Skip to main content
Log in

An Empirical Model of Advertising Dynamics

  • Published:
Quantitative Marketing and Economics Aims and scope Submit manuscript

Abstract

This paper develops a model of dynamic advertising competition, and applies it to the problem of optimal advertising scheduling through time. In many industries we observe advertising “pulsing”, whereby firms systematically switch advertising on and off at a high-frequency. Hence, we observe periods of zero and non-zero advertising, as opposed to a steady level of positive advertising. Previous research has rationalized pulsing through two features of the sale response function: an S-shaped response to advertising, and long-run effects of current advertising on demand. Despite considerable evidence for advertising carry-over, existing evidence for non-convexities in the shape of the sales-response to advertising has been limited and, often, mixed. We show how both features can be included in a discrete choice based demand system and estimated using a simple partial maximum likelihood estimator. The demand estimates are then taken to the supply side, where we simulate the outcome of a dynamic game using the Markov perfect equilibrium (MPE) concept. Our objective is not to test for the specific game generating observed advertising levels. Rather, we wish to verify whether the use of pulsing (on and off) can be justified as an equilibrium advertising practice. We solve for the equilibrium using numerical dynamic programming methods. The flexibility provided by the numerical solution method allows us to improve on the existing literature, which typically considers only two competitors, and places strong restrictions on the demand models for which the supply side policies can be obtained. We estimate the demand model using data from the Frozen Entree product category. We find evidence for a threshold effect, which is qualitatively similar to the aforementioned S-shaped advertising response. We also show that the threshold is robust to functional form assumptions for the marginal impact of advertising on demand. Our estimates, which are obtained without imposing any supply side restrictions, imply that firms should indeed pulse in equilibrium. Predicted advertising in the MPE is higher, on average, than observed advertising. On average, the optimal advertising policies yield a moderate profit improvement over the profits under observed advertising.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ackerberg, D.A. (2001). “Empirically Distinguishing Informative and Prestige Effects of Advertising.” RAND Journal of Economics 32(2), 316–333.

    Google Scholar 

  • Assmus, G., J.U. Farley, and D.R. Lehmann. (1984). “How Advertising Affects Sales: Meta-Analysis of Econometric Results.” Journal of Marketing Research 21, 65–74.

    Google Scholar 

  • Baron, R. (2003). “Planning’s Top 5 Questions.” TelevisionWeek 22(12), 23–32.

    Google Scholar 

  • Benítez-Silva, H., G. Hall, G.J. Hitsch, G. Pauletto, and J. Rust. (2003). “A Comparison of Discrete and Parametric Approximation Methods for Continuous-State Dynamic Programming Problems.” manuscript.

  • Benkard, L. (2001). “A Dynamic Analysis of the Market for Wide-bodied Commercial Aircraft.” Review of Economic Studies (forthcoming).

  • Berry, S. (1994). “Estimating Discrete-Choice Models of Product Differentiation.” Rand Journal of Economics 25, 242–62.

    Google Scholar 

  • Berry, S., J. Levinsohn, and A. Pakes. (1995). “Automobile Prices in Market Equilibrium.” Econometrica 63(4), 841–890.

    Google Scholar 

  • Bronnenberg, B. (1998). “Advertising Frequency Decisions in a Discrete Markov Process Under a Budget Constraint.” Journal of Marketing Research 35, 399–406.

    Google Scholar 

  • Cannon, H.M., J.D. Leckenby, and A. Abernethy. (2002). “Beyond Effective Frequency: Evaluating Media Schedules Using Frequency Value Planning.” Journal of Advertising Research 42(6), 33–47.

    Google Scholar 

  • Chen, X. and T.G. Conley. (2001). “A New Semiparametric Spatial Model for Panel Time Series.” Journal of Econometrics 105, 59–83.

    Article  MathSciNet  Google Scholar 

  • Chintagunta, P.K., V. Kadiyali, and N.J. Vilcassim. (1999). “Investigating Dynamic Multi-firm Market Interactions in Price and Advertising”, Management Science 45(4), 499–518.

    Google Scholar 

  • Chintagunta, P.K., J. Dube, and K. Yong Goh. (2005). “Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models.” Management Science 51(2).

  • Clarke, D.G. (1976). “Econometric Measurement of the Duration of Advertising Effect on Sales.” Journal of Marketing Research 13, 345–357.

    Google Scholar 

  • Doraszelski, U. and S. Markovich. (2003). “Advertising Dynamics and Competitive Advantage.” Working Paper, Hoover Institution.

  • Doraszelski, U. and M. Satterthwaite. (2003). “Foundations of Markov-Perfect Industry Dynamics: Existence, Purification, and Multiplicity.” manuscript.

  • Ephron, E. (2002). “Median Scheduling and Carry-over Effects: Is Adstock a Useful TV Planning Tool?” Journal of Advertising Research 42(4), 66–71.

    Google Scholar 

  • Erdem, T. and M.P. Keane. (1996). “Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets.” Marketing Science 15, 1–20.

    Google Scholar 

  • Erickson, G. (1995). “Advertising Strategies in a Dynamic Oligopoly.” Journal of Marketing Research 32, 233–237.

    Google Scholar 

  • Erickson, G. (2002). Dynamic Models of Advertising Competition (International Series in Quantitative Marketing, 13), 2nd edition. Kluwer Academic Publishers.

  • Ericson, R. and A. Pakes. (1995). “Markov Perfect Industry Dynamics: A Framework for Empirical Work.” Review of Economic Studies 62(1), 53–82

    Google Scholar 

  • Feinberg, F.M. (1992). “Pulsing Policies for Aggregate Advertising Models.” Marketing Science 11(3), 221–.

    Google Scholar 

  • Hitsch, G.J. (2004). “An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty.” Marketing Science (forthcoming).

  • Jones, J.P. (1995). “Single-Source Research Begins to Fulfill its Promise.” Journal of Advertising Research 35(3), 9–16.

    Google Scholar 

  • Judd, K.L. (1998). Numerical Methods in Economics. MIT Press.

  • Kingman, M. (1977). “Admen See Pulsing as Way to Beat Soaring TV Time Costs.” Advertising Age 48(27), 27–29.

    Google Scholar 

  • Krugman, H.E. (1972). “Why Three Exposures May be Enough.” Journal of Advertising Research 12, 11–14.

    Google Scholar 

  • Leckenby, J.D. and J.D. Kim. (1994). “How Media Directors View Reach/Frequency Estimation: Now and a Decade Ago.” Journal of Advertising Research 34(5), 9–21.

    Google Scholar 

  • Leeflang, P.S.H. and D.R. Wittink. (1992). “Diagnosing Competitive Reactions Using (Aggregated) Scanner Data.” International Journal of Research in Marketing 9, 39–57.

    Article  Google Scholar 

  • Leeflang, P.S.H. and D.R. Wittink. (1996). “Competitive Reaction Versus Consumer Response: Do Managers Overreact?” International Journal of Research in Marketing 13, 103–119.

    Article  Google Scholar 

  • Lilien, G.L., P. Kotler, and K.S. Moorthy. (1992). Marketing Models. New Jersey: Prentice Hall.

    Google Scholar 

  • Mahajan, V. and E. Muller. (1986). “Advertising Pulsing Policies for Generating Awareness for New Products.” Marketing Science 5(2), 110–111.

    Google Scholar 

  • Maskin, E. and J. Tirole. (2001). “Markov Perfect Equilibrium.” Journal of Economic Theory 100(2), 191–219.

    Article  Google Scholar 

  • Montgomery, D.B., M.C. Moore, and J.E. Urbany. (2005). “Reasoning About Competitive Reactions.” Marketing Science 24(1), 138–149.Montgomery, D.B., M.C. Moore, and J.E. Urbany. (2005). “Reasoning About Competitive Reactions.” Marketing Science 24(1), 138–149.

    Article  Google Scholar 

  • Naik, P.A., M.K. Mantrala, and A.G. Sawyer. (1998). “Planning Media Schedules in the Presence of Dynamic Advertising Quality.” Marketing Science 17, 214–235.Montgomery, D.B., M.C. Moore, and J.E. Urbany. (2005). “Reasoning About Competitive Reactions.” Marketing Science 24(1), 138–149.

    Google Scholar 

  • Naples, M.J. (1979). Effective Frequency. New York: NY Association of National Advertisers.

    Google Scholar 

  • Nerlove, M. and K.J. Arrow. (1962). “Optimal Advertising Policy Under Dynamic Conditions.” Economica 29, 129–142.

    Google Scholar 

  • Nevo, A. (2001). “Measuring Market Power in the Ready-To-Eat Cereal Industry.” Econometrica 69(2), 307–342.

    Article  Google Scholar 

  • Pakes, A. and P. McGuire. (1994). “Computing Markov Perfect Nash Equilibrium: Numerical Implications of a Dynamic Differentiated Product Model.” Rand Journal of Economics 25(4), 555–589.

    Google Scholar 

  • Rao, A.G. and P.B. Miller. (1975). “Advertising/Sales Response Functions.” Journal of Advertising Research 15, 7–15.

    Google Scholar 

  • Rust, J. (1996). “Numerical Dynamic Programming in Economics.” In H.M. Amman, D.A. Kendrick, and J. Rust (eds.), Handbook of Computational Economics, Elsevier Science B.V., pp. 619–729.

  • Samuelson, L. and M.J. Roberts. (1988). “An Empirical Analysis of Dynamic Non-Price Competition in an Oligopolistic Industry.” Rand Journal of Economics 19, 200–219.

    Google Scholar 

  • Sasiensi, M.W. (1971). “Optimal Advertising Expenditures.” Management Science 18(4), 64–72.Sasiensi, M.W. (1971). “Optimal Advertising Expenditures.” Management Science 18(4), 64–72.

    Google Scholar 

  • Shugan, S. and S. Radas. (2003). “Selective Marketing Strategies: Implications of Response Thresholds and Capacity Constraints.” Working Paper, University of Florida.

  • Simon, J.L. (1969). “New Evidence for No Effect of Scale in Advertising.” Journal of Advertising Research 9, 38–41.

    Google Scholar 

  • Simon, H. (1982). “ADPULS: An Advertising Model with Wearout and Pulsation.” Journal of Marketing Research 19, 352–363.

    Google Scholar 

  • Slade, M.E. (1995). “Product Rivalry with Multiple Strategic Weapons: An Analysis of Price and Advertising Competition.” Journal of Economics and Management Strategy 4(3), 445–476.

    Article  Google Scholar 

  • Vakratsas, D., F.M. Feinberg, F.M. Bass, and G. Kalyanaram. (2004). “The Shape Of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds.” Marketing Science 23(1), 109–119.

    Article  Google Scholar 

  • Villas-Boas, M.J. (1993). “Predicting Advertising Pulsing Policies in an Oligopoly: A Model and Empirical Test.” Marketing Science 12, 88–102

    Google Scholar 

  • Villas-Boas, M. and R. Winer. (1999). “Endogeneity in Brand Choice Models.” Management Science 45(10), 1324–1338.

    Google Scholar 

  • White, H. (1982). “Maximum Likelihood Estimation of Misspecified Models.” Econometrica 50(1), 1–25.

    Google Scholar 

  • Wittink, D.R. (1979). “Exploring Territorial Differences in the Relationship between Marketing Variables.” Journal of Marketing Research 15, 145–155.

    Google Scholar 

  • Wooldridge, J.M. (2001). Econometric Analysis of Cross Section and Panel Data. Boston: MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jean-Pierre Dubé.

Additional information

JEL Classification: L11, L66, M30 M37 R12

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dubé, JP., Hitsch, G.J. & Manchanda, P. An Empirical Model of Advertising Dynamics. Quant Market Econ 3, 107–144 (2005). https://doi.org/10.1007/s11129-005-0334-2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11129-005-0334-2

Key Words

Navigation