Churn Forecast Based on Two-step Classification in Security Industry
Affiliation(s)
. School of Business, East China University of Science and Technology, Shanghai, China.
. School of Business, East China University of Science and Technology, Shanghai, China.
ABSTRACT
Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease caused by churn, security companies must find those customers who have the loss risk and make measures to maintain loyal customers. Now it is the question that how to find and analyze those customers. In this paper, a two-step classification method about churn Analysis is proposed and the problem of churn in security is analyzed.
Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease caused by churn, security companies must find those customers who have the loss risk and make measures to maintain loyal customers. Now it is the question that how to find and analyze those customers. In this paper, a two-step classification method about churn Analysis is proposed and the problem of churn in security is analyzed.
Cite this paper
nullY. Li, Z. Deng, Q. Qian and R. Xu, "Churn Forecast Based on Two-step Classification in Security Industry," Intelligent Information Management, Vol. 3 No. 4, 2011, pp. 160-165. doi: 10.4236/iim.2011.34019.
nullY. Li, Z. Deng, Q. Qian and R. Xu, "Churn Forecast Based on Two-step Classification in Security Industry," Intelligent Information Management, Vol. 3 No. 4, 2011, pp. 160-165. doi: 10.4236/iim.2011.34019.
References
[1] H. H. Xie, “Application of Data Mining in Customer Segmen-tation of Stockjobber’s CRM,” Computer Engineering, Vol. 30, No. 10, 2002, pp. 553-554. [2] W. H. Chen, “Application of Data Mining in CRM,” Microcomputer Applications, Vol. 17, No. 10, 2001, pp. 25-28. [3] G. J. Mao, L. J. Duan, S. Wang and Y. Shi, “Principle and Algorithm of Data Mining,” Tsinghua University Press, Beijing, 2007. [4] Y. C. Cai and L. C. Chen, “Summarizing the Researches on the Clustering Al-gorithm,” Science and Techenology Information Development & Economy, Vol. 17, No. 1, 2007, pp. 145-146. [5] F. Murtagh, “Interpreting the Kohonen Self-Organizing Feature Map Using Contiguity-Constrained Clustering,” Pattern Rec-ognition Letters, No.16, 1995, pp. 99-408. [6] A. Chen, N. Chen and L. X. Zhou, “The Data Mining Technology and Ap-plication,” Science Press, Shanghai, 2006. [7] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, 1986, pp. 81-106. doi:10.1007/BF00116251 [8] Y. Liu, D. Wan and P. Zhang, “Comparative Research of Solutions for the Customer Seg-mentation Based on the Purchase Behave,” Management Sci-ence in China, Vol. 16, No. 1, 2003, pp. 69-72. [9] L. Liu and G. P. Yu, “Application of Discovering the Potential Customers Based on Self-Organizing Feature Map Neural Network,” Journal of Nanchang University(Natural Science), Vol. 30, No. 5, 2006, pp. 508-510. [10] L. Wang, S. L. Chen and X. D. Gu, “Analysis and Application for National Telecoms of Customer Churn Alarm Models,” Telecommunications Science, No. 9, 2006, pp. 47-5l. [11] S. R. Combating, “The Churn Phenome-non of Telecommunications,” International edition, Vol. 31, No. 10, 1997, pp. 77-79.
[1] H. H. Xie, “Application of Data Mining in Customer Segmen-tation of Stockjobber’s CRM,” Computer Engineering, Vol. 30, No. 10, 2002, pp. 553-554. [2] W. H. Chen, “Application of Data Mining in CRM,” Microcomputer Applications, Vol. 17, No. 10, 2001, pp. 25-28. [3] G. J. Mao, L. J. Duan, S. Wang and Y. Shi, “Principle and Algorithm of Data Mining,” Tsinghua University Press, Beijing, 2007. [4] Y. C. Cai and L. C. Chen, “Summarizing the Researches on the Clustering Al-gorithm,” Science and Techenology Information Development & Economy, Vol. 17, No. 1, 2007, pp. 145-146. [5] F. Murtagh, “Interpreting the Kohonen Self-Organizing Feature Map Using Contiguity-Constrained Clustering,” Pattern Rec-ognition Letters, No.16, 1995, pp. 99-408. [6] A. Chen, N. Chen and L. X. Zhou, “The Data Mining Technology and Ap-plication,” Science Press, Shanghai, 2006. [7] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, 1986, pp. 81-106. doi:10.1007/BF00116251 [8] Y. Liu, D. Wan and P. Zhang, “Comparative Research of Solutions for the Customer Seg-mentation Based on the Purchase Behave,” Management Sci-ence in China, Vol. 16, No. 1, 2003, pp. 69-72. [9] L. Liu and G. P. Yu, “Application of Discovering the Potential Customers Based on Self-Organizing Feature Map Neural Network,” Journal of Nanchang University(Natural Science), Vol. 30, No. 5, 2006, pp. 508-510. [10] L. Wang, S. L. Chen and X. D. Gu, “Analysis and Application for National Telecoms of Customer Churn Alarm Models,” Telecommunications Science, No. 9, 2006, pp. 47-5l. [11] S. R. Combating, “The Churn Phenome-non of Telecommunications,” International edition, Vol. 31, No. 10, 1997, pp. 77-79.