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A simulation model for industrial marketing

Author

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  • Arinze, B
  • Burton, J

Abstract

Developing an effective marketing mix is an important task for product planners seeking to gain competitive advantage in industrial markets. In these markets, product planning is made complex due to inadequate data sources, stochastic behavior, and the peculiar response of industrial markets to marketing instruments. By employing a simulation model as the heart of a marketing decision support system (MKDSS) it is possible to model the stochastic elements of the marketing mix, the interactions between marketing instruments, and competitive effects, to support marketing decision-making processes. This paper describes such a simulation model for aiding product planners in developing the marketing mix. The model utilizes Monte Carlo simulation in representing market dynamics, comparative marketing efforts, and competitive actions, in order to assess the effectiveness of combinations of marketing actions, that is, marketing policies. It is therefore viewed as a tool for improving decision-making effectiveness, and the basis of a marketing decision support system (MKDSS) for marketing managers. The methodology for applying the decision model is outlined, together with an illustrative example of its use, based on data from case study in a British firm.

Suggested Citation

  • Arinze, B & Burton, J, 1992. "A simulation model for industrial marketing," Omega, Elsevier, vol. 20(3), pages 323-335, May.
    • Handle: RePEc:eee:jomega:v:20:y:1992:i:3:p:323-335
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    Cited by:

    1. Mehdi Neshat & Ali Akbar Pourahmad & Mohammad Reza Hasani, 2016. "Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-21, December.

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