The purpose of this paper is to develop and implement an approach for solving the optimization problem in the retailers goods market and different consumers groups based on a hybrid model of intelligent data analysis. It is revealed that for each consumer type product information array formed consisting of the factors values that affect the company revenue. An example of the optimal prices hybrid model synthesis in the product (beer) market and grocery stores network were considered. Solving the problem of optimizing product prices by consumer groups was implemented in two stages: first constructed interpolation function of consumer demand each group, and then solve the problem of optimizing implicitly given function. Demand function obtained by artificial neural networks.
Keywords: pricing, optimization, intelligent data analysis, artificial neural network, hybrid model.
- Cherep, A. V. (2011), Pricing as a Basis for the Enterprises Efficiency and Improving Welfare Factor. Zaporizhskyi natsionalnyi universytet. (ukrainian).
- Pavlenko, A. F. (2004), Marketing Pricing Policy. KNEU. (ukrainian).
- Zhehus, O .V. (2013), Theory and Practice of Pricing in the Marketing System. KhDUKhT. (ukrainian).
- Kryvoshyia, O. (2010), “Practices of Pricing, Classification and Examples of Use”, Ekonomichnyi analiz, 7, pp. 86–90. (ukrainian).
- Kudlai, V. H. (2006), “The Marketing Approach in Pricing”, Ekonomika. Finansy. Pravo, 4. (ukrainian).
- Mazur, O. Ye. (2010), “Classification Pricing Factors and Methods of Analysis”, Rehionalna ekonomika, 2 (56), pp. 55–62. (ukrainian).
- Shkvarchuk, L. (2005), “Modern Problems of Methodology for Setting Prices”, Formuvannia rynkovoi ekonomiky v Ukraini, pp. 167–173. (ukrainian).
- Matviichuk, A. V. (2011), Artificial Intelligence in the Economy: Neural Networks, fuzzy logic. KNEU. (ukrainian).
- Zghurovskyi, M. Z. (2013), Foundations of Computational Intelligence. Naukova dumka. (russian).
- http://www.irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe?Z21ID=&I21DBN=EC&P21DBN=EC&S21STN= 1&S21REF=10&S21FMT=fullw&C21COM=S&S21CNR=20&S21P01=3&S21P02=0&S21P03=A=&S21COLORTERMS=0& S21STR=%D0%9A%D0%B8%D0%B7%D0%B8%D0%BC,%20%D0%9D%D0%B8%D0%BA%D0%BE%D0%BB%D0%B0 %D0%B9%20%D0%90%D0%BB%D0%B5%D0%BA%D1%81%D0%B0%D0%BD%D0%B4%D1%80%D0%BE%D0%B2% D0%B8%D1%87Kyzym, N. A. (2006), Neural Networks: Theory and Practice. INZhEK. (russian).
- Subbotin, S. O. (2009), Noniterative, Evolutionary and Multi-agent Methods of Fuzzy Logic Synthesis and Neural Network Models. ZNTU. (ukrainian).
- Klebanova, T. S. (2011), Fuzzy Logic and Neural Networks in Enterprise Management. INZhEK. (ukrainian).
- Lysenko, Yu. H. (2012), Fuzzy Models and Neural Networks in the Analysis and Management of Economic Entities. Yuho-Vostok. (russian).
- Ngai, E. W. T., Li Xiu and Chau D. C. K. (2009), “Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification”, Expert Systems with Applications, 36, pp 2592–2622. (english). (DOI: 10.1016/j.eswa.2008.02.021).
- Ogut A., Kocabacak A.,Demirsel M. T. (2008), “The Impact of Data Mining on the Managerial Decision-Making Process: A Strategic Approach”, The Journal of American Academy of Business, Cambridge, 1(14), pp. 137–143. (english).
- Chiu, S. (2011), Data Mining and Market Intelligence for Optimal Marketing Returns. Butterworth- Heinemann, 296 p. (english).
- Amosenok, E. P. (2014), System Modeling and Analysis of Meso- and Micro-objects. IEOPP SO RAN. (russian).
- Williams, G. J., Simoff, S. J. (2006), Data Mining: Theory, Methodology, Techniques, and Applications. Springer Verlag. (english).
- Fedorenko, I. K. and Cherniak, O. I. (2007), Operations Research in Economics. Znannia. (english).
- Imanov, K. D. (2012), “Fuzzy Model Quality Assessment of the Social System”, Neironechitki tekhnolohii modeliuvannia v ekonomitsi, 1, pp. 142–160. (russian).