IDEAS home Printed from https://ideas.repec.org/a/wsi/jikmxx/v15y2016i04ns0219649216500374.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219649216500374
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219649216500374?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Arinze, B & Burton, J, 1992. "A simulation model for industrial marketing," Omega, Elsevier, vol. 20(3), pages 323-335, May.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    3. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
    4. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    5. Shapiro, Arnold F., 2004. "Fuzzy logic in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 399-424, October.
    6. Jing Li & Kuei-Ying Huang & Jionghua Jin & Jianjun Shi, 2008. "A survey on statistical methods for health care fraud detection," Health Care Management Science, Springer, vol. 11(3), pages 275-287, September.
    7. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    8. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    9. Stephan Birle & Mohamed Ahmed Hussein & Thomas Becker, 2016. "Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-11, July.
    10. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    11. Jonker, J.-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Belles-Sampera, Jaume & Merigó, José M. & Guillén, Montserrat & Santolino, Miguel, 2013. "The connection between distortion risk measures and ordered weighted averaging operators," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 411-420.
    13. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, vol. 2(2), pages 1-14, April.
    14. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    15. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    16. Nader Salari & Shamarina Shohaimi & Farid Najafi & Meenakshii Nallappan & Isthrinayagy Karishnarajah, 2014. "A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-50, November.
    17. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    18. Sancho Salcedo-Sanz & L. Carro-Calvo & Mercè Claramunt & Anna Castañer & Maite Marmol, 2013. "An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches," Working Papers XREAP2013-04, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2013.
    19. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
    20. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:jikmxx:v:15:y:2016:i:04:n:s0219649216500374. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/jikm/jikm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.