Pricing Models for Online Advertising: CPM vs. CPC
Abstract
Online advertising has transformed the advertising industry with its measurability and accountability. Online software and services supported by online advertising is becoming a reality as evidenced by the success of Google and its initiatives. Therefore, the choice of a pricing model for advertising becomes a critical issue for these firms. We present a formal model of pricing models in online advertising using the principal–agent framework to study the two most popular pricing models: input-based cost per thousand impressions (CPM) and performance-based cost per click-through (CPC). We identify four important factors that affect the preference of CPM to the CPC model, and vice versa. In particular, we highlight the interplay between uncertainty in the decision environment, value of advertising, cost of mistargeting advertisements, and alignment of incentives. These factors shed light on the preferred online-advertising pricing model for publishers and advertisers under different market conditions.
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Content creators are exposed to dynamic pricing, expressed by the cost per thousand impressions or the cost per mille (CPM).
“content creators are exposed to dynamic pricing, expressed by the cost per thousand impressions or the cost per mille (CPM)”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Asdemir et al. 2012
The paper provides in-depth insights about monetary control and perceptions of CPM-related fluctuations that extend existing concepts of dynamic pricing.
“We also provide in-depth insights about monetary control and perceptions of CPM-related fluctuations that extend the existing concepts of dynamic pricing, which is dependent on time, place, and display (Asdemir et al. 2012; Choi et al. 2020; Möhlmann et al. 2021).”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Asdemir et al. 2012
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Hödl, T., & Myrach, T. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00808-9 · Cited by 51 · see citing works
- Authors
- Asdemir, K., Kumar, N., & Jacob, V. S.
- Affiliations
- United Arab Emirates University (AE)
- The University of Texas at Dallas (US)
- Venue
- Information Systems Research
- Published
- 2012-09
- Volume
- 23
- Issue
- 3-part-1
- Pages
- 804-822
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2146855486
- Cited by
- 78
- References
- 0
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 4 · 2023: 10 · 2022: 8 · 2021: 9 · 2020: 4 · 2019: 5 · 2018: 8 · 2017: 5 · 2016: 2 · 2015: 6 · 2014: 2 · 2012: 1
- FWCI
- 3.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- Keywords
- online advertising, cost per thousand impressions, cost per click, principal-agent model, incentive alignment, uncertainty, advertising value, mistargeting, publishers, advertisers
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