IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v16y2018i1d10.1007_s11129-017-9188-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-017-9188-7
    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/s11129-017-9188-7?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. Randall Lewis & David Reiley, 2014. "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 235-266, September.
    2. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    3. Navdeep S. Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    4. Shapiro, Stewart & MacInnis, Deborah J & Heckler, Susan E, 1997. "The Effects of Incidental Ad Exposure on the Formation of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(1), pages 94-104, June.
    5. Steven D. Levitt & John A. List, 2007. "Viewpoint: On the generalizability of lab behaviour to the field," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(2), pages 347-370, May.
    6. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    7. Navdeep Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    8. Brucks, Merrie, 1985. "The Effects of Product Class Knowledge on Information Search Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(1), pages 1-16, June.
    9. Thomas Blake & Chris Nosko & Steven Tadelis, 2015. "Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment," Econometrica, Econometric Society, vol. 83, pages 155-174, January.
    10. Bajari, Patrick & Hortacsu, Ali, 2003. "The Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 329-355, Summer.
    11. Navdeep S. Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    12. Erik Brynjolfsson & Astrid Dick & Michael Smith, 2010. "A nearly perfect market?," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 1-33, March.
    13. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    14. Joseph O. Eastlack, Jr. & Ambar G. Rao, 1989. "Advertising Experiments at the Campbell Soup Company," Marketing Science, INFORMS, vol. 8(1), pages 57-71.
    15. Naik, Prasad A. & Peters, Kay, 2009. "A Hierarchical Marketing Communications Model of Online and Offline Media Synergies," Journal of Interactive Marketing, Elsevier, vol. 23(4), pages 288-299.
    16. John U. Farley & Donald R. Lehmann & Alan Sawyer, 1995. "Empirical Marketing Generalization Using Meta-Analysis," Marketing Science, INFORMS, vol. 14(3_supplem), pages 36-46.
    17. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    18. Zacharias Maniadis & Fabio Tufano & John A. List, 2014. "One Swallow Doesn't Make a Summer: New Evidence on Anchoring Effects," American Economic Review, American Economic Association, vol. 104(1), pages 277-290, January.
    19. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    20. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    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. Shankar, Venkatesh & Kalyanam, Kirthi & Setia, Pankaj & Golmohammadi, Alireza & Tirunillai, Seshadri & Douglass, Tom & Hennessey, John & Bull, J.S. & Waddoups, Rand, 2021. "How Technology is Changing Retail," Journal of Retailing, Elsevier, vol. 97(1), pages 13-27.
    2. 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.
    3. Bradley Shapiro & Günter J. Hitsch & Anna Tuchman, 2020. "Generalizable and Robust TV Advertising Effects," NBER Working Papers 27684, National Bureau of Economic Research, Inc.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Michael Thomas, 2020. "Spillovers from Mass Advertising: An Identification Strategy," Marketing Science, INFORMS, vol. 39(4), pages 807-826, July.
    11. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.
    12. 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.

    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. 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.
    2. Navdeep S. Sahni & Harikesh S. Nair, 2020. "Sponsorship Disclosure and Consumer Deception: Experimental Evidence from Native Advertising in Mobile Search," Marketing Science, INFORMS, vol. 39(1), pages 5-32, January.
    3. 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.
    4. Garrett A. Johnson & Randall A. Lewis & David H. Reiley, 2017. "When Less Is More: Data and Power in Advertising Experiments," Marketing Science, INFORMS, vol. 36(1), pages 43-53, January.
    5. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    6. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    7. Caio Waisman & Navdeep S. Sahni & Harikesh S. Nair & Xiliang Lin, 2019. "Parallel Experimentation and Competitive Interference on Online Advertising Platforms," Papers 1903.11198, arXiv.org, revised Feb 2024.
    8. 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.
    9. Navdeep S. Sahni & Dan Zou & Pradeep K. Chintagunta, 2017. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Management Science, INFORMS, vol. 63(8), pages 2688-2705, August.
    10. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    11. Min Tian & Paul R. Hoban & Neeraj Arora, 2024. "What Cookie-Based Advertising Effectiveness Fails to Measure," Marketing Science, INFORMS, vol. 43(2), pages 407-418, March.
    12. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    13. Johannes Hermle & Giorgio Martini, 2022. "Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split," Papers 2207.00206, arXiv.org.
    14. Kurt P. Munz & Minah H. Jung & Adam L. Alter, 2020. "Name Similarity Encourages Generosity: A Field Experiment in Email Personalization," Marketing Science, INFORMS, vol. 39(6), pages 1071-1091, November.
    15. Anja Lambrecht & Catherine Tucker & Caroline Wiertz, 2018. "Advertising to Early Trend Propagators: Evidence from Twitter," Marketing Science, INFORMS, vol. 37(2), pages 177-199, March.
    16. Thomas W. Frick & Rodrigo Belo & Rahul Telang, 2023. "Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment," Management Science, INFORMS, vol. 69(3), pages 1665-1686, March.
    17. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    18. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    19. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.
    20. Chen He & Tobias J. Klein, 2023. "Advertising as a Reminder: Evidence from the Dutch State Lottery," Marketing Science, INFORMS, vol. 42(5), pages 892-909, September.

    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

    Statistics

    Access and download statistics

    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:16:y:2018:i:1:d:10.1007_s11129-017-9188-7. 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.