An enhanced ensemble classifier for telecom churn prediction using cost based uplift modelling

被引:11
作者
Ahmed A.A.Q. [1 ]
Maheswari D. [1 ]
机构
[1] Department of Computer Science, Rathnavel Subramaniam College of Arts and Science Sulur, Coimbatore, Tamil Nadu
关键词
Cost based modeling; Ensemble technique; Telecom churns prediction; Uplift modeling;
D O I
10.1007/s41870-018-0248-3
中图分类号
学科分类号
摘要
Telecom, being a dynamic and competitive industry which contains an inherently high potential for customer churn, necessitating of accurate churn prediction models. Regular classification approaches fail to effectively predict churn due to low correlation levels between conventional performance metrics and business goals. This work presents an ensemble stacking incorporated with uplifting-based strategies for telecom churn prediction model. Evaluations have been performed based on conventional performance and a cost heuristic, with a major focus upon the cost heuristic. This mode of operation exhibits a high correlation levels between performance indicators and business goals, thus enabling the algorithm suitable for most cost-sensitive applications. A heterogeneous ensemble is created by using multiple algorithms to provide first level predictions. Those predictions with discrepancies are processed at the secondary level using a heuristic based combiner to provide the final predictions. Combination heuristics are fine-tuned based on the cost to predict more accurately concentrating on business goals. Subsequently, Customer uplifting is performed on final predictions, thus making the proposed model 50% more cost efficient than the state-of-the-art ensemble models. © 2018, Bharati Vidyapeeth's Institute of Computer Applications and Management.
引用
收藏
页码:381 / 391
页数:10
相关论文
共 36 条
[1]  
Desa U.N., World Population Prospects: The 2012 Revision. Population Division of The Department of Economic and Social Affairs of The United Nations Secretariat, (2013)
[2]  
Bhattacharya C., When customers are members: customer retention in paid membership contexts, J Acad Mark Sci, 26, 1, pp. 31-44, (1998)
[3]  
Athanassopoulos A., Customer satisfaction cues to support market segmentation and explain switching behavior, J Bus Res, 47, 3, pp. 191-207, (2000)
[4]  
Farris P.W., Bendle N.T., Pfeifer P.E., Reibstein D.J., Marketing Metrics: The Definitive Guide to Measuring Marketing Performance, (2010)
[5]  
Keaveney S.M., Customer switching behavior in service industries: an exploratory study, J Mark, 59, 2, pp. 71-82, (1995)
[6]  
Padmanabhan B., Et al., From information to operations: service quality and customer retention, ACM Trans Manag Inf Syst, 2, (2011)
[7]  
Eiben A., Koudijs A., Slisser F., Genetic modeling of customer retention, Lect Notes Comput Sci, 1391, pp. 178-186, (1998)
[8]  
Mozer M., Wolniewicz R., Grimes D., Johnson E., Kaushansky H., Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry, IEEE Trans Neural Netw, 11, 3, pp. 690-696, (2000)
[9]  
Buckinx W., Van den Poel D., Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting, Eur J Oper Res, 164, 1, pp. 252-268, (2005)
[10]  
Kumar D., Ravi V., Predicting credit card customer churn in banks using data mining, Int J Data Anal Tech Strat, 1, 1, pp. 4-28, (2008)