Mining Customer Behavior Knowledge to Develop Analytical Expert System for Beverage Marketing

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - April Issue 2013 by IJCTT Journal
Volume-4 Issue-4                           
Year of Publication : 2013
Authors :Chun Fu Lin , Yu Hsin Hung , and Ray I Chang

MLA

Chun Fu Lin , Yu Hsin Hung , and Ray I Chang"Mining Customer Behavior Knowledge to Develop Analytical Expert System for Beverage Marketing "International Journal of Computer Trends and Technology (IJCTT),V4(4):579-584 April Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: -Consumer relationship management (CRM) requires detailed information and business knowledge for successful adoption. Data mining techniques are widely used in business administration, the financial industry, and marketing. Mining techniques provide decision administration reference for enterprises by integrating useful information and discovering new information from different perspectives. In this study, we applied data mining technique and statistics and utilized questionnaires in CRM to analyze customer behavior. The Chinese tea market is famous worldwide, customizing the tea service is a special trend in chain stores, and customer behavior analysis is essential for the tea market. This study aims to develop a customer behavior analysis expert system (CBAES) in which a decision tree is used to identify relevant knowledge and personalize merchandise based on association rule framework of consumer behavior analysis in chain store beverage marketing. Identifying consumers’ preferences and providing optimal purchase strategy using this approach is a helpful characteristic of customers and facilitates marketing strategy development.

 

References-
[1] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Journal of Decision Support Systems, vol. 50, no. 3, pp. 559-569, 2011.
[2] M. Thelwall, D. Wilkinson, and S. Uppal, “Data mining emotion in social network communication: Gender differences in MySpace,” Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190-199, 2010.
[3] E. J. McCarthy, “Basic marketing: a managerial approach,” RD Irwin, 1978.
[4] H. Tohidi, “CRM technology as a strengthening factor to business outcomes,” AWERProcedia Information Technology and Computer Science, 1, 2012.
[5] X.Chen, and D. Simchi-Levi, “Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case,” Operations Research, vol. 52, no. 6, pp. 887-896, 2004.
[6] Taudes, and C. Rudloff, “Integrating inventory control and a price change in the presence of reference price effects: a two-period model,” Mathematical Methods of Operations Research, vol. 75, no. 1, pp. 29- 65, 2012.
[7] T. Allard, B. Babin, J. C. Chebat, and M. Crispo, “Reinventing the branch: An empirical assessment of banking strategies to environmental differentiation,” Journal of Retailing and Consumer Services, vol. 16, no. 6, pp. 442-450, 2009.

Keywords — Consumer relationship management, expert system, decision tree algorithm, marketing

Additional information