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Display advertising’s competitive spillovers to consumer search

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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.

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  1. Lewis (2010) uses this variation to study the effect of frequency on clicks and new account sign-ups.

  2. This is a burgeoning research area—since we released this work in 2011, others have begun to work on similar research.

  3. 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.

  4. Lewis (2010) discusses this natural experiment and its virtues and failings in depth.

  5. Only 18 % of impressions were followed by another impression from the study within ten minutes.

  6. 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.

  7. 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.

  8. 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

    $$ Search_{ijt} = \alpha_{j} + \beta_{j} AD_{it} + \epsilon_{ijt} $$

    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.

  9. 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.

  10. 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.

  11. Nearly all users who searched for either the advertiser or at least one of its competitors did not search for both.

  12. 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.

  13. 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.

  14. 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.

  15. 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.


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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.

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Correspondence to Randall Lewis.

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Lewis, R., Nguyen, D. Display advertising’s competitive spillovers to consumer search. Quant Mark Econ 13, 93–115 (2015).

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