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An aggregate advertising response model based on consumer population dynamics

Author

Listed:
  • Menghan Wang
  • Qinglong Gou
  • Chunxu Wu
  • Liang Liang

Abstract

Aggregate advertising models are functions reflecting the relationship between product sales and advertising expenditure for a market as a whole. In this paper, we proposed a new advertising response model based on consumer population dynamics. Considering that the population dynamics is one of the basic characteristics of consumers, we try to apply it in describing the effects of advertising. In detail, a two-level framework advertising response model is introduced in this paper, in which the general Lotka-Volterra model is used to describe population dynamics among consumers and another increasing function is utilised to reflect the advertisement's effect on the intrinsic sales growth rate of a product or a brand. Mathematic analysis shows that the new advertising model has more advantages than other classic models such as Vidale-Wolfe model, Nerlove-Arrow model, Lanchester model and their modifications. Also, an early-warning marketing mechanism is introduced as an application of the proposed model.

Suggested Citation

  • Menghan Wang & Qinglong Gou & Chunxu Wu & Liang Liang, 2013. "An aggregate advertising response model based on consumer population dynamics," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 5(1), pages 22-38.
    • Handle: RePEc:ids:injams:v:5:y:2013:i:1:p:22-38
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    Citations

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    Cited by:

    1. Sweilam, Nasser H. & Abou Hasan, Muner M. & Baleanu, Dumitru, 2017. "New studies for general fractional financial models of awareness and trial advertising decisions," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 772-784.
    2. Benito Chen-Charpentier & Gilberto González-Parra & Abraham J. Arenas, 2016. "Fractional Order Financial Models for Awareness and Trial Advertising Decisions," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 555-568, December.

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