IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v35y2016i3p465-483.html
   My bibliography  Save this article

Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces

Author

Listed:
  • Joel Barajas

    (University of California, Santa Cruz, California 95064)

  • Ram Akella

    (School of Information, University of California, Berkeley, California 94720; and University of California, Santa Cruz, California 95064)

  • Marius Holtan

    (AOL Research, Palo Alto, California 94306)

  • Aaron Flores

    (AOL Research, Palo Alto, California 94306)

Abstract

Online Display Advertising’s importance as a marketing channel is partially due to its ability to attribute conversions to campaigns. Current industry practice to measure ad effectiveness is to run randomized experiments using placebo ads, assuming external validity for future exposures. We identify two different effects, i.e., a strategic effect of the campaign presence in marketplaces, and a selection effect due to user targeting; these are confounded in current practices. We propose two novel randomized designs to: (1) estimate the overall campaign attribution without placebo ads, (2) disaggregate the campaign presence and ad effects. Using the Potential Outcomes Causal Model, we address the selection effect by estimating the probability of selecting influenceable users. We show the ex-ante value of continuing evaluation to enhance the user selection for ad exposure mid-flight. We analyze two performance-based (CPA) and one Cost-Per-Impression (CPM) campaigns with 20 million users each. We estimate a negative CPM campaign presence effect due to cross product spillovers. Experimental evidence suggests that CPA campaigns incentivize selection of converting users regardless of the ad, up to 96% more than CPM campaigns, thus challenging the standard practice of targeting most likely converting users.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0982 .

Suggested Citation

  • Joel Barajas & Ram Akella & Marius Holtan & Aaron Flores, 2016. "Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces," Marketing Science, INFORMS, vol. 35(3), pages 465-483, May.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:3:p:465-483
    DOI: 10.1287/mksc.2016.0982
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2016.0982
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2016.0982?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
    ---><---

    References listed on IDEAS

    as
    1. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    2. Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
    3. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    4. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    5. 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.
    6. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    7. Sahni, Navdeep & Zou, Dan & Chintagunta, Pradeep, 2015. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Research Papers 3331, Stanford University, Graduate School of Business.
    8. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    9. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    10. 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.
    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. Du, Ruihuan & Zhong, Yu & Nair, Harikesh S. & Cui, Bo & Shou, Ruyang, 2019. "Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network," Research Papers 3761, Stanford University, Graduate School of Business.
    2. 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.
    3. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
    4. Lukáš Kakalejč & Jozef Bucko & Paulo A. A. Resende & Martina Ferencova, 2018. "Multichannel Marketing Attribution Using Markov Chains," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 7(1), pages 49-60, February.
    5. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    6. Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
    7. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.

    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. Peitz, Martin & Reisinger, Markus, 2014. "The Economics of Internet Media," Working Papers 14-23, University of Mannheim, Department of Economics.
    2. Randall Lewis & Dan Nguyen, 2015. "Display advertising’s competitive spillovers to consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 13(2), pages 93-115, June.
    3. Bayer, Emanuel & Srinivasan, Shuba & Riedl, Edward J. & Skiera, Bernd, 2020. "The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 789-804.
    4. Wei Zhou & Zidong Wang, 2020. "Competing for Search Traffic in Query Markets: Entry Strategy, Platform Design, and Entrepreneurship," Working Papers 20-12, NET Institute.
    5. 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.
    6. Arnold, René & Marcus, J. Scott & Petropoulos, Georgios & Schneider, Anna, 2018. "Is data the new oil? Diminishing returns to scale," 29th European Regional ITS Conference, Trento 2018 184927, International Telecommunications Society (ITS).
    7. 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.
    8. Mariia I. Okuneva & Dmitriy B. Potapov, 2015. "The Effectiveness of Individual Targeting Through Smartphone Application in Retail: Evidence from Field Experiment," HSE Working papers WP BRP 47/MAN/2015, National Research University Higher School of Economics.
    9. 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.
    10. Vilma Todri, 2022. "Frontiers: The Impact of Ad-Blockers on Online Consumer Behavior," Marketing Science, INFORMS, vol. 41(1), pages 7-18, January.
    11. Shun-Yang Lee & Julian Runge & Daniel Yoo & Yakov Bart & Anett Gyurak & J. W. Schneider, 2023. "COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?," Papers 2307.09035, arXiv.org, revised Jan 2024.
    12. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.
    13. Anindya Ghose & Vilma Todri, 2015. "Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior," Working Papers 15-15, NET Institute.
    14. Xiang Hui & Meng Liu, 2022. "Quality Certificates Alleviate Consumer Aversion to Sponsored Search Advertising," CESifo Working Paper Series 9886, CESifo.
    15. Miller, Klaus M. & Skiera, Bernd, 2024. "Economic consequences of online tracking restrictions: Evidence from cookies," International Journal of Research in Marketing, Elsevier, vol. 41(2), pages 241-264.
    16. Garrett Johnson & Julian Runge & Eric Seufert, 2022. "Privacy-Centric Digital Advertising: Implications for Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 9(1), pages 49-54, June.
    17. 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.
    18. Andre Veiga & Tommaso Valletti, 2020. "Attention, recall and purchase: Experimental evidence on online news and advertising," Working Papers 20-15, NET Institute.
    19. Shunyao Yan & Klaus M. Miller & Bernd Skiera, 2020. "How Does the Adoption of Ad Blockers Affect News Consumption?," Papers 2005.06840, arXiv.org, revised Aug 2021.
    20. 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.

    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:inm:ormksc:v:35:y:2016:i:3:p:465-483. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.