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Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com

Author

Listed:
  • Kirthi Kalyanam

    (Santa Clara University)

  • John McAteer

    (Google Inc.)

  • Jonathan Marek

    (Applied Predictive Technologies)

  • James Hodges

    (University of Minnesota)

  • Lifeng Lin

    (University of Minnesota)

Abstract

We investigate the cross channel effects of search engine advertising on Google.com on sales in brick and mortar retail stores. Obtaining causal and actionable estimates in this context is challenging: Brick and mortar store sales vary widely on a weekly basis; offline media dominate the marketing budget; search advertising and demand are contemporaneously correlated; and estimates have to be credible to overcome agency issues between the online and offline marketing groups. We report on a meta-analysis of a population of 15 independent field experiments, in which 13 well-known U.S. multi-channel retailers spent over $4 Million in incremental search advertising. In test markets category keywords were maintained in positions 1-3 for 76 product categories with no search advertising on these keywords in the control markets. Outcomes measured include sales in the advertised categories, total store sales and Return on Ad Spending. We estimate the average effect of each outcome for this population of experiments using a Hierarchical Bayesian (HB) model. The estimates from the HB model provide causal evidence that increasing search engine advertising on broad keywords on Google.com had a positive effect on sales in brick and mortar stores for the advertised categories for this population of retailers. There also was a positive effect on total store sales. Hence the increase in sales in the advertised categories was incremental to the retailer net of any sales borrowed from non-advertised categories. The total store sales increase was a meaningful improvement compared to the baseline sales growth rates. The average Return on Ad Spend (ROAS) is positive, but does not breakeven on average although several retailers achieved or exceeded break-even based only on brick and mortar sales. We examine the robustness of our findings to alternative assumptions about the data specific to this set of experiments. Our estimates suggest online and offline are linked markets, that media planners should account for the offline effects in the planning and execution of search advertising campaigns, and that these effects should be adjusted by category and retailer. Extensive replication and a unique research protocol ensure that our results are general and credible.

Suggested Citation

  • Kirthi Kalyanam & John McAteer & Jonathan Marek & James Hodges & Lifeng Lin, 2018. "Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 1-42, March.
    • Handle: RePEc:kap:qmktec:v:16:y:2018:i:1:d:10.1007_s11129-017-9188-7
    DOI: 10.1007/s11129-017-9188-7
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    Cited by:

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    2. Bradley Shapiro & Günter J. Hitsch & Anna Tuchman, 2020. "Generalizable and Robust TV Advertising Effects," NBER Working Papers 27684, National Bureau of Economic Research, Inc.
    3. Alice Mazzucchelli & Roberto Chierici & Angelo Di Gregorio & Claudio Chiacchierini, 2021. "Is Facebook an effective tool to access foreign markets? Evidence from international export performance of fashion firms," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1107-1144, December.
    4. Xingyue Zhang & Yuliang Yao, 2020. "How Much is Too Much? The Effect of Offline Call Intensity on Online Purchase of Digital Services," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 509-525, March.
    5. Mucunska Palevska, Valentina & Novkovska, Blagica, 2018. "The Participation Of Ict In Activities Of Economic Subjects In Small Economy," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(2), pages 157-168.
    6. Serravalle, Francesca & Vanheems, Régine & Viassone, Milena, 2023. "Does product involvement drive consumer flow state in the AR environment? A study on behavioural responses," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    7. Méndez-Suárez, Mariano & Monfort, Abel, 2020. "The amplifying effect of branded queries on advertising in multi-channel retailing," Journal of Business Research, Elsevier, vol. 112(C), pages 254-260.
    8. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    9. Bradley Shapiro & Günter J. Hitsch & Anna Tuchman, 2020. "Generalizable and Robust TV Advertising Effects," Working Papers 2020-111, Becker Friedman Institute for Research In Economics.
    10. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.
    11. Michael Thomas, 2020. "Spillovers from Mass Advertising: An Identification Strategy," Marketing Science, INFORMS, vol. 39(4), pages 807-826, July.
    12. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.

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    More about this item

    Keywords

    Search engine advertising; Cross channel impact; Field experiments; Bayesian meta analysis; Retail marketing; Advertising; Retail sales; Replication;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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