IDEAS home Printed from
   My bibliography  Save this article

Contextual Advertising


  • Kaifu Zhang

    () (Cheung Kong Graduate School of Business, Beijing 100738, China)

  • Zsolt Katona

    () (Haas School of Business, University of California, Berkeley, Berkeley, California 94720)


Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor.

Suggested Citation

  • Kaifu Zhang & Zsolt Katona, 2012. "Contextual Advertising," Marketing Science, INFORMS, vol. 31(6), pages 980-994, November.
  • Handle: RePEc:inm:ormksc:v:31:y:2012:i:6:p:980-994
    DOI: 10.1287/mksc.1120.0740

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    2. Gene M. Grossman & Carl Shapiro, 1984. "Informative Advertising with Differentiated Products," Review of Economic Studies, Oxford University Press, vol. 51(1), pages 63-81.
    3. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    4. Yongmin Chen & Chuan He, 2011. "Paid Placement: Advertising and Search on the Internet," Economic Journal, Royal Economic Society, vol. 121(556), pages 309-328, November.
    5. Esther Gal-Or & Anthony Dukes, 2003. "Minimum Differentiation in Commercial Media Markets," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 12(3), pages 291-325, September.
    6. Varian, Hal R., 2007. "Position auctions," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1163-1178, December.
    7. Narasimhan, Chakravarthi, 1988. "Competitive Promotional Strategies," The Journal of Business, University of Chicago Press, vol. 61(4), pages 427-449, October.
    8. Anne T. Coughlan, 1985. "Competition and Cooperation in Marketing Channel Choice: Theory and Application," Marketing Science, INFORMS, vol. 4(2), pages 110-129.
    9. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    10. Esther Gal-Or & Mordechai Gal-Or, 2005. "Customized Advertising via a Common Media Distributor," Marketing Science, INFORMS, vol. 24(2), pages 241-253, July.
    11. Dirk Bergemann & Alessandro Bonatti, 2011. "Targeting in advertising markets: implications for offline versus online media," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 417-443, September.
    12. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Individual Marketing with Imperfect Targetability," Marketing Science, INFORMS, vol. 20(1), pages 23-41, November.
    13. Zsolt Katona & Miklos Sarvary, 2010. "The Race for Sponsored Links: Bidding Patterns for Search Advertising," Marketing Science, INFORMS, vol. 29(2), pages 199-215, 03-04.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Wilfred Amaldoss & Preyas S. Desai & Woochoel Shin, 2015. "Keyword Search Advertising and First-Page Bid Estimates: A Strategic Analysis," Management Science, INFORMS, vol. 61(3), pages 507-519, March.
    2. Guitart, Ivan A. & Hervet, Guillaume, 2017. "The impact of contextual television ads on online conversions: An application in the insurance industry," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 480-498.
    3. Wang, Wei & Li, Gang & Fung, Richard Y.K. & Cheng, T.C.E., 2019. "Mobile Advertising and Traffic Conversion: The Effects of Front Traffic and Spatial Competition," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 84-101.
    4. Gong Qiang & Pan Siqi & Yang Huanxing, 2019. "Targeted Advertising on Competing Platforms," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 19(1), pages 1-20, January.
    5. Steven Schmeiser, 2018. "Sharing Audience Data: Strategic Participation in Behavioral Advertising Networks," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 52(3), pages 429-450, May.
    6. Chunhua Wu, 2015. "Matching Value and Market Design in Online Advertising Networks: An Empirical Analysis," Marketing Science, INFORMS, vol. 34(6), pages 906-921, November.
    7. Grewal, Dhruv & Bart, Yakov & Spann, Martin & Zubcsek, Peter Pal, 2016. "Mobile Advertising: A Framework and Research Agenda," Journal of Interactive Marketing, Elsevier, vol. 34(C), pages 3-14.
    8. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.
    9. Ng, Irene C.L. & Wakenshaw, Susan Y.L., 2017. "The Internet-of-Things: Review and research directions," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 3-21.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:31:y:2012:i:6:p:980-994. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Matthew Walls). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.