IDEAS home Printed from
   My bibliography  Save this article

Building Brand Awareness in Dynamic Oligopoly Markets


  • Prasad A. Naik

    () (Department of Marketing, University of California, Davis, Davis, California 95616)

  • Ashutosh Prasad

    () (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Suresh P. Sethi

    () (School of Management, University of Texas at Dallas, Richardson, Texas 75083)


Companies spend hundreds of millions of dollars annually on advertising to build and maintain awareness for their brands in competitive markets. However, awareness formation models in the marketing literature ignore the role of competition. Consequently, we lack both the empirical knowledge and normative understanding of building brand awareness in dynamic oligopoly markets. To address this gap, we propose an N-brand awareness formation model, design an extended Kalman filter to estimate the proposed model using market data for five car brands over time, and derive the optimal closed-loop Nash equilibrium strategies for every brand. The empirical results furnish strong support for the proposed model in terms of both goodness-of-fit in the estimation sample and cross-validation in the out-of-sample data. In addition, the estimation method offers managers a systematic way to estimate ad effectiveness and forecast awareness levels for their particular brands as well as competitors' brands. Finally, the normative analysis reveals an inverse allocation principle that suggests--contrary to the proportional-to-sales or competitive parity heuristics--that large (small) brands should invest in advertising proportionally less (more) than small (large) brands.

Suggested Citation

  • Prasad A. Naik & Ashutosh Prasad & Suresh P. Sethi, 2008. "Building Brand Awareness in Dynamic Oligopoly Markets," Management Science, INFORMS, vol. 54(1), pages 129-138, January.
  • Handle: RePEc:inm:ormnsc:v:54:y:2008:i:1:p:129-138

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    2. Naik, Prasad A. & Shi, Peide & Tsai, Chih-Ling, 2007. "Extending the Akaike Information Criterion to Mixture Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 244-254, March.
    3. Koen Pauwels, 2004. "How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness," Marketing Science, INFORMS, vol. 23(4), pages 596-610, June.
    4. Gary M. Erickson, 1992. "Empirical Analysis of Closed-Loop Duopoly Advertising Strategies," Management Science, INFORMS, vol. 38(12), pages 1732-1749, December.
    5. Alessandra Luati & Giorgio Tassinari, 2005. "Intervention analysis to identify significant exposures in pulsing advertising campaigns: an operative procedure," Computational Management Science, Springer, vol. 4(4), pages 295-308, November.
    6. Demetrios Vakratsas & Fred M. Feinberg & Frank M. Bass & Gurumurthy Kalyanaram, 2004. "The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds," Marketing Science, INFORMS, vol. 23(1), pages 109-119, April.
    7. Pradeep K. Chintagunta & Naufel J. Vilcassim, 1992. "An Empirical Investigation of Advertising Strategies in a Dynamic Duopoly," Management Science, INFORMS, vol. 38(9), pages 1230-1244, September.
    8. Chintagunta, Pradeep K & Jain, Dipak C, 1995. "Empirical Analysis of a Dynamic Duopoly Model of Competition," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 4(1), pages 109-131, Spring.
    9. Prasad A. Naik & Kalyan Raman & Russell S. Winer, 2005. "Planning Marketing-Mix Strategies in the Presence of Interaction Effects," Marketing Science, INFORMS, vol. 24(1), pages 25-34, June.
    10. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    11. Gila E. Fruchter & Shlomo Kalish, 1997. "Closed-Loop Advertising Strategies in a Duopoly," Management Science, INFORMS, vol. 43(1), pages 54-63, January.
    12. Fershtman, Chaim, 1984. "Goodwill and Market Shares in Oligopoly," Economica, London School of Economics and Political Science, vol. 51(23), pages 271-281, August.
    13. Joe A. Dodson, Jr. & Eitan Muller, 1978. "Models of New Product Diffusion Through Advertising and Word-of-Mouth," Management Science, INFORMS, vol. 24(15), pages 1568-1578, November.
    14. Gustav Feichtinger & Richard F. Hartl & Suresh P. Sethi, 1994. "Dynamic Optimal Control Models in Advertising: Recent Developments," Management Science, INFORMS, vol. 40(2), pages 195-226, February.
    15. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    16. Jinn-Tsair Teng & Gerald L. Thompson, 1983. "Oligopoly Models for Optimal Advertising When Production Costs Obey a Learning Curve," Management Science, INFORMS, vol. 29(9), pages 1087-1101, September.
    17. Sorger, Gerhard, 1989. "Competitive dynamic advertising : A modification of the Case game," Journal of Economic Dynamics and Control, Elsevier, vol. 13(1), pages 55-80, January.
    18. Vijay Mahajan & Eitan Muller & Subhash Sharma, 1984. "An Empirical Comparison of Awareness Forecasting Models of New Product Introduction," Marketing Science, INFORMS, vol. 3(3), pages 179-197.
    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. repec:eee:ijrema:v:34:y:2017:i:4:p:761-779 is not listed on IDEAS
    2. Guidolin, Mariangela & Guseo, Renato, 2015. "Technological change in the U.S. music industry: Within-product, cross-product and churn effects between competing blockbusters," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 35-46.
    3. Krishnamoorthy, Anand & Prasad, Ashutosh & Sethi, Suresh P., 2010. "Optimal pricing and advertising in a durable-good duopoly," European Journal of Operational Research, Elsevier, vol. 200(2), pages 486-497, January.
    4. Anshuman Chutani & Suresh Sethi, 2012. "Cooperative Advertising in a Dynamic Retail Market Oligopoly," Dynamic Games and Applications, Springer, vol. 2(4), pages 347-375, December.
    5. James, Waters, 2015. "Do vegetarian marketing campaigns promote a vegan diet?," MPRA Paper 66737, University Library of Munich, Germany.
    6. Crespo Cuaresma, Jesus & Stöckl, Matthias, 2012. "The Effect of Marketing Spending on Sales in the Premium Car Segment: New Evidence from Germany," Working Papers in Economics 2012-2, University of Salzburg.
    7. Huang, Jian & Leng, Mingming & Liang, Liping, 2012. "Recent developments in dynamic advertising research," European Journal of Operational Research, Elsevier, vol. 220(3), pages 591-609.
    8. Erickson, Gary M., 2009. "An oligopoly model of dynamic advertising competition," European Journal of Operational Research, Elsevier, vol. 197(1), pages 374-388, August.
    9. Olivier Rubel & Prasad A. Naik & Shuba Srinivasan, 2011. "Optimal Advertising When Envisioning a Product-Harm Crisis," Marketing Science, INFORMS, vol. 30(6), pages 1048-1065, November.
    10. Ashwin Aravindakshan & Prasad Naik, 2011. "How does awareness evolve when advertising stops? The role of memory," Marketing Letters, Springer, vol. 22(3), pages 315-326, September.
    11. Srinath Gopalakrishna & Jason Garrett & Murali K. Mantrala & Shrihari Sridhar, 2016. "Assessing sales contest effectiveness: the role of salesperson and sales district characteristics," Marketing Letters, Springer, vol. 27(3), pages 589-602, September.


    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:ormnsc:v:54:y:2008:i:1:p:129-138. 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: (Mirko Janc). 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.