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Using genetic algorithms to assess the impact of pricing activity timing

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  • Klemz, Bruce R.

Abstract

Current methods of assessing competitive structure from aggregate sales data, such as the estimating of marketing mix elasticities, offer insight into the magnitude and direction of competitive actions. However, this level of analysis offers no insight into the impact that the timing of such mix activity has on one's market share. In this research, I illustrate a method of assessing market structure by utilizing the timing of competitive mix activity. Specifically, using a genetic algorithm approach, I estimate marketing mix timing rules for a consumer product market. This research illustrates the ability of these marketing mix timing rules to assess the impact that the timing of competitive mix activity has on one's market share. In addition, I show that these marketing mix timing rules offer strategic insight that can complement existing methods of assessing competitors using aggregate sales data, such as elasticity analysis, thereby improving the brand manager's understanding of the competitive structure of the market.

Suggested Citation

  • Klemz, Bruce R., 1999. "Using genetic algorithms to assess the impact of pricing activity timing," Omega, Elsevier, vol. 27(3), pages 363-372, June.
    • Handle: RePEc:eee:jomega:v:27:y:1999:i:3:p:363-372
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    1. Gregory S. Carpenter & Lee G. Cooper & Dominique M. Hanssens & David F. Midgley, 1988. "Modeling Asymmetric Competition," Marketing Science, INFORMS, vol. 7(4), pages 393-412.
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    6. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
    7. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
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    Cited by:

    1. Gruca, Thomas S. & Klemz, Bruce R., 2003. "Optimal new product positioning: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 621-633, May.

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