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)


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.


Artificial neural networks Consumer’s preferences Backpropagation learning 


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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

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