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Introduction

Advertising research has focused on three substantive areas: sales response to advertising, optimal advertising policy (constant spending or pulsing), competitive reactions and over-time effects. The research has employed econometric, time-serioes, optimization and game theoretic analytical techniques to address the issues. The advent of enormous amounts of scanner panel and internet data has led to some fruitful modeling at the individual household level. Contributions in each one of the three areas are discussed. A thorough review of optimal control advertising models is given in Feichtinger, Hartl, and Sethi (1994). Mathematical programming also has been a useful technology. Since some early successful applications of this technology for media planning, the progress has been limited because of measurement problems relating to advertising response function (Little and Lodish 1969). Advances in research, however, provide reasons for optimism in identifying the response...

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References

  • Bass, F. M. (1969). A simultaneous equation regression study of advertising and sales of cigarettes. Journal of Marketing Research, 6, 291–300.

    Article  Google Scholar 

  • Bass, F. M., & Clarke, D. G. (1972). Testing distributed lag models of advertising effect. Journal of Marketing Research, 9, 298–308.

    Article  Google Scholar 

  • Bass, F. M., & Leone, R. P. (1986). Estimating micro relationships from macro data: A comparative study of two approximations of the brand loyal model under temporal aggregation. Journal of Marketing Research, 23, 291–297.

    Article  Google Scholar 

  • Bass, F. M., & Parsons, L. J. (1969). Simultaneous-equation regression analysis of sales and advertising. Applied Economics, 1, 103–124.

    Article  Google Scholar 

  • Blattberg, R. C., & Jeuland, A. P. (1981). A micro-modeling approach to investigate the advertising-sales relationship. Management Science, 27, 988–1005.

    Article  Google Scholar 

  • Bourguignon, F., & Sethi, S. P. (1981). Dynamic optimal pricing and (possibly) advertising in the face of various kinds of potential entrants. Journal of Economic Dynamics and Control, 3, 119–140.

    Article  Google Scholar 

  • Bronnenberg, B. J. (1998). Advertising frequency decisions in a discrete markov process under a budget constraint. Journal of Marketing Research, 35, 399–406.

    Article  Google Scholar 

  • Dekimpe, M. G., & Hanssens, D. M. (1995a). The persistence of marketing effects on sales. Marketing Science, 14(Winter), 1–21.

    Article  Google Scholar 

  • Dekimpe, M. G., & Hanssens, D. M. (1995b). Empirical generalization about market evolution and stationarity. Marketing Science, 14, 109–121.

    Article  Google Scholar 

  • Eastlack, J. O., & Rao, A. (1986). Modeling response to advertising and pricing changes for V8 cocktail vegetable juice. Marketing Science, 5, 245–259.

    Article  Google Scholar 

  • Erickson, G. M. (1991). Empirical analysis of closed-loop duopoly advertising strategies. Working Paper. Seattle: University of Washington.

    Google Scholar 

  • Feichtinger, G., Hartl, R. F., & Sethi, S. P. (1994). Dynamic optimal control models in advertising: Recent developments. Management Science, 40, 195–226.

    Article  Google Scholar 

  • Feinberg, F. (1992). Pulsing policies for aggregate advertising models. Marketing Science, 11, 221–234.

    Article  Google Scholar 

  • Hahn, M., & Hyun, J. S. (1990). Advertising cost interpretations and the optimality of pulsing. Management Science, 37, 157–169.

    Article  Google Scholar 

  • Hanssens, D.M. (2011). What is known about measuring (forecasting & improving) the long-term impact of advertising. Marketing Accountability Standards Board, Practitioner Paper 2011-01.

    Google Scholar 

  • Jedidi, K., Mela, C. F., & Gupta, S. (1999). Managing advertising and promotion for long-run profitability. Marketing Science, 18, 1–22.

    Article  Google Scholar 

  • Koyck, L. M. (1954). Distributed lags and investment analysis. Amsterdam: North Holland.

    Google Scholar 

  • Leone, R. P. (1995). Generalizing what is known of temporal aggregation and advertising carryover. Marketing Science, 14(3), G141–150.

    Article  Google Scholar 

  • Little, J. D. C. (1979). Aggregate advertising models, the state of the art. Operations Research, 27, 629–667.

    Article  Google Scholar 

  • Little, J. D. C., & Lodish, L. M. (1969). A media planning calculus. Operations Research, 17, 1–35.

    Article  Google Scholar 

  • Lodish, L. M., Abraham, M., Kalmenson, S., Livelsberger, J., Lubetkin, B., Richardson, B., et al. (1995). How TV advertising works: A meta-analysis of 389 real world split cable TV advertising experiments. Journal of Marketing Research, 32(May), 125–39.

    Article  Google Scholar 

  • Mahajan, V., & Muller, E. (1986). Advertising pulsing policies for generating awareness for new products. Marketing Science, 5, 89–106.

    Article  Google Scholar 

  • Naik, P. A., Mantrala, M. K., & Sawyer, A. F. (1998). Planning media schedules in the presence of dynamic advertising quality. Marketing Science, 17, 1–35.

    Article  Google Scholar 

  • Nerlove, M., & Arrow, K. (1962). Optimal advertising policy under dynamic conditions. Economica, 29, 129–142.

    Article  Google Scholar 

  • Nijs, V., Dekimpe, M. G., Steenkamp, J.-B., & Hanssens, D. M. (2001). The category-demand effects of price promotions. Marketing Science, 20(1), 1–22.

    Article  Google Scholar 

  • Pauwels, K. (2004). How dynamic consumer response, competitor response, company support, and company inertia shape long-term marketing effectiveness. Marketing Science, 23(4), 596–610.

    Article  Google Scholar 

  • Pauwels, K., & Hanssens, D. M. (2007). Performance regimes and marketing policy shifts. Marketing Science, 26(3), 293–311.

    Article  Google Scholar 

  • Pedrick, J. H., & Zufryden, F. S. (1991). Evaluating the impact of advertising media plans: A model of consumer purchase dynamics using single-source data. Marketing Science, 10, 111–130.

    Article  Google Scholar 

  • Rao, R. C. (1984). Advertising decisions in oligopoly: An industry equilibrium analysis. Optimal Control Applications and Methods, 5, 331–344.

    Article  Google Scholar 

  • Rao, R. C. (1986). Estimating continuous time advertising-sales models. Marketing Science, 5, 125–142.

    Article  Google Scholar 

  • Russell, G. J. (1988). Recovering measures of advertising carryover from aggregate data: The role of the firm’s decision behavior. Marketing Science, 7(Summer), 252–70.

    Article  Google Scholar 

  • Rust, R. T., & Eechambadi, N. (1989). Scheduling network television programs: A heuristic audience flow approach to maximizing audience sharel. Journal of Advertising, 18(2), 11–18.

    Article  Google Scholar 

  • Sasieni, M. W. (1971). Optimal advertising expenditures. Management Science, 18, 64–72.

    Article  Google Scholar 

  • Sethi, S. P. (1973). Optimal control of the Vidale-Wolfe advertising model. Operations Research, 21, 998–1013.

    Article  Google Scholar 

  • Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-set metrics in market response models: An integrative approach. Journal of Marketing Research, 47(August), 672–684.

    Article  Google Scholar 

  • Steenkamp, J.-B. E. M., Nijs, V. R., Hanssens, D. M., & Dekimpe, M. G. (2005). Competitive reactions and the cross-sales effects of advertising and promotion. Marketing Science, 24, 35–54.

    Article  Google Scholar 

  • Tellis, G. J., & Ambler, T. (Eds.). (2007). The SAGE handbook of advertising. London: Sage Publications.

    Google Scholar 

  • Vidale, M. L., & Wolfe, H. B. (1957). An operations research study of sales response to advertising. Operations Research, 5, 370–381.

    Article  Google Scholar 

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Kalyanaram, G., Bass, F.M., Hanssens, D.M. (2013). Advertising. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_17

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  • DOI: https://doi.org/10.1007/978-1-4419-1153-7_17

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