IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v37y2018i5p771-792.html
   My bibliography  Save this article

Beyond the Last Touch: Attribution in Online Advertising

Author

Listed:
  • Ron Berman

    (Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Online advertisers often utilize multiple publishers to deliver ads to multihoming consumers. These ads often generate externalities and their exposure is uncertain, impacting advertising effectiveness across publishers. We analyze the inefficiencies created by externalities and uncertainty when information is symmetric between advertisers and publishers, in contrast to most previous research that assumes information asymmetry. Although these inefficiencies cannot be resolved through publisher-side actions, attribution methods that measure campaign uncertainty can serve as alternative solutions to help advertisers adjust their strategies. Attribution creates a virtual competition between publishers, resulting in a team compensation problem. The equilibrium may potentially increase the aggressiveness of advertiser bidding, leading to increased advertiser profits. The popular last-touch method is shown to overincentivize ad exposures, often resulting in lower advertiser profits. The Shapley value achieves an increase in profits compared with the last-touch method. Popular publishers and those that appear early in the conversion funnel benefit the most from advertisers using last-touch attribution. The increase in advertiser profits comes at the expense of total publisher profits and often results in decreased ad allocation efficiency. We also find that the prices paid in the market will decrease when more sophisticated attribution methods are adopted.

Suggested Citation

  • Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
  • Handle: RePEc:inm:ormksc:v:37:y:2018:i:5:p:771-792
    DOI: 10.1287/mksc.2018.1104
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2018.1104
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2018.1104?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. 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.
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Rosenthal, Robert W. & Wang, Ruqu, 1996. "Simultaneous Auctions with Synergies and Common Values," Games and Economic Behavior, Elsevier, vol. 17(1), pages 32-55, November.
    4. Bengt Holmstrom, 1982. "Moral Hazard in Teams," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 324-340, Autumn.
    5. Krishna, Vijay & Rosenthal, Robert W., 1996. "Simultaneous Auctions with Synergies," Games and Economic Behavior, Elsevier, vol. 17(1), pages 1-31, November.
    6. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    7. Hongshuang (Alice) Li & P. K. Kannan & Siva Viswanathan & Abhishek Pani, 2016. "Attribution Strategies and Return on Keyword Investment in Paid Search Advertising," Marketing Science, INFORMS, vol. 35(6), pages 831-848, November.
    8. 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.
    9. Anderl, Eva & Becker, Ingo & von Wangenheim, Florian & Schumann, Jan Hendrik, 2016. "Mapping the customer journey: Lessons learned from graph-based online attribution modeling," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 457-474.
    10. Kireyev, Pavel & Pauwels, Koen & Gupta, Sunil, 2016. "Do display ads influence search? Attribution and dynamics in online advertising," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 475-490.
    11. Nikhil Agarwal & Susan Athey & David Yang, 2009. "Skewed Bidding in Pay-per-Action Auctions for Online Advertising," American Economic Review, American Economic Association, vol. 99(2), pages 441-447, May.
    12. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    13. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    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. Berman, Ron & Heller, Yuval, 2020. "Naive Analytics Equilibrium," MPRA Paper 103824, University Library of Munich, Germany.
    3. Jialie Chen & Vithala R. Rao, 2021. "Measuring the Effects of Marketing Solicitations," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(4), pages 111-122, December.
    4. 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.
    5. 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.
    6. 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.
    7. Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.
    8. Anna D’Annunzio & Antonio Russo, 2020. "Ad Networks and Consumer Tracking," Management Science, INFORMS, vol. 66(11), pages 5040-5058, November.
    9. Kaifeng Zhao & Seyed Hanif Mahboobi & Saeed R. Bagheri, 2018. "Shapley Value Methods for Attribution Modeling in Online Advertising," Papers 1804.05327, arXiv.org.
    10. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.
    11. Yu (Jeffrey) Hu & Jiwoong Shin & Zhulei Tang, 2016. "Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action," Management Science, INFORMS, vol. 62(7), pages 2022-2038, July.
    12. 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.
    13. Stefano Balietti & Brennan Klein & Christoph Riedl, 2021. "Optimal design of experiments to identify latent behavioral types," Experimental Economics, Springer;Economic Science Association, vol. 24(3), pages 772-799, September.
    14. S{i}la Ada & Nadia Abou Nabout & Elea McDonnell Feit, 2020. "Context information increases revenue in ad auctions: Evidence from a policy change," Papers 2012.00840, arXiv.org.
    15. Amin Sayedi, 2018. "Real-Time Bidding in Online Display Advertising," Marketing Science, INFORMS, vol. 37(4), pages 553-568, August.
    16. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    17. Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.
    18. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.
    19. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.
    20. 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.
    21. W. Jason Choi & Amin Sayedi, 2023. "Open and Private Exchanges in Display Advertising," Marketing Science, INFORMS, vol. 42(3), pages 451-475, May.
    22. Raghav Singal & Omar Besbes & Antoine Desir & Vineet Goyal & Garud Iyengar, 2022. "Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising," Management Science, INFORMS, vol. 68(10), pages 7457-7479, October.

    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. 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. 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.
    4. 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.
    5. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    6. Berman, Ron & Heller, Yuval, 2020. "Naive Analytics Equilibrium," MPRA Paper 103824, University Library of Munich, Germany.
    7. Jialie Chen & Vithala R. Rao, 2021. "Measuring the Effects of Marketing Solicitations," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(4), pages 111-122, December.
    8. Raghav Singal & Omar Besbes & Antoine Desir & Vineet Goyal & Garud Iyengar, 2022. "Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising," Management Science, INFORMS, vol. 68(10), pages 7457-7479, October.
    9. 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.
    10. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    11. Ewerhart, Christian & Cassola, Nuno & Valla, Natacha, 2012. "Overbidding in fixed rate tenders: The role of exposure risk," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 539-549.
    12. Bradley T. Shapiro, 2020. "Advertising in Health Insurance Markets," Marketing Science, INFORMS, vol. 39(3), pages 587-611, May.
    13. Ravi Bapna & Chrysanthos Dellarocas & Sarah Rice, 2010. "Vertically Differentiated Simultaneous Vickrey Auctions: Theory and Experimental Evidence," Management Science, INFORMS, vol. 56(7), pages 1074-1092, July.
    14. Lamprirni Zarpala & Dimitris Voliotis, 2022. "A core-selecting auction for portfolio's packages," Papers 2206.11516, arXiv.org, revised Feb 2024.
    15. Meng, Xin & Gunay, Hikmet, 2017. "Exposure problem in multi-unit auctions," International Journal of Industrial Organization, Elsevier, vol. 52(C), pages 165-187.
    16. 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.
    17. Lamy, Laurent, 2012. "On minimal ascending auctions with payment discounts," Games and Economic Behavior, Elsevier, vol. 75(2), pages 990-999.
    18. Lawrence M. Ausubel & Peter Cramton & R. Preston McAfee & John McMillan, 1997. "Synergies in Wireless Telephony: Evidence from the Broadband PCS Auctions," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(3), pages 497-527, September.
    19. Jacob LaRiviere & Mikolaj Czajkowski & Nick Hanley & Katherine Simpson, 2016. "What is the Causal Impact of Knowledge on Preferences in Stated Preference Studies?," Working Papers 2016-12, Faculty of Economic Sciences, University of Warsaw.
    20. Brusco, Sandro & Lopomo, Giuseppe & Marx, Leslie M., 2009. "The [`]Google effect' in the FCC's 700Â MHz auction," Information Economics and Policy, Elsevier, vol. 21(2), pages 101-114, June.

    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:37:y:2018:i:5:p:771-792. 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.