.wikipedia.org/wiki/Marketing_mix_modeling, accessed March 2018.

  10. 10.

    We remain circumspect about this argument, as agencies may be aware of their clients’ evaluation function and act to maximize their own incentives to demonstrate advertising effects to their clients.

  11. 11.

    Industry/week fixed effects were essentially replaced by a separate set of week fixed effects estimated within each partition.

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Acknowledgments

The authors thank Wes Hartmann, two anonymous reviewers, and numerous seminar audiences for helpful comments and discussions. This study was made possible by the authors’ employers, and data were drawn from standard Kantar and YouGov data sources, but the analysis is the authors’ alone as it was not funded or otherwise influenced by any other party.

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Correspondence to Mingyu Joo.

Appendix

Appendix

This appendix presents information and results that are not included in the main body for brevity. Table 12 lists all brands in the sample by industry. Tables 131415 and 16 present ad parameter estimates and their standard errors in descriptive models, models with industry/week controls, models with brand/quarter controls, and all-controls models, respectively. Tables 171819 and 20 present parameter estimates and standard errors for lagged dependent variables in all four models. Table 21 presents ad parameter estimate variation with number of lags included in the perceived quality all-controls model specification. Tables 22 and 23 report industry-specific ad parameters in the perceived value and recent satisfaction models. Tables 24 and 25 indicate results for the all-controls models estimated in data aggregated into two-week and four-week intervals.

Table 12 Summary of brands in yougov data by industry
Table 13 Ad parameter estimates in descriptive model
Table 14 Ad parameter estimates in model with industry/week control
Table 15 Ad parameter estimates in model with brand/quarter control
Table 16 Ad parameter estimates in all controls model
Table 17 Parameter estimates for lagged D.V.’s in descriptive model
Table 18 Parameter estimates for lagged D.V.’s in model with industry/week control
Table 19 Parameter estimates for lagged D.V.’s in model with brand/quarter control
Table 20 Parameter estimates for lagged D.V.’s in all controls model
Table 21 Advertising parameter estimate variation with ta in all−controls perceived quality model
Table 22 Ad parameter estimates by industry on perceived value
Table 23 ad parameter estimates by industry on recent satisfaction
Table 24 All−controls model estimated using 2−week data aggregation
Table 25 All−controls model estimated using 4−week data aggregation

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Du, R.Y., Joo, M. & Wilbur, K.C. Advertising and brand attitudes: Evidence from 575 brands over five years. Quant Mark Econ 17, 257–323 (2019). https://doi.org/10.1007/s11129-018-9204-6

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Keywords

  • Advertising
  • Brand attitude
  • Brand tracking metrics
  • Media mix models

JEL Classification

  • M31
  • M37
  • L81