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Cost-per-Click Pricing for Display Advertising

Author

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  • Sami Najafi-Asadolahi

    (Leavey School of Business, Santa Clara University, Santa Clara, California 95053)

  • Kristin Fridgeirsdottir

    (Management Science and Operations, London Business School, London NW1 4SA, United Kingdom)

Abstract

Display advertising is a $25 billion business with a promising upward revenue trend. In this paper, we consider an online display advertising setting in which a web publisher posts display ads on its website and charges based on the cost-per-click pricing scheme while promising to deliver a certain number of clicks to the ads posted. The publisher is faced with uncertain demand for advertising slots and uncertain traffic to its website as well as uncertain click behavior of visitors. We formulate the problem as a novel queueing system, where the slots correspond to service channels with the service rate of each server inversely related to the number of active servers. We obtain the closed-form solution for the steady-state probabilities of the number of ads in the publisher's system. We determine the publisher's optimal price to charge per click and show that it can increase in the number of advertising slots and the number of promised clicks. We show that the common heuristic used by many web publishers to convert between the cost-per-click and cost-per-impression pricing schemes using the so-called click-through-rate can be misleading because it may incur substantial revenue loss to web publishers. We provide an alternative explanation for the phenomenon observed by several publishers that the click-through-rate tends to drop when they switch from the cost-per-click to cost-per-impression pricing scheme.

Suggested Citation

  • Sami Najafi-Asadolahi & Kristin Fridgeirsdottir, 2014. "Cost-per-Click Pricing for Display Advertising," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 482-497, October.
    • Handle: RePEc:inm:ormsom:v:16:y:2014:i:4:p:482-497
    DOI: 10.1287/msom.2014.0491
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    6. Huaxiao Shen & Yanzhi Li & Jingjing Guan & Geoffrey K.F. Tso, 2021. "A Planning Approach to Revenue Management for Non‐Guaranteed Targeted Display Advertising," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1583-1602, June.
    7. Omar Besbes & Yonatan Gur & Assaf Zeevi, 2016. "Optimization in Online Content Recommendation Services: Beyond Click-Through Rates," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 15-33, February.
    8. Duan, Yongrui & Liu, Peng & Feng, Yixuan, 2022. "Pricing strategies of two-sided platforms considering privacy concerns," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    9. Zilong Jiang & Shu Gao & Mingjiang Li, 2018. "An improved advertising CTR prediction approach based on the fuzzy deep neural network," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-24, May.
    10. H. Mirzaei, Fouad & Ødegaard, Fredrik & Yan, Xinghao, 2015. "User reward programs in online social media," Omega, Elsevier, vol. 57(PB), pages 123-144.
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    13. Zhu Mingxi & Song Michelle, 2024. "Design Information Disclosure under Bidder Heterogeneity in Online Advertising Auctions: Implications of Bid-Adherence Behavior," Papers 2410.05535, arXiv.org.
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    18. 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.

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