Advertisement

Identifying Consumer’s Preferences Using Artificial Neural Network Techniques

  • Nikolaos F. Matsatsinis
  • Christos N. Hatzis
  • Andreas P. Samaras
Part of the Applied Optimization book series (APOP, volume 19)

Abstract

Many factors should be considered when bringing out a new product in the market. The product’s basic features including price, quality, package, promotion e.t.c. should be taken into account which is a very complicated process, considering the amount of data to be managed. In the recent years many efforts, based on expert systems and rules, turned out to be very effective. A methodology, expected to give a new perspective in the marketing decision support systems, is presented in this paper. An artificial neural network is designed so as to learn the relationships between the products’ features and the customers’ preferences. The necessary data for the neural network learning process are collected through a market survey. The neural network inputs are the consumer’s judgements concerning the products’ features. The system is trained to generate as output, the ordinance consumer preference for each product. Finally, examples of applied scenarios are illustrated aiming in the formation of the optimal penetration strategy.

Keywords

Artificial neural networks Consumer’s preferences Backpropagation learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Demuth, H. and Beale, M. (1992), Neural Network Toolbox, The MATHWORKS Inc.Google Scholar
  2. Fish, K.E., Barnes, J.H. and Aiken, M.W. (1995), “Artificial neural networks: a new methodology for industrial market segmentation”, Industrial Marketing Management 24/5,431–438.CrossRefGoogle Scholar
  3. Ghung, Y. and Fischer, G.W. (1995), “A neural algorithm for finding the shortest flow path for an automated guide vehicle system”, IEE Trans 27/6,773–783.CrossRefGoogle Scholar
  4. Hansen, J.V., McDonald, J.B. and Stice, J.D. (1992), “Artificial intelligence and generalized qualitative response models: An empirical test on two audit decision-making domains”, Decision Sciences 23/3,708–723.CrossRefGoogle Scholar
  5. Hawley, Delvin.D., Johnson, J.D. and Raina, D. (1990), “Artificial neural Systems: A new tool for financial decision making”, Financial Analyst Journal, November-December, 63–72.Google Scholar
  6. Hueter, G.J. (1993), “Neural networks automate inspections”, Quality 32/1,41–44.Google Scholar
  7. Klimasauskas, C.C (1991), “Applying neural networks”, PCAI, January-February, 30–33.Google Scholar
  8. Lenard, M.J., Alam, P. and Madey, G.R. (1995), “The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision”, Decision Sciences 26/2,209–227.CrossRefGoogle Scholar
  9. Markham, L.S. and Gagsdale, C.T., (1995), ‘“Combining neural networks and statistical predictions to solve the classification problem in discriminant analysis”, Decision Sciences 26/2,229–242.CrossRefGoogle Scholar
  10. Matsatsinis, N.F. and Siskos Y. (1998), “MARKEX: An intelligent decision support system for new product development decisions”, European Journal of Operational Research, (to appear)Google Scholar
  11. Medsker, L., Trippi, R.R. and Turban, E. (1996), “Neural network fundamentals for financial analyst”, Neural Networks in Finance and Investing. Using Artificial Intelligence to improve real-world performance, IRWIN Professional Publishing.Google Scholar
  12. Moon, Y.B. and Janowski, R. (1995), “A neural network approach for smoothing and categorizing noisy data”, Computers in Industry 26/1,23–39.CrossRefGoogle Scholar
  13. Obaidat, M.S. and Macchairrolo, D.T. (1994), “A multilayer neural network system for computer access security”, IEEE Trans. Systems, Man and Cybernetics 24/5, 806–813.CrossRefGoogle Scholar
  14. Proctor, R. (1992), “An expert system to aid in staff selection: a neural network approach”, Int. J Manpower 12/8, 267–276.Google Scholar
  15. Trippi, R.R. and Turban, E. (1996), Neural Networks in Finance and Investing. Using Artificial Intelligence to improve real-world performance, IRWIN Professional Publishing.Google Scholar
  16. Rogers, J, (1995), “Neural network user authentication”, AI Expert, 10,29–33.Google Scholar
  17. Salchenberger, L.M., Cinar, E.M. and Lash, N.A (1992), “Neural Networks: A new tool for predicting thrift failures”, Decision Sciences 23, 899–916.CrossRefGoogle Scholar
  18. Widrow, B. and Lehr, M. A (1990), “30 Years of Adaptive Neural Networks: Perceptron, Madaline and Backpropagation”, Proc. IEEE 78/9, September, 1415–1442.CrossRefGoogle Scholar
  19. Wilson, R.L.(1994), “A neural network approach to decision to alternative prioritization”, Decision Support Systems 11/5,431–447.CrossRefGoogle Scholar
  20. Wong, Bo K., Bodnovich, T.A and Y. Selvi (1997), “ Neural Network applications in business: A review and analysis of the literature (1988–95)”, Decision Support Systems 19,301–320.CrossRefGoogle Scholar
  21. Zopounidis, C., Doumpos, M. and Matsatsinis, N.F. (1997), “On the use of knowledge-based decision supportsystems in financial management: A survey”, Decision Support Systems 11, 259–277.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Nikolaos F. Matsatsinis
    • 1
  • Christos N. Hatzis
    • 1
  • Andreas P. Samaras
    • 1
  1. 1.Dept. of Production Engineering and Management, Decision Support Systems LaboratoryTechnical University of CreteChaniaGreece

Personalised recommendations