Classifying the Customers of Telecommunication Company in order to Identify Profitable Customers Based on Their First Transaction, Using Decision Tree: A Case Study of System 780
الموضوعات :
Mohammad Velayati
1
,
Mohammadreza Shahriari
2
,
Farhad Hosseinzadeh Lotfi
3
1 - Department of Management , UAE Branch, Islamic Azad University, Dubai, United Arab Emirates.
2 - Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - Faculty of Basic Sciences, Science & Research Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: Customer Relationship Management (CRM), Segmentation, Decision tree, Telecom,
ملخص المقالة :
Effective knowledge and awareness of customers require the market segmentation, through which the customers who have the same needs and purchasing patterns as well as the same response to marketing plans are identified. The selection of a proper variable is a requirement, among other, for a successful market segmentation. In today' world, on one hand, the consumers are bombarded with new goods and new services, and on the other hand, they face the varying qualities of the goods and services. Consequently, such uncertainties will lead to more vague decisions and cumulative data. The timely and accurate analysis of these cumulative data can bring about competitive advantages to the enterprises. Furthermore, thanks to new technology and global competition, the majority of organizations have focused on Customer Relationship Management (CRM), with the goal of better serving the customers. The customer relationship planning entails the facilitation and creation of interfaces related to market segmentation, which is considered as a requirement for predicting behavior of the prospective customers in the future. Market segmentation refers to the process of dividing the customers into some segments based on their common characteristics while different groups have the least similarity to each other. This is followed by the formulation of plans for new product production, advertisement and marketing in accordance with the characteristics of each group of customers. Current study aims at identifying the profitable customers of a telecom System, based on their first transaction, using binary tree. The customers of System 780 participated in this case study. The dependent variable and independent variable of the study were identified through mining the data of customers, registered in the databases of System 780. The results showed the acceptable calculation error in distinguishing the profitable customers from other customers.
Chan, C.C., (200), intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert systems with applications. 2008 May 31; 34(4):2754-62.
Chang, P., Lai C., (2005), A hybrid system combining self-organizing maps with case-based reasoning in wholesaler's new-release book forecasting. Expert Systems with Applications. Jul 31; 29(1):183-92.
Han, J., Kamber, M., (2006), Data Mining: Concepts and Techniques (Second ed), Morgan Kaufmann Publishers, pp. 285–464.
Hosseini, M.H.,.(2003), the investigation of the effect of customer's satisfaction, trust in trademark and the value of trademark on the customers' loyalty and attitude: A Case Study of Bank Refah, Business Studies, Vol. 88,PP 42-51.
Hsieh, N., (2004), An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications. Nov 30; 27(4):623-33.
Hung, C., Tsai, C., (2008), “Market Segmentation Based on Hierarchical Self-Organization Map for Market of Multimedia on Demand”. Expert System with Applications, Vol. 34, pp. 780-787.
Hwang, H., Jung, T., Suh. E.. (2004), An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications. Feb 29; 26(2):181-8.
Jonker, J., Piersma, N., Van den Poel, D., (2004), Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Systems with Applications. Aug 31; 27(2):159-68.
Kim, K., Ahn, H., (2008), A recommender system using GA K-means clustering in an online shopping market. Expert systems with applications. Feb 29; 34(2):1200-9.
Khajvand, M., Zolfaghar K., Ashoori, S., Alizadeh, S., (2011), “Estimating Customer Lifetime Value Based On RFM Analysis of Customer Purchase Behavior: Case Study, Procedia Computer Science”, Vol. 3, pp.57-63.
Khajvand, M., Tarokh, M., (2011), "Estimating customer future value of different customer segments based on adapted RFM model in retail banking context." Procedia Computer Science 3: 1327-1332.
Kaufman, L., Rousseeuw, P., (1990), Finding Groups in Data. An Introduction to Cluster Analysis. Wiley-Interscience.
Kerkaus E., C. Spathis, Y., Manolopoulos, Y.,(2014), Data Mining Techniques for the Detection of Fraudulent Financial Statements, Expert Systems with Applications, Vol.32,
Kim, S., Jung, T., Suh, E., Hwang, H., (2006), Customer segmentation and strategy development based on customer lifetime value: A case study. Expert systems with applications. Jul 31; 31(1):101-7.
Lee, S., Suh, Y., Kim J., Lee, K., (2004), A cross-national market segmentation of online game industry using SOM. Expert systems with applications. Nov 30; 27(4):559-70.
Makhtar, M., S. Nafis, M., Mohamed, K., Awang, M., Rahman, A., (2017), "Churn classification model for local telecommunication company based on rough set theory." Journal of Fundamental and Applied Sciences 9, no. 6, 854-868.
McCarty, J., Hastak, M., (2007), Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research. Jun 30; 60(6):656-62.
Ngai, E., Yong, Y., Yijun, C., Xin, S., (2010), The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of literature; Decision Support Systems, vol. 50(3), pp. 559-569.
Phua C., V. Lee, K., Gayler, R., (2004), A Comprehensive Survey of Data Mining-based Fraud Detection research, Clayton School of Information Technology, Monash University.
Sheu, J., Su, Y., Chu, K., (2009), Segmenting online game customers–The perspective of experiential marketing. Expert systems with applications. May 31; 36(4):8487-95.
Stone, M., Woodcock, N., Wilson, M., (199), Managing the change from marketing planning to customer relationship management. Long Range Planning. Oct 1; 29(5):675-83.
Zhang, D., and Zhou, L.,(2004), Discovering Golden Nuggets: Data Mining in Financial Application, IEEE Transactions on Systems, Man and Cybernetics, Vol. 34(4), pp.513-522.
