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Home > Archive>Volume 26, Issue 12, 2017 >9-17. DOI:10.15888/j.cnki.csa.006086
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Current Situation and Prospect of Customer Churn Management
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    Abstract:

    This paper summarizes the literature about the following aspects: the definitions of customer churn and customer churn management; research contents and application scenarios of customer churn issues; customer churn prediction algorithms and feature extraction methods; the evaluation technologies and measurements. In the end, we point out the shortcomings of the current research and put forward some future research directions.

    Reference
    [1] Hadden J, Tiwari A, Roy R, et al. Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 2007, 34(10): 2902-2917.
    [2] Roberts JH. Developing new rules for new markets. Journal of the Academy of Marketing Science, 2000, 28(1): 31-44. [DOI:10.1177/0092070300281004]
    [3] Nie GL, Rowe W, Zhang LL, et al. Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 2011, 38(12): 15273-15285. [DOI:10.1016/j.eswa.2011.06.028]
    [4] Blattberg RC, Kim BD, Neslin SA. Database Marketing: Analyzing and Managing Customers. New York: Springer, 2008.
    [5] Jahromi AT, Stakhovych S, Ewing M. Managing B2B customer churn, retention and profitability. Industrial Marketing Management, 2014, 43(7): 1258-1268. [DOI:10.1016/j.indmarman.2014.06.016]
    [6] 应维云, 覃正, 赵宇, 等. SVM方法及其在客户流失预测中的应用研究. 系统工程理论与实践, 2007, 27(7): 105-110.
    [7] Amin A, Anwar S, Adnan A, et al. Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 2017, (237): 242-254.
    [8] Larivière B, Van Den Poel D. Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services. Expert Systems with Applications, 2004, 27(2): 277-285. [DOI:10.1016/j.eswa.2004.02.002]
    [9] Coussement K, Lessmann S, Verstraeten G. A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 2017, 95: 27-36. [DOI:10.1016/j.dss.2016.11.007]
    [10] Huang Y, Kechadi T. An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications, 2013, 40(14): 5635-5647. [DOI:10.1016/j.eswa.2013.04.020]
    [11] 张玮, 杨善林, 刘婷婷. 基于CART和自适应Boosting算法的移动通信企业客户流失预测模型. 中国管理科学, 2014, 22(10): 90-96.
    [12] Hadiji F, Sifa R, Drachen A, et al. Predicting player churn in the wild. IEEE Conference on Computational Intelligence and Games. Dortmund, Germany. 2014. 1-8.
    [13] Miguéis VL, Van Den Poel D, Camanho AS, et al. Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Systems with Applications, 2012, 39(12): 11250-11256. [DOI:10.1016/j.eswa.2012.03.073]
    [14] Datta P, Masand B, Mani DR, et al. Automated cellular modeling and prediction on a large scale. Artificial Intelligence Review, 2000, 14(6): 485-502. [DOI:10.1023/A:1006643109702]
    [15] Lima E, Mues C, Baesens B. Monitoring and backtesting churn models. Expert Systems with Applications, 2011, 38(1): 975-982. [DOI:10.1016/j.eswa.2010.07.091]
    [16] Neslin SA, Gupta S, Kamakura W, et al. Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 2006, 43(2): 204-211. [DOI:10.1509/jmkr.43.2.204]
    [17] Lemmens A, Gupta S. Managing churn to maximize profits. Boston, MA: Harvard Business School, 2013.
    [18] 罗彬, 邵培基, 罗尽尧, 等. 基于粗糙集理论—神经网络—蜂群算法集成的客户流失研究. 管理学报, 2011, 8(2): 265-272.
    [19] 于小兵, 曹杰, 巩在武. 客户流失问题研究综述. 计算机集成制造系统, 2012, 18(10): 2253-2263.
    [20] Vafeiadis T, Diamantaras KI, Sarigiannidis G, et al. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 2015, 55: 1-9. [DOI:10.1016/j.simpat.2015.03.003]
    [21] Chen ZY, Fan ZP, Sun MH. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research, 2012, 223(2): 461-472. [DOI:10.1016/j.ejor.2012.06.040]
    [22] De Bock KW, Van Den Poel D. An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems with Applications, 2011, 38(10): 12293-12301. [DOI:10.1016/j.eswa.2011.04.007]
    [23] Moeyersoms J, Martens D. Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decision Support Systems, 2015, 72: 72-81. [DOI:10.1016/j.dss.2015.02.007]
    [24] Amin A, Anwar S, Adnan A, et al. Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. IEEE Access, 2016, (4): 7940-7957. [DOI:10.1109/ACCESS.2016.2619719]
    [25] Jamil S, Khan A. Churn comprehension analysis for telecommunication industry using ALBA. 2016 International Conference on Emerging Technologies (ICET) Islamabad, Pakistan. 2016. 1-5.
    [26] Keaveney SM. Customer switching behavior in service industries: An exploratory study. Journal of Marketing, 1995, 59(2): 71-82. [DOI:10.2307/1252074]
    [27] Padmanabhan B, Hevner A, Cuenco M, et al. From information to operations: Service quality and customer retention. ACM Trans. on Management Information Systems, 2011, 2(4): 1-21.
    [28] 盛昭瀚, 柳炳祥. 客户流失危机分析的决策树方法. 管理科学学报, 2005, 8(2): 20-25.
    [29] 于小兵, 曹杰, 张梦男. B2C电子商务客户流失原因评估研究. 模糊系统与数学, 2012, 26(6): 166-172.
    [30] 李婷婷. 影响B2C电子商务企业客户流失因素的实证分析. 对外经贸, 2014, (1): 136-137, 160.
    [31] Glady N, Baesens B, Croux C. Modeling churn using customer lifetime value. European Journal of Operational Research, 2009, 197(1): 402-411. [DOI:10.1016/j.ejor.2008.06.027]
    [32] 胡理增, 于信阳, 张长赋, 等. 基于经费约束和广义客户终身价值最大化的多客户流失挽救模型. 系统工程理论与实践, 2009, 29(2): 63-69.
    [33] 胡理增, 陈建军. 无约束条件下多客户流失挽救最优化决策. 中国管理科学, 2009, 17(6): 39-43.
    [34] 罗彬, 邵培基, 罗尽尧, 等. 基于竞争对手反击的电信客户流失挽留研究. 管理科学学报, 2011, 14(8): 17-33.
    [35] 罗彬, 邵培基, 罗尽尧, 等. 基于预算限制和客户挽留价值最大化的电信客户流失挽留研究. 管理学报, 2012, 9(2): 280-288.
    [36] Tamaddoni A, Stakhovych S, Ewing M. Comparing churn prediction techniques and assessing their performance: A contingent perspective. Journal of Service Research, 2015, 19(2): 123-141.
    [37] Masand B, Datta P, Mani DR, et al. CHAMP: A prototype for automated cellular churn prediction. Data Mining and Knowledge Discovery, 1999, 3(2): 219-225. [DOI:10.1023/A:1009873905876]
    [38] 丁君美, 刘贵全, 李慧. 改进随机森林算法在电信业客户流失预测中的应用. 模式识别与人工智能, 2015, 28(11): 1041-1049.
    [39] Gordini N, Veglio V. Customers churn prediction and marketing retention Strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 2016, 62: 100-107.
    [40] Yu XB, Guo SS, Guo J, et al. An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 2011, 38(3): 1425-1430. [DOI:10.1016/j.eswa.2010.07.049]
    [41] Saradhi VV, Palshikar GK. Employee churn prediction. Expert Systems with Applications, 2011, 38(3): 1999-2006. [DOI:10.1016/j.eswa.2010.07.134]
    [42] Runge J, Gao P, Garcin F, et al. Churn prediction for high-value players in casual social games. IEEE Conference on Computational Intelligence and Games (CIG). Dortmund, Germany. 2014. 1-8.
    [43] Günther CC, Tvete IF, Aas K, et al. Modelling and predicting customer churn from an insurance company. Scandinavian Actuarial Journal, 2014, 2014(1): 58-71. [DOI:10.1080/03461238.2011.636502]
    [44] Schmittlein DC, Morrison DG, Colombo R. Counting your customers: Who are they and what will they do next? Management Science, 1987, 33(1): 1-24. [DOI:10.1287/mnsc.33.1.1]
    [45] 马少辉, 刘金兰. Pareto/NBD模型实证与应用研究. 管理科学, 2006, 19(5): 45-49.
    [46] Fader PS, Hardie BGS, Lee KL. “Counting your customers”the easy way: An alternative to the Pareto/NBD model. Marketing Science, 2005, 24(2): 275-284. [DOI:10.1287/mksc.1040.0098]
    [47] 夏国恩. 客户流失预测的现状与发展研究. 计算机应用研究, 2010, 27(2): 413-416.
    [48] Xie YY, Li X. Churn prediction with linear discriminant boosting algorithm. 2008 International Conference on Machine Learning and Cybernetics. Kunming, China. 2008. 228-233.
    [49] 应维云, 蔺楠, 谢雅雅, 等. 用LDA Boosting算法进行客户流失预测. 数理统计与管理, 2010, 29(3): 400-408.
    [50] Abbasimehr H, Setak M, Tarokh MJ. A comparative assessment of the performanceof of ensemble learning in customer churn prediction. The International Arab Journal of Information Technology, 2014, 11(6): 599-606.
    [51] Hu XH. A data mining approach for retailing bank customer attrition analysis. Applied Intelligence, 2005, 22(1): 47-60. [DOI:10.1023/B:APIN.0000047383.53680.b6]
    [52] Kim YS. Toward a successful CRM: Variable selection, sampling, and ensemble. Decision Support Systems, 2006, 41(2): 542-553. [DOI:10.1016/j.dss.2004.09.008]
    [53] Lu JX. Predicting customer churn in the telecommunications industry—An application of survival analysis modeling using SAS. SAS User Group International (SUGI27) Online Proceedings. http://www2.sas.com/proceedings/sugi27/p114-27.pdf. [2012/2017-02-28].
    [54] Adebiyi SO, Oyatoye EO, Mojekwu JN. Predicting customer churn and retention rates in Nigeria’s mobile telecommunication industry using markov chain modelling. Acta Universitatis Sapientiae Economics and Business, 2015, 3(1): 67-80.
    [55] 袁旭梅, 康键, 张昕. 动态CRM模型在电子商务中的应用. 中国管理科学, 2003, 11(S1): 343-347.
    [56] Richter Y, Yom-Tov E, Slonim N. Predicting customer churn in mobile networks through analysis of social groups. Proc. of the 2010 SIAM International Conference on Data Mining. Columbus. 2010. 732-741.
    [57] Verbeke W, Martens D, Baesens B. Social network analysis for customer churn prediction. Applied Soft Computing, 2014, 14: 431-446. [DOI:10.1016/j.asoc.2013.09.017]
    [58] Rehman A, Ali AR. Customer churn prediction, segmentation and fraud detection in telecommunication industry. ASE Science Division Conferences. Cambridge, MA, USA. 2014. 1-9.
    [59] Pettersson M. SPC with applications to churn management. Quality and Reliability Engineering International, 2004, 20(5): 397-406. [DOI:10.1002/(ISSN)1099-1638]
    [60] Jiang W, Au T, Tsui KL. A statistical process control approach to business activity monitoring. ⅡE Trans., 2007, 39(3): 235-249. [DOI:10.1080/07408170600743912]
    [61] Samimi Y, Aghaie A. Monitoring usage behavior in subscription-based services using control charts for multivariate attribute characteristics. IEEE International Conference on Industrial Engineering and Engineering Management. Singapore, Singapore. 2008. 1469-1474.
    [62] Chen SH. The gamma CUSUM chart method for online customer churn prediction. Electronic Commerce Research and Applications, 2016, 17: 99-111. [DOI:10.1016/j.elerap.2016.04.003]
    [63] Mozer MC, Wolniewicz R, Grimes DB, et al. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. on Neural Networks, 2000, 11(3): 690-696. [DOI:10.1109/72.846740]
    [64] Brandusoiu I, Toderean G. Churn prediction in the telecommunications sector using support vector machines. Acta Technica Napocensis, 2016, 57(1): 27-30.
    [65] Kirui C, Hong L, Wilson C, et al. Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining. IJCSI International Journal of Computer Science Issues, 2013, 10(2): 165-172.
    [66] Fathian M, Hoseinpoor Y, Minaei-Bidgoli B. Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes, 2016, 45(5): 732-743. [DOI:10.1108/K-07-2015-0172]
    [67] 柳炳祥, 盛昭翰. 一种基于Rough集的客户流失风险分析方法. 中国管理科学, 2002, 10(S1): 130-133.
    [68] 朱帮助. 基于SMC-RS-LSSVM的电子商务客户流失预测模型. 系统工程理论与实践, 2010, 30(11): 1960-1967. [DOI:10.12011/1000-6788(2010)11-1960]
    [69] 琚春华, 卢琦蓓, 郭飞鹏. 融入个体活跃度的电子商务客户流失预测模型. 系统工程理论与实践, 2013, 33(1): 141-150. [DOI:10.12011/1000-6788(2013)1-141]
    [70] Holtrop N, Wieringa JE, Gijsenberg MJ, et al. No future without the past? Predicting churn in the face of customer privacy. International Journal of Research in Marketing, 2016, 34(1): 154-172.
    [71] Provost FJ, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. Proc. of the 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, USA. 1998. 98. 445-453.
    [72] Langley P. Crafting papers on machine learning. Proc. of the 17th International Conference on Machine Learning. San Francisco, CA, USA. 2000. 343-354.
    [73] Hand DJ. Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning, 2009, 77(1): 103-123. [DOI:10.1007/s10994-009-5119-5]
    [74] 夏国恩, 金炜东. 基于支持向量机的客户流失预测模型. 系统工程理论与实践, 2008, 28(1): 71-77.
    [75] Lessmann S, Baesens B, Seow HV, et al. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 2015, 247(1): 124-136. [DOI:10.1016/j.ejor.2015.05.030]
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张珠香,骆念蓓.客户流失管理研究现状及展望.计算机系统应用,2017,26(12):9-17

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  • Received:March 10,2017
  • Revised:March 27,2017
  • Online: December 07,2017
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