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A communication mix for an event planning: a linear quadratic approach

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  • Alessandra Buratto
  • Luca Grosset

Abstract

The communication mix is a relevant decision issue for an organization that plans the advertising campaign for a fixed future event. It is assumed that the objectives of the organization are to minimize the cost of the advertising campaign and to drive the final demand as close as possible to a target value. Two different advertising channels are available: the first affects deterministically the consumers’ demand, whereas the second presents some stochastic aspects which are out of decision-maker’s control. Some recent mathematical developments on the stochastic linear quadratic control problem allow to formulate and solve some interesting instances of the problem. A comparative analysis of the efficiency of deterministic and stochastic controls is done and the optimal feedback policies are discussed. The trade-off between efficiency and risk of an advertising channel is essential to understand the features of the optimal solutions. Copyright Physica-Verlag 2006

Suggested Citation

  • Alessandra Buratto & Luca Grosset, 2006. "A communication mix for an event planning: a linear quadratic approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(3), pages 247-259, September.
    • Handle: RePEc:spr:cejnor:v:14:y:2006:i:3:p:247-259
    DOI: 10.1007/s10100-006-0002-y
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    References listed on IDEAS

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    1. Luca Grosset & Bruno Viscolani, 2004. "Advertising for a new product introduction: A stochastic approach," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 149-167, June.
    2. 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.
    3. S. Jørgensen & G. Zaccour, 1999. "Equilibrium Pricing and Advertising Strategies in a Marketing Channel," Journal of Optimization Theory and Applications, Springer, vol. 102(1), pages 111-125, July.
    4. Alessandra Buratto & Bruno Viscolani, 2002. "New product introduction: goodwill, time and advertising cost," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 55(1), pages 55-68, March.
    5. Charles S. Tapiero, 1978. "Optimum Advertising and Goodwill under Uncertainty," Operations Research, INFORMS, vol. 26(3), pages 450-463, June.
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    Cited by:

    1. L. Grosset & B. Viscolani, 2010. "Advertising Events in a Competitive Framework," Journal of Optimization Theory and Applications, Springer, vol. 146(2), pages 375-385, August.
    2. Beltran-Royo, C. & Zhang, H. & Blanco, L.A. & Almagro, J., 2013. "Multistage multiproduct advertising budgeting," European Journal of Operational Research, Elsevier, vol. 225(1), pages 179-188.

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