Credit card churn forecasting by logistic regression and decision tree

Expert Systems with Applications

Volumn 38, Issue 12, 2011, Pages 15273-15285

  Nie, Guangli ^a,d   Rowe, Wei ^c   Zhang, Lingling ^a,b   Tian, Yingjie ^a   Shi, Yong ^a,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

^d AGRICULTURAL BANK OF CHINA   (China)

Author keywords

Credit card; Customer churn; Data mining; Decision tree; Logistic regression

Indexed keywords

CLASSIFICATION ALGORITHM; CREDIT CARDS; CUSTOMER CHURN; CUSTOMER INFORMATION; DATA MINING ALGORITHM; ECONOMIC SENSE; LOGISTIC REGRESSION; LOGISTIC REGRESSIONS; MISCLASSIFICATION COSTS; PREDICTION MODEL; RISK INFORMATION; TEST RESULTS;

ALGORITHMS; DECISION TREES; FORECASTING; MATHEMATICAL MODELS; PLANT EXTRACTS; REGRESSION ANALYSIS; TREES (MATHEMATICS);

DATA MINING;

EID: 80052022630     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.06.028     Document Type: Article

References (39)

1. A.S. Al-Ghamdi Using logistic regression to estimate the influence of accident factors on accident severity Accident Analysis and Prevention 34 2002 729 741
2. M. Better, F. Glover, G. Kochenberger, and H. Wang Simulation optimization: Applications in risk management International Journal of Information Technology and Decision Making 7 4 2008 571 587
3. C. Bielza, P. Barreiro, M.I. Rodríguez-Galiano, and J. Martín Logistic regression for simulating damage occurrence on a fruit grading line Computers and Electronics in Agriculture 39 2003 95 113
4. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth California, USA
5. W. Buckinx, and D. Van den Poel Customer base analysis: Partial defection of behaviorally-loyal clients in a non-contractual fmcg retail setting European Journal of Operational Research 164 1 2005 252 268
6. K. Coussement, and D.V.d. Poel Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques Expert Systems with Applications 34 2008 313 327
7. M. Dixon 39 Experts predict the future America's Community Banker 8 7 1999 20 31
8. T. Floyd Creating a new customer experience Bank Systems and Technology 37 1 2000 R8 R13
9. N. Glady, B. Baesens, and C. Croux Modeling churn using customer lifetime value European Journal of Operational Research 197 2009 402 411
10. N. Glady, B. Baesens, and C. Croux Modeling churn using customer lifetime value European Journal of Operational Research 2008 10.1016/j.ejor.2008.06.027
11. S. Gupta, D.R. Lehmann, and J.A. Stuart Valuing customers Journal of Marketing Research 41 1 2004 7 18
12. A. Gustafsson, M.D. Johnson, and I. Roos The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention Journal of Marketing 69 4 2005 210 218
13. J. Hadden, A. Tiwari, R. Roy, and D. Ruta Computer assisted customer churn management: State-of-the-art and future trends Computers and Operations Research 34 2005 2902 2917
14. D.W. Hosmer, and S. Lemeshow Applied logistic regression 1989 Wiley New York
15. S.Y. Hung, D.C. Yen, and H.Y. Wang Applying data mining to telecom churn management Expert Systems with Applications 31 3 2006 515 524
16. J. Lu Predicting customer churn in the telecommunications industry - An application of survival analysis modeling using SAS 2002 Sprint Communications Company
17. B. Larivire, 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 2004 277 285
18. T.-S. Lee, C.-C. Chiu, Y.-C. Chou, and C.-J. Lu Mining the customer credit using classification and regression tree and multivariate adaptive regression splines Computational Statistics and Data Analysis 50 2006 1113 1130
19. A. Li, Y. Shi, and J. He MCLP-based methods for improving "bad" catching rate in credit cardholder behavior analysis Applied Soft Computing 8 3 2008 1259 1265
20. A.C. Lorena, and A.C.P.L.F.d. Carvalho Protein cellular localization prediction with support vector machines and decision trees Computers in Biology and Medicine 37 2007 115 125
21. A.R.g. Martín, J.M.M.n. Nieto, J.P.N. Ruiz, and L.s.E. Jiménez Overweight and obesity: The role of education, employment and income in Spanish adults Appetite 51 2008 266 272
22. T. Mitchell Machine learning 1997 McGraw-Hill New York
23. D. Mclain, and R. Aldag Complexity and familiarity with computer assistance when making ill-structured business decisions International Journal of Information Technology and Decision Making 8 3 2009 407 426
24. S.A. Neslin, S. Gupta, W. Kamakura, J. Lu, and C. Mason Defection detection: Improving predictive accuracy of customer churn models Journal of Marketing Research 43 2 2006 204 211
25. G. Nie, G. Wang, P. Zhang, Y. Tian, and Y. Shi Finding the hidden pattern of credit card holder's churn: A case of China Lecture Notes in Computer Science 5545 2009 561 569
26. Nie, G., Zhang, L., Li, X., & Shi, Y. (2006). The analysis on the customers churn of charge email based on data mining - Take one internet company for example. In Sixth IEEE international conference on data mining, Hong Kong, China (pp. 843-847).
27. K.M. Osei-Bryson Evaluation of decision trees: a multi-criteria approach Computers and Operations Research 31 2004 1933 1945
28. Y. Peng, G. Kou, Y. Shi, and Z. Chen A multi-criteria convex quadratic programming model for credit data analysis Decision Support Systems 44 2008 1016 1030
29. J.R. Quinlan Induction on decision trees Machine Learning 1 1986 81 106
30. J.R. Quinlan C4.5: Programs for machine learning 1993 Kluwer Academic Publishers
31. F.F. Reichheld, and W.E. Sasser Jr. Zero defections: Quality comes to service Harvard Business Review 68 5 1990 105 111
32. J.H. Roberts Developing new rules for new markets Journal of the Academy of Marketing Science 28 1 2000 31 44
33. Y. Shi, Y. Peng, G. Kou, and Z. Chen Classifying credit card accounts for business intelligence and decision making: A multiple-criteria quadratic programming approach International Journal of Information Technology and Decision Making 4 2005 581 600
34. S.F. Slater, and J.C. Narver Intelligence generation and superior customer value Journal of the Academy of Marketing Science 28 1 2000 120 127
35. C.F. Tsai, and Y.J. Chiou Earnings management prediction: A pilot study of combining neural networks and decision trees Expert Systems with Applications 36 2009 7183 7191
36. D. Van den Poel, and B. Larivire Customer attrition analysis for financial services using proportional hazard models European Journal of Operational Research 157 1 2004 196 217
37. I.H. Witten, and E. Frank Data mining: Practical machine learning tools and techniques 2nd ed. 2005 Morgan Kaufman San Francisco
38. D. Wu Supplier selection: A hybrid model using DEA, decision tree and neural network Expert Systems with Applications 36 2009 9105 9112
39. Y. Zhao, B. Li, X. Li, W. Liu, and S. Ren Customer churn prediction using improved one-class support vector machine 2005 Springer Berlin