Dynamic churn prediction framework with more effective use of rare event data: The case of private banking

Expert Systems with Applications

Volumn 41, Issue 17, 2014, Pages 7889-7903

  Gür Ali, Özden ^a   Aritürk, Umut ^a  

^a KOÇ UNIVERSITY   (Turkey)

Author keywords

Customer relationship management; Customer retention; Data mining; Dynamic churn prediction; Private banking; Rare event; Sampling; Training data generation

Indexed keywords

BIOINFORMATICS; CUSTOMER SATISFACTION; DATA MINING; FORECASTING; SALES; SAMPLING;

CHURN PREDICTIONS; CUSTOMER RELATIONSHIP MANAGEMENT; CUSTOMER RETENTION; PRIVATE BANKING; RARE EVENT; TRAINING DATA;

CLASSIFICATION (OF INFORMATION);

EID: 84905266057     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.06.018     Document Type: Article

References (54)

1. M. Ballings, and D. Van den Poel Customer event history for churn prediction: How long is long enough? Expert Systems with Applications 39 18 2012 13517 13522
2. R. Bellman Directions of mathematical research in nonlinear circuit theory IRE Transactions on Circuit Theory 7 4 1960 542 553
3. T.H.A. Bijmolt, P.S.H. Leeflang, F. Block, M. Eisenbeiss, B.G.S. Hardie, and A. Lemmens et al. Analytics for customer engagement Journal of Service Research 13 3 2010 341 356 10.1177/1094670510375603
4. M. Braun, and D.A. Schweidel Modeling customer lifetimes with multiple causes of churn Marketing Science 30 5 2011 881 902
5. W. Buckinx, and D. Van den Poel Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting European Journal of Operational Research 164 1 2005 252 268 10.1016/j.ejor.2003. 12.010
6. J. Burez, and D. Van den Poel Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department Expert Systems with Applications 35 1-2 2008 497 514 10.1016/j.eswa.2007.07.036
7. J. Burez, and D. Van den Poel Handling class imbalance in customer churn prediction Expert Systems with Applications 36 3, Part 1 2009 4626 4636 10.1016/j.eswa.2008.05.027
8. L. Cao In-depth behavior understanding and use: The behavior informatics approach Information Sciences 180 17 2010 3067 3085 http://dx.doi.org/10.1016/j. ins.2010.03.025
9. N.V. Chawla, K.W. Bowyer, and L.O. Hall Smote: Synthetic minority over-sampling technique Journal of Artificial Intelligence Research 16 2002 321 357 (Pubitemid 43057176)
10. B.H. Chu, M.S. Tsai, and C.S. Ho Toward a hybrid data mining model for customer retention Knowledge-Based Systems 20 8 2007 703 718 10.1016/j.knosys.2006.10.003 (Pubitemid 350081071)
11. K. Coussement, and D. Van den Poel Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques Expert Systems with Applications 34 1 2008 313 327 10.1016/j.eswa.2006.09.038 (Pubitemid 46756260)
12. S.F. Crone, S. Lessmann, and R. Stahlbock The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing European Journal of Operational Research 173 3 2006 781 800 http://dx.doi.org/ 10.1016/j.ejor.2005.07.023 (Pubitemid 43866326)
13. J. Dawes, and S. Swailes Retention sans frontiers: Issues for financial service retailers International Journal of Bank Marketing 17 1 1999 36 43
14. K.W. De Bock, and D.V.d. Poel An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction Expert Systems with Applications 38 10 2011 12293 12301
15. P.S. Fader, and B.G. Hardie Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity Marketing Science 29 1 2010 85 93
16. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 8 2006 861 874
17. Frank, E.; Hall, M. (2001). A simple approach to ordinal classification. In Paper presented at the proceedings of the 12th European conference on machine learning.
18. M. Gebregziabher, L. Egede, G.E. Gilbert, K. Hunt, P.J. Nietert, and P. Mauldin Fitting parametric random effects models in very large data sets with application to VHA national data BMC Medical Research Methodology 12 1 2012 163
19. W.H. Greene Econometric analysis 7th ed. 2012 Prentice Hall Boston
20. T. Hastie, R. Tibshirani, and J.H. Friedman The elements of statistical learning: Data mining, inference, and prediction 2nd ed. 2009 Springer New York, NY
21. B.Q. Huang, T.-M. Kechadi, B. Buckley, G. Kiernan, E. Keogh, and T. Rashid A new feature set with new window techniques for customer churn prediction in land-line telecommunications Expert Systems with Applications 37 5 2010 3657 3665
22. Z. Jamal, and R.E. Bucklin Improving the diagnosis and prediction of customer churn: A heterogeneous hazard modeling approach Journal of Interactive Marketing 20 3-4 2006 16 29 10.1002/dir.20064
23. W. Kamakura, C.F. Mela, A. Ansari, A. Bodapati, P. Fader, and R. Iyengar et al. Choice models and customer relationship management Marketing Letters 16 3-4 2005 279 291 (Pubitemid 43201193)
24. A. Karahoca, and D. Karahoca GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system Expert Systems with Applications 38 3 2011 1814 1822
25. G. King, and L. Zeng Logistic regression in rare events data Political Analysis 9 2 2001 137 163
26. D.G. Kleinbaum, and M. Klein Survival analysis: A self-learning text 2nd ed. 2005 Springer New York, NY
27. A.D. Kumar, and V. Ravi Predicting credit card customer churn in banks using data mining International Journal of Data Analysis Techniques and Strategies 1 1 2008 4 28 [10.1504/IJDATS.2008.02002]
28. B. Larivière, and D. Van den Poel 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 27 2 2004 277 285 10.1016/j.eswa.2004.02.002
29. W.M. Lassar, C. Manolis, and R.D. Winsor Service quality perspectives and satisfaction in private banking Journal of Services Marketing 14 3 2000 244 271
30. Y.-H. Lee, C.-P. Wei, T.-H. Cheng, and C.-T. Yang Nearest-neighbor-based approach to time-series classification Decision Support Systems 53 1 2012 207 217
31. A. Lemmens, and C. Croux Bagging and boosting classification trees to predict churn Journal of Marketing Research 43 2 2006 276 286 10.1509/jmkr.43.2.276 (Pubitemid 43813059)
32. L. Lusa SMOTE for high-dimensional class-imbalanced data BMC Bioinformatics 14 1 2013 106
33. M. Mavri, and G. Ioannou Customer switching behaviour in Greek banking services using survival analysis Managerial Finance 34 3 2008 186 197
34. S.A. Neslin, S. Gupta, W. Kamakura, L. Junxiang, and C.H. Mason Defection detection: Measuring and understanding the predictive accuracy of customer churn models Journal of Marketing Research (JMR) 43 2 2006 204 211 (Pubitemid 43813052)
35. C. Orsenigo, and C. Vercellis Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification Pattern Recognition 43 11 2010 3787 3794 http://dx.doi.org/10.1016/j.patcog. 2010.06.005
36. M. Owczarczuk Churn models for prepaid customers in the cellular telecommunication industry using large data marts Expert Systems with Applications 37 6 2010 4710 4712
37. M.L. Pennell, and D.B. Dunson Fitting semiparametric random effects models to large data sets Biostatistics 8 4 2007 821 834 (Pubitemid 47511807)
38. C. Perlich, F. Provost, and J.S. Simonoff Tree induction vs. logistic regression: A learning-curve analysis The Journal of Machine Learning Research 4 2003 211 255
39. A. Prinzie, and D. Van den Poel Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM Decision Support Systems 42 2 2006 508 526 http://dx.doi.org/10.1016/j.dss.2005. 02.004 (Pubitemid 44537460)
40. F.F. Reichheld Learning from customer defections Harvard Business Review 74 1996 56 69
41. F.F. Reichheld, and D.W. Kenny The hidden advantages of customer retention Journal of Retail Banking 12 4 1990 19 23
42. H.K. Stahl, K. Matzler, and H.H. Hinterhuber Linking customer lifetime value with shareholder value Industrial Marketing Management 32 4 2003 267 279 (Pubitemid 36598455)
43. Stock, J. H.; Watson, M. W. (1989). New indices of coincident and leading economic indicators. In Paper presented at the NBER macroeconomics annual.
44. K. Storbacka Segmentation based on customer profitability - retrospective analysis of retail bank customer bases Journal of Marketing Management 13 5 1997 479 492
45. C.-F. Tsai, and M.-Y. Chen Variable selection by association rules for customer churn prediction of multimedia on demand Expert Systems with Applications 37 3 2010 2006 2015
46. D. Van den Poel, and B. Lariviere Customer attrition analysis for financial services using proportional hazard models European Journal of Operational Research 157 1 2004 196 217 10.1016/S0377-2217(03)00069-9
47. R. Venkatesan, and V. Kumar A customer lifetime value framework for customer selection and resource allocation strategy Journal of Marketing 68 4 2004 106 125 (Pubitemid 39508315)
48. W. Verbeke, K. Dejaeger, D. Martens, J. Hur, and B. Baesens New insights into churn prediction in the telecommunication sector: A profit driven data mining approach European Journal of Operational Research 218 1 2012 211 229 http://dx.doi.org/10.1016/j.ejor.2011.09.031
49. W. Verbeke, D. Martens, C. Mues, and B. Baesens Building comprehensible customer churn prediction models with advanced rule induction techniques Expert Systems with Applications 38 3 2011 2354 2364 10.1016/j.eswa.2010.08.023
50. G.M. Weiss Mining with rarity: A unifying framework SIGKDD Exploration Newsletter 6 1 2004 7 19 10.1145/1007730.1007734
51. G. Widmer, and M. Kubat Learning in the presence of concept drift and hidden contexts Machine Learning 23 1 1996 69 101 10.1023/a:1018046501280 (Pubitemid 126737384)
52. J. Xiao, L. Xie, C. He, and X. Jiang Dynamic classifier ensemble model for customer classification with imbalanced class distribution Expert Systems with Applications 39 3 2012 3668 3675
53. Y. Xie, X. Li, E.W.T. Ngai, and W. Ying Customer churn prediction using improved balanced random forests Expert Systems with Applications 36 3, Part 1 2009 5445 5449 10.1016/j.eswa.2008.06.121
54. U. Yavas, M. Benkenstein, and U. Stuhldreier Relationships between service quality and behavioral outcomes: A study of private bank customers in Germany International Journal of Bank Marketing 22 2 2004 144 157