IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v29y2010i4p602-623.html
   My bibliography  Save this article

Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?

Author

Listed:
  • Sha Yang

    (Stern School of Business, New York University, New York, New York 10012)

  • Anindya Ghose

    (Stern School of Business, New York University, New York, New York 10012)

Abstract

The phenomenon of paid search advertising has now become the most predominant form of online advertising in the marketing world. However, we have little understanding of the impact of search engine advertising on consumers' responses in the presence of organic listings of the same firms. In this paper, we model and estimate the interrelationship between organic search listings and paid search advertisements. We use a unique panel data set based on aggregate consumer response to several hundred keywords over a three-month period collected from a major nationwide retailer store chain that advertises on Google. In particular, we focus on understanding whether the presence of organic listings on a search engine is associated with a positive, a negative, or no effect on the click-through rates of paid search advertisements, and vice versa for a given firm. We first build an integrated model to estimate the relationship between different metrics such as search volume, click-through rates, conversion rates, cost per click, and keyword ranks. A hierarchical Bayesian modeling framework is used and the model is estimated using Markov chain Monte Carlo methods. Our empirical findings suggest that click-throughs on organic listings have a positive interdependence with click-throughs on paid listings, and vice versa. We also find that this positive interdependence is asymmetric such that the impact of organic clicks on increases in utility from paid clicks is 3.5 times stronger than the impact of paid clicks on increases in utility from organic clicks. Using counterfactual experiments, we show that on an average this positive interdependence leads to an increase in expected profits for the firm ranging from 4.2% to 6.15% when compared to profits in the absence of this interdependence. To further validate our empirical results, we also conduct and present the results from a controlled field experiment. This experiment shows that total click-through rates, conversions rates, and revenues in the presence of both paid and organic search listings are significantly higher than those in the absence of paid search advertisements. The results predicted by the econometric model are also corroborated in this field experiment, which suggests a causal interpretation to the positive interdependence between paid and organic search listings. Given the increased spending on search engine-based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.

Suggested Citation

  • Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:4:p:602-623
    DOI: 10.1287/mksc.1090.0552
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1090.0552
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1090.0552?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hawkins, Scott A & Hoch, Stephen J, 1992. "Low-Involvement Learning: Memory without Evaluation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 212-225, September.
    2. Shapiro, Stewart & MacInnis, Deborah J & Heckler, Susan E, 1997. "The Effects of Incidental Ad Exposure on the Formation of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(1), pages 94-104, June.
    3. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
    4. Bajari, Patrick & Hong, Han & Krainer, John & Nekipelov, Denis, 2010. "Estimating Static Models of Strategic Interactions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 469-482.
    5. Eric T. Bradlow & David C. Schmittlein, 2000. "The Little Engines That Could: Modeling the Performance of World Wide Web Search Engines," Marketing Science, INFORMS, vol. 19(1), pages 43-62, June.
    6. Juan Feng & Hemant K. Bhargava & David M. Pennock, 2007. "Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 137-148, February.
    7. Patrali Chatterjee & Donna L. Hoffman & Thomas P. Novak, 2003. "Modeling the Clickstream: Implications for Web-Based Advertising Efforts," Marketing Science, INFORMS, vol. 22(4), pages 520-541, May.
    8. Anindya Ghose & Sha Yang, 2007. "An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets," Working Papers 07-35, NET Institute, revised Sep 2007.
    9. Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
    10. 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.
    11. Michael R. Baye & John Morgan, 2001. "Information Gatekeepers on the Internet and the Competitiveness of Homogeneous Product Markets," American Economic Review, American Economic Association, vol. 91(3), pages 454-474, June.
    12. 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.
    13. Hausman, Jerry A, 1975. "An Instrumental Variable Approach to Full Information Estimators for Linear and Certain Nonlinear Econometric Models," Econometrica, Econometric Society, vol. 43(4), pages 727-738, July.
    14. 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.
    15. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    16. White, Alexander, 2013. "Search engines: Left side quality versus right side profits," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 690-701.
    17. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    18. Kenneth C. Wilbur & Yi Zhu, 2009. "Click Fraud," Marketing Science, INFORMS, vol. 28(2), pages 293-308, 03-04.
    19. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    20. Varian, Hal R., 2007. "Position auctions," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1163-1178, December.
    21. Lahiri, Kajal & Schmidt, Peter, 1978. "On the Estimation of Triangular Structural Systems," Econometrica, Econometric Society, vol. 46(5), pages 1217-1221, September.
    22. Bresnahan, Timothy F & Reiss, Peter C, 1991. "Entry and Competition in Concentrated Markets," Journal of Political Economy, University of Chicago Press, vol. 99(5), pages 977-1009, October.
    23. Janiszewski, Chris, 1993. "Preattentive Mere Exposure Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 376-392, December.
    24. Nelson, Forrest & Olson, Lawrence, 1978. "Specification and Estimation of a Simultaneous-Equation Model with Limited Dependent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(3), pages 695-709, October.
    25. Brynjolfsson, Erik & Dick, Astrid Andrea & Smith, Michael D., 2004. "Search and Product Differentiation at an Internet Shopbot," Working papers 4441-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    26. Andrés Musalem & Eric T. Bradlow & Jagmohan S. Raju, 2009. "Bayesian estimation of random‐coefficients choice models using aggregate data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 490-516, April.
    27. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    2. Ashish Agarwal & Kartik Hosanagar & Michael D. Smith, 2015. "Do Organic Results Help or Hurt Sponsored Search Performance?," Information Systems Research, INFORMS, vol. 26(4), pages 695-713, December.
    3. Xiaoquan (Michael) Zhang & Juan Feng, 2011. "Cyclical Bid Adjustments in Search-Engine Advertising," Management Science, INFORMS, vol. 57(9), pages 1703-1719, February.
    4. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    5. Sha Yang & Shijie Lu & Xianghua Lu, 2014. "Modeling Competition and Its Impact on Paid-Search Advertising," Marketing Science, INFORMS, vol. 33(1), pages 134-153, January.
    6. Kinshuk Jerath & Liye Ma & Young-Hoon Park & Kannan Srinivasan, 2011. "A "Position Paradox" in Sponsored Search Auctions," Marketing Science, INFORMS, vol. 30(4), pages 612-627, July.
    7. Yu (Jeffrey) Hu & Jiwoong Shin & Zhulei Tang, 2016. "Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action," Management Science, INFORMS, vol. 62(7), pages 2022-2038, July.
    8. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    9. Ashish Agarwal & Tridas Mukhopadhyay, 2016. "The Impact of Competing Ads on Click Performance in Sponsored Search," Information Systems Research, INFORMS, vol. 27(3), pages 538-557.
    10. Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
    11. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    12. Anindya Ghose & Sha Yang, 2007. "An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets," Working Papers 07-35, NET Institute, revised Sep 2007.
    13. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    14. Burguet, Roberto & Caminal, Ramon & Ellman, Matthew, 2015. "In Google we trust?," International Journal of Industrial Organization, Elsevier, vol. 39(C), pages 44-55.
    15. Alex Kim & Subramanian Balachander & Karthik Kannan, 2012. "On the optimal number of advertising slots in a generalized second-price auction," Marketing Letters, Springer, vol. 23(3), pages 851-868, September.
    16. Kenneth C. Wilbur & Yi Zhu, 2009. "Click Fraud," Marketing Science, INFORMS, vol. 28(2), pages 293-308, 03-04.
    17. Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
    18. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2016. "Search Engine Optimization: What Drives Organic Traffic to Retail Sites?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(1), pages 6-31, March.
    19. Peitz, Martin & Reisinger, Markus, 2014. "The Economics of Internet Media," Working Papers 14-23, University of Mannheim, Department of Economics.
    20. Abou Nabout, Nadia & Skiera, Bernd, 2012. "Return on Quality Improvements in Search Engine Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(3), pages 141-154.

    Corrections

    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:29:y:2010:i:4:p:602-623. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.