Online Display Advertising Markets: A Literature Review and Future Directions
Abstract
Display advertising is a $50 billion industry in which advertisers’ (e.g., P&G, Geico) demand for impressions is matched to publishers’ (e.g., Facebook, Wall Street Journal) supply of them. An ideal match is one wherein the publisher’s ad impression is assigned to the advertiser with the highest value for it. Intermediaries (e.g., Google) facilitate this match between advertisers and publishers by managing data and providing optimization tools and algorithms for serving ads. Although these markets exhibit high allocative efficiency, we argue there is considerable scope for improvement.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 Choi et al. 2020
CPM changes and is determined by time, place, and display.
“It is in the nature of CPM that it changes and is determined by time, place, and display”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Choi et al. 2020
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 Choi et al. 2020
Cites
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Lee, D., Hosanagar, K., & Nair, H. S. (2018) · Management Science · doi:10.1287/mnsc.2017.2902 · Cited by 1,069 · see citing works
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015) · Journal of Retailing · doi:10.1016/j.jretai.2014.09.005 · Cited by 868 · see citing works
Social Networks, Personalized Advertising, and Privacy Controls
Tucker, C. E. (2014) · Journal of Marketing Research · doi:10.1509/jmr.10.0355 · Cited by 829 · see citing works
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019) · Marketing Science · doi:10.1287/mksc.2018.1135 · Cited by 287 · see citing works
Lewis, R. A., & Reiley, D. H. (2014) · Quantitative Marketing and Economics · doi:10.1007/s11129-014-9146-6 · Cited by 211 · see citing works
Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting
Trusov, M., Ma, L., & Jamal, Z. (2016) · Marketing Science · doi:10.1287/mksc.2015.0956 · Cited by 165 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Hödl, T., & Myrach, T. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00808-9 · Cited by 51 · see citing works
Epistemic fragmentation poses a threat to the governance of online targeting
Milano, S., Mittelstadt, B., Wachter, S., & Russell, C. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00358-3 · Cited by 29 · see citing works
- Authors
- Choi, H., Mela, C. F., Balseiro, S. R., & Leary, A.
- Affiliations
- University of Rochester (US)
- Duke University (US)
- Columbia University (US)
- CBS Interactive, San Francisco, California 92105
- Venue
- Information Systems Research
- Published
- 2020-06
- Volume
- 31
- Issue
- 2
- Pages
- 556-575
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2892075429
- Cited by
- 181
- References
- 6
- Citations by year
- 2026: 27 · 2025: 30 · 2024: 29 · 2023: 26 · 2022: 22 · 2021: 26 · 2020: 14 · 2019: 6 · 2016: 1
- FWCI
- 13.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- 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
- Keywords
- online display advertising, allocative efficiency, demand-side platforms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
