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Beyond the Last Touch: Attribution in Online Advertising

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  • 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
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    References listed on IDEAS

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    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.
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    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. 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.
    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.
    11. Amin Sayedi, 2018. "Real-Time Bidding in Online Display Advertising," Marketing Science, INFORMS, vol. 37(4), pages 553-568, August.
    12. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    13. 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.
    14. 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.
    15. 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.
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    17. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.
    18. 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.
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    20. 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.

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