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Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction

Author

Listed:
  • Mehdi Neshat

    (Department of Computer Science, College of Software Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran)

  • Ali Akbar Pourahmad

    (Department of Information and Library Science, College of Human Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran)

  • Mohammad Reza Hasani

    (Department of Information and Library Science, College of Human Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran)

Abstract

Nowadays, in order to succeed in business and presence in the world markets, it is essential to outperform the competitors to get bigger market share. To get customers satisfaction of products is the first stage of success in business. Studying the different factors involved in increasing the level of customer's satisfaction and researching in this field has caused development in several companies. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) and a fuzzy inference system (FIS) are designed for marketing mix model. By using the P4 principle (price, product, place, promotion) and by combining it with the marketing experts' knowledge, good results were achieved using ANFIS. This system as an advisor with high accuracy can reduce the human errors and play a significant role in decision making by corporate managers. The results of two systems were compared and it was seen that ANFIS had a better performance than FIS with mean accuracies of 98.6% and 87.25%, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:jikmxx:v:15:y:2016:i:04:n:s0219649216500374
    DOI: 10.1142/S0219649216500374
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    References listed on IDEAS

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    1. Shapiro, Arnold F., 2002. "The merging of neural networks, fuzzy logic, and genetic algorithms," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 115-131, August.
    2. Arinze, B & Burton, J, 1992. "A simulation model for industrial marketing," Omega, Elsevier, vol. 20(3), pages 323-335, May.
    3. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
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