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Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation

Author

Listed:
  • Ranjit M. Christopher

    (Department of Marketing and Supply Chain Management, Henry W. Bloch School of Management, University of Missouri–Kansas City, Kansas City, Missouri 64112)

  • Sungho Park

    (Department of Marketing, SNU Business School, Seoul National University, Seoul 08826, Republic of Korea)

  • Sang Pil Han

    (Department of Information Systems, W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • Min-Kyu Kim

    (Department of Marketing, W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

Abstract

Demand-side platforms (DSPs) that purchase digital ad space using real-time bidding (RTB) systems employ “black-box” performance optimizers to adjust bids at run time. Advertisers using field experiments to estimate the marginal value of display ads need to contend with the selective targeting of users by optimizers that adjust bids to target users with a greater propensity to respond favorably (i.e., click or conversion). In this paper, we propose an alternative approach for advertisers who choose to bypass their DSP’s performance optimizers for the purpose of assessing the value of their ads. We show that external frequency caps that set upper limits on the number of ad impressions outside the purview of bidding algorithms can serve as a suitable instrumental variable. Eliminating performance optimizers allows the advertiser to value ads without relying on the support services of the DSP with the added benefit of a broader customer reach and a markedly lower cost. As the focal advertiser disables performance optimizers, any overbidding or underbidding vis-à-vis competition that employs them results in a negative correlation between the numbers of ad impressions won and their underlying quality in real time. Using two large-scale randomized field experiments in different geographies (United States and Asia) and different devices (PC and mobile), we validate the proposed approach and report a positive effect of ad impression count after adjusting for net negative bias.

Suggested Citation

  • Ranjit M. Christopher & Sungho Park & Sang Pil Han & Min-Kyu Kim, 2022. "Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation," Information Systems Research, INFORMS, vol. 33(2), pages 399-412, June.
    • Handle: RePEc:inm:orisre:v:33:y:2022:i:2:p:399-412
    DOI: 10.1287/isre.2021.1050
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    References listed on IDEAS

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    3. De Liu & Jianqing Chen & Andrew B. Whinston, 2010. "Ex Ante Information and the Design of Keyword Auctions," Information Systems Research, INFORMS, vol. 21(1), pages 133-153, March.
    4. Vibhanshu Abhishek & Kartik Hosanagar & Peter S. Fader, 2015. "Aggregation Bias in Sponsored Search Data: The Curse and the Cure," Marketing Science, INFORMS, vol. 34(1), pages 59-77, January.
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