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Optimal Duration of Magazine Promotions

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Author Info

  • Mercedes Esteban-Bravo

    ()

  • José Múgica

    ()

  • Jose Vidal-Sanz

    ()

Abstract

The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion events. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s11002-005-1675-z
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Bibliographic Info

Article provided by Springer in its journal Marketing Letters.

Volume (Year): 16 (2005)
Issue (Month): 2 (April)
Pages: 99-114

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Handle: RePEc:kap:mktlet:v:16:y:2005:i:2:p:99-114

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Web page: http://www.springerlink.com/link.asp?id=100312

Related research

Keywords: optimal duration of promotion events; Markovian process; dynamic programming;

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  1. 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.
  2. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
  3. Scott A. Neslin & Robert W. Shoemaker, 1983. "A Model for Evaluating the Profitability of Coupon Promotions," Marketing Science, INFORMS, vol. 2(4), pages 361-388.
  4. Vijay Mahajan & Eitan Muller, 1986. "Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 89-106.
  5. Fred M. Feinberg, 1992. "Pulsing Policies for Aggregate Advertising Models," Marketing Science, INFORMS, vol. 11(3), pages 221-234.
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