Abstract
We find display advertising influences customer search for both the advertised brand and its competitors. We exploit a natural experiment that randomizes ad delivery on 500 million visits to the Yahoo! homepage and compare visitors’ subsequent activities on Yahoo! Search. In three advertisers’ campaigns, display ads increase searches for advertised brands by 30–45 % and for competitors’ brands by up to 23 %. Strikingly, the total number of incremental searches for competitors is 2–8 times the increase for advertisers’ brands. We discuss how these spillovers create strategic complementarities for search advertisers and reduce firms’ investments in advertising.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Lewis (2010) uses this variation to study the effect of frequency on clicks and new account sign-ups.
This is a burgeoning research area—since we released this work in 2011, others have begun to work on similar research.
Blake et al. (2013) found little short-term value of brand keyword advertising for eBay because those who clicked on eBay’s search ads and later made a purchase would have clicked on the organic links to the company’s website on the search result page anyway. Hence, customers use brand keywords to find brands’ websites.
Lewis (2010) discusses this natural experiment and its virtues and failings in depth.
Only 18 % of impressions were followed by another impression from the study within ten minutes.
This method incorrectly attributes some searches to the brand’s product such as queries for the advertiser’s financial statements or stock price. Additionally, ambiguous search terms such as ‘apple,’ ‘galaxy,’ and ‘progressive’ complicates the analysis of queries. After reviewing some of the categorized searches, we believe that most of the categorized searches are for the brands’ products. See Table 4 for examples of affected brand-related queries.
We do not analyze the Best Buy campaign for competitive spillovers because by 2011 Best Buy was the only nationwide electronics retailer remaining after the demise of Circuit City and many other stores.
We present this simple analysis rather than a linear regression with control variables because the exogenous variation of the natural experiment removes the need for control variables to identify the effect of the display ad and reduces the linear regression to a comparison of means between the test and control group. Our analysis is equivalent to estimating the following (saturated) linear probability model
$$ Search_{ijt} = \alpha_{j} + \beta_{j} AD_{it} + \epsilon_{ijt} $$(1)where S e a r c h i j t is an indicator variable equal to one if user i searched for brand j during period t and zero otherwise; A D i t is another indicator variable equal to one if the target ad was delivered to user i during period t; 𝜖 i j t is the error term.
Robustness checks examining the effects of the number of prior target exposures during the control periods showed no statistically significant effect, with the point estimates generally negative, suggesting no misattribution. These results are available in the Online Appendix.
Any diminishing returns to additional impressions (i.e., wearing out) of the effects, which could complicate this simplified counterfactual computation, are found to be of modest importance in the Online Appendix. Similar to the findings in Lewis (2010) for clicks and new account sign-ups and in Johnson et al. (2015) for combined online and in-store sales, our model shows no significant effects of decreasing marginal returns, but the confidence intervals grow wider in the number of impressions.
Nearly all users who searched for either the advertiser or at least one of its competitors did not search for both.
The search queries were already “canonized” in the Yahoo! Search raw data: user-entered queries are transformed via many grammatical and data-driven rules such as correcting common misspellings, trimming pluralized words ending in ‘s’s, and removing articles and common words such as ‘the’ and ‘out.’ As a result, a query such as ‘FX Lights Out’ becomes ‘fx light’ in the data.
For example, for the Acura ad, we analyze 48,656 subdomains that received at least 40 clicks in our sample; only 2 of the top 25 subdomains, ranked by p-value, are not obviously car-related and were considered false positives. This observation, combined with the fact that car-related searches account for a small share of all online search behavior, leads us to expect car-related false positives to have no meaningful effect on our findings.
All statistically significant relevant outcomes show positive effects—we include potentially negatively affected outcomes under the control ads’ significant terms in Table 4 because, by design, any negative effects of the target ad are observationally equivalent to positive effects of the control ad.
An unpublished study by one of the authors in 2010 found that showing display ads on both mobile and desktop platforms to the same individual was very difficult due to the endogeneity of individuals’ platform usage.
References
Alba, J.W., & Chattopadhyay, A. (1985). Effects of context and part-category cues on recall of competing brands. Journal of Marketing Research, 340–349.
Anderson, E. T., & Simester, D. (2013). Advertising in a competitive market: the role of product standards, customer learning, and switching costs. Journal of Marketing Research, 50(4), 489–504.
Atlas Institute (2008). Engagement mapping: a new measurement standard is emerging for advertisers. Microsoft.
Autobytel (2011). Autobytel - new cars. Retrieved Jun. 30, 2011. http://www.autobytel.com/new-cars.
Bagwell, K. (2007). The economic analysis of advertising. Handbook of Industrial Organization, 3, 1701–1844.
Blake, T., Nosko, C., & Tadelis, S. (2013). Consumer heterogeneity and paid search effectiveness: a large scale field experiment. Working Paper.
Butters, G.R. (1977). Equilibrium distributions of sales and advertising prices. The Review of Economic Studies, 465–491.
Carmi, E., Oestreicher-Singer, G., & Sundararajan, A. (2010). Is Oprah contagious? Identifying demand spillovers in online networks. Identifying demand spillovers in online networks (August 3, 2012). NET Institute Working Paper No. 10–18.
Chiou, L., & Tucker, C (2011). How does pharmaceutical advertising affect consumer search? Available at SSRN 1542934.
Drèze, X., & Hussherr, F.X. (2003). Internet advertising: is anybody watching? Journal of Interactive Marketing, 17(4), 8–23.
Economides, N., & Salop, S.C. (1992). Competition and integration among complements, and network market structure. The Journal of Industrial Economics, 105–123.
Franklin, E. (2011). CNET looks at current and upcoming tablets. Retrieved Jul. 29, 2011. CNET. http://news.cnet.com/8301-17938_105-20037960-1/cnet-looks-at-current-and-upcoming-tablets.
Ghose, A., Han, S.P., & Sung H.P (2013). Analyzing the interdependence between web and mobile advertising analyzing the interdependence between web and mobile advertising: a randomized field experiment.
Goldfarb, A., & Tucker, C. (2011a). Online display advertising: targeting and obtrusiveness. Marketing Science, 30(3), 389–404.
Goldfarb, A., & Tucker, C. (2011b). Privacy regulation and online advertising. Management Science, 57(1), 57–71.
Goldfarb, A., & Tucker, C. (2011c). Search engine advertising: channel substitution when pricing ads to context. Management Science, 57(3), 458–470.
Grossman, G.M., & Shapiro, C. (1984). Informative advertising with differentiated products. The Review of Economic Studies, 51(1), 63–81.
IAB and PwC (2004). IAB internet advertising revenue report FY 2003. Retrieved Mar. 11, 2015. http://www.iab.net/AdRevenueReport.
IAB and PwC (2014). IAB internet advertising revenue report FY 2013. Retrieved Mar. 11, 2015. http://www.iab.net/AdRevenueReport.
Jeziorski, P., & Segal, I. (2009). What makes them click: empirical analysis of consumer demand for search advertising. SSRN eLibrary, 33.
Johnson, G. A., Lewis, R. A., & Reiley, D. H. (2015). Location, location, location: repetition and proximity increase advertising effectiveness. Working paper.
Joo, M., Wilbur, K.C., Cowgill, B., & Zhu, Y. (2013). Television advertising and online search. Management Science.
Joo, M., Wilbur, K., & Zhu, Y. (2011). Does Television Advertising Influence Online Search? Available at SSRN 1720713. http://ssrn.com/abstract=1720713.
Kaiser, H. M., Alston, J.M., Crespi, J.M., & Sexton, R.J. (2005). The economics of commodity promotion programs: lessons from California. Peter Lang Pub Incorporated.
Kwoka, J.E. Jr. (1993). The sales and competitive effects of styling and advertising practices in the US auto industry. The Review of Economics and Statistics, 649–656.
Lewis, R. (2010). Where’s the “Wear-Out?”: Online display ads and the impact of frequency. Measuring the effects of online advertising on human behavior using natural and field experiments. PhD dissertation, Massachusetts Institute of Technology.
Lewis, R.A., & Reiley, D.H. (2013). Down-to-the-minute effects of super bowl advertising on online search behavior. In ACM conference on electronic commerce (pp. 639–656).
Lewis, R.A., & Reiley, D.H. (2014). Online ads and offline sales: measuring the effects of retail advertising via a controlled experiment on Yahoo! Quantitative Marketing and Economics, 12(3), 235–266.
Lewis, R.A., & Rao, J.M. (2015). The unfavorable economics of measuring the returns to advertising. Quarterly Journal of Economics. forthcoming.
Lewis, R.A., Rao, J.M., & Reiley, D.H. (2011). Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising. In Proceedings of the 20th international conference on World wide web (pp. 157–166). ACM.
Mayzlin, D., & Shin, J. (2011). Uninformative advertising as an invitation to search. Marketing Science, 30(4), 666–685.
Mintel Reports Database (2009). Auto Insurance. Retrieved Aug. 1, 2011. http://academic.mintel.proxy.uchicago.edu.
Mintel Reports Database (2010). Attitudes to internet and new media marketing. Retrieved Sept. 8, 2011. http://academic.mintel.proxy.uchicago.edu.
Naik, P.A., & Raman, K. (2003). Understanding the impact of synergy in multimedia communications. Journal of Marketing Research, 375–388.
Nedungadi, P. (1990). Recall and consumer consideration sets: influencing choice without altering brand evaluations. Journal of Consumer Research, 263–276.
Reiley, D.H., Li, S.-M., & Lewis, R.A. (2010). Northern exposure: a field experiment measuring externalities between search advertisements. In Proceedings of the 11th ACM conference on electronic commerce (pp. 297–304). ACM.
Rutz, O.J., & Bucklin, R.E. (2011). From generic to branded: a model of spillover in paid search advertising. Journal of Marketing Research, 48(1), 87–102. doi:10.1509/jmkr.48.1.87.
Sahni, N. (2013). Advertising spillovers: field-experiment evidence and implications for returns from advertising. Working Paper.
Shapiro, B.T. (2013). Positive spillovers and free riding in advertising of prescription pharmaceuticals: The case of antidepressants.
Sinkinson, M., & Starc, A. (2014). Consumer advertising competition in prescription drugs.
Varian, H.R. (2006). Advertising commodities can be tricky, but it does pay off. Retrieved Nov. 12, 2012. The New York Times. http://people.ischool.berkeley.edu/~hal/people/hal/NYTimes/2006-06-01.html.
Wu, L., & Brynjolfsson, E. (2009). The future of prediction: how Google searches foreshadow housing prices and sales. Available at SSRN 2022293.
Acknowledgments
This research was performed during the summer of 2011 while at Yahoo! Research. We thank Yahoo!, Inc. for facilitating this research through financial support, data access, and academic independence. We thank Iwan Sakran for helping with data extraction. We would also like to express our thanks to David Reiley, Justin Rao, Preston McAfee, Patrick Bajari, and Catherine Tucker for their constructive feedback and many other participants at the Berkeley Marketing Seminar, 2012 NBER SI of IT & Digitization Workshop, and FTC Microeconomics Conference for their questions, comments, and critiques.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Lewis, R., Nguyen, D. Display advertising’s competitive spillovers to consumer search. Quant Mark Econ 13, 93–115 (2015). https://doi.org/10.1007/s11129-015-9155-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11129-015-9155-0