International Journal of Computer
Trends and Technology

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Volume 4 | Issue 4 | Year 2013 | Article Id. IJCTT-V4I4P128 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I4P128

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

Chun Fu Lin , Yu Hsin Hung , and Ray I Chang

Citation :

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), vol. 4, no. 4, pp. 579-584, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I4P128

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.

Keywords

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

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 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 communication: Gender 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. 2965, 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.  
[8] De Nisco, and G. Warnaby, “Urban design and tenant variety influences on consumers' emotions and approach behavior,” Journal of Business Research, In Press, 2012.
[9] Brunner-Sperdin, M. Peters, and Strobl, A , “It is all about the emotional state: Managing tourists’ experiences,” International Journal of Hospitality Management, vol. 31, no. 1, pp. 23-30, 2012
[10] R. Kittler, and W. Wang, “Data mining for yield improvements. Proceedings from MASM, 2000. 
[11] M. Suman, T. Anuradha, and K. M. Veena, “Direct marketing with the application of data mining,” Journal of Information Engineering and Applications, vol. 1, no. 6, pp. 1-4, 2012.  
[12] U. Marjanović, D. Graĉanin, and B. Lalić, “Web stores in Serbia: recommendation for e-business strategy implementation,” Information Technology in Management, 968, 2012. 
[13] Warf, “Global E-Commerce,” In Global Geographies of the Internet, pp. 77-113, Springer Netherlands, 2013.
[14] J. R. Méndez, F. Fdez-Riverola, E. L. Iglesias, F. Díaz, and J. M. Corchado, “Tracking concept drift at feature selection stage in SpamHunting: An anti-spam instance-based reasoning system,” Lecture Notes in Computer Science, 4106, pp.504-518, 2006.