New insights into churn prediction in the telecommunication sector: A profit driven data mining approach

European Journal of Operational Research

Volumn 218, Issue 1, 2012, Pages 211-229

  Verbeke, Wouter ^a   Dejaeger, Karel ^a   Martens, David ^b   Hur, Joon ^c   Baesens, Bart ^a,d,e  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF ANTWERP   (Belgium)

^c Samjoung Building 701 2   (South Korea)

^d UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^e VLERICK BUSINESS SCHOOL   (Belgium)

Author keywords

Churn prediction; Data mining; Input selection; Oversampling; Profit; Telecommunication sector

Indexed keywords

CLASSIFICATION TECHNIQUE; CUSTOMER CHURN PREDICTION; CUSTOMER RETENTION; EXTENSIVE BENCHMARKING; INPUT SELECTION; LARGE GROUPS; MODEL SELECTION; OPTIMAL MODEL; OVER SAMPLING; PERFORMANCE MEASURE; PREDICTION MODEL; PROFIT; REAL LIFE DATASETS; STATISTICAL MEASURES; TELECOM OPERATORS; TELECOMMUNICATION SECTOR;

BENCHMARKING; CLASSIFICATION (OF INFORMATION); COST REDUCTION; CUSTOMER SATISFACTION; FORECASTING; MATHEMATICAL MODELS; OPTIMIZATION; PROFITABILITY; SALES; TELECOMMUNICATION;

DATA MINING;

EID: 83955164226     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2011.09.031     Document Type: Article

References (70)

1. A. Athanassopoulos Customer satisfaction cues to support market segmentation and explain switching behavior Journal of Business Research 47 3 2000 191 207
2. W. Au, K. Chan, and X. Yao A novel evolutionary data mining algorithm with applications to churn prediction IEEE Transactions on Evolutionary Computation 7 6 2003 532 545
3. B. Baesens, R. Setiono, C. Mues, and J. Vanthienen Using neural network rule extraction and decision tables for credit-risk evaluation Management Science 49 3 2003 312 329
4. B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen Benchmarking state-of-the-art classification algorithms for credit scoring Journal of the Operational Research Society 54 6 2003 627 635
5. C. Bhattacharya When customers are members: Customer retention in paid membership contexts Journal of the Academy of Marketing Science 26 1 1998 31 44
6. C. Bishop Neural networks for pattern recognition 1996 Oxford University Press Oxford, UK
7. R. Bolton, K. Lemon, and M. Bramlett The effect of service experiences over time on a supplier's retention of business customers Management Science 52 2006 1811 1823 (Pubitemid 44926997)
8. L. Breiman, J. Friedman, R. Olshen, and C. Stone Classification and Regression Trees 1984 Chapman & Hall New York
9. 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. J. Burez, and D. Van den Poel CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services Expert Systems with Applications 32 2007 277 288 (Pubitemid 44647797)
11. J. Burez, and D. Van den Poel Handling class imbalance in customer churn prediction Expert Systems with Applications 36 3 2009 4626 4636
12. N. Chawla Smote: Synthetic minority over-sampling technique Journal of artificial intelligence research 16 2002 231
13. N. Chawla Data mining for imbalanced datasets: An overview O. Maimon, L. Rokach, Data Mining and Knowledge Discovery Handbook 2010 Springer US 875 886
14. Cohen, W.W.; 1995. Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115-123.
15. M. Colgate, K. Stewart, and R. Kinsella Customer defection: A study of the student market in Ireland International Journal of Bank Marketing 14 3 1996 23 29
16. 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 2008 313 327 (Pubitemid 46756260)
17. Dasgupta, K.; Singh, R.; Viswanathan, B.; Chakraborty, D.; Mukherjea, S.; Nanavati, A.; Joshi, A.; 2008. Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th international conference on Extending Database Technology: Advances in database technology, EDBT'08, pp. 697-711.
18. P. Datta, B. Masand, D. Mani, and B. Li Automated cellular modeling and prediction on a large scale Artificial Intelligence Review 14 2000 485 502 (Pubitemid 32286603)
19. E. DeLong, D. DeLong, and D. Clarke-Pearson Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach Biometrics 44 1988 837 845
20. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
21. O. Dunn Multiple comparisons among means Journal of the American Statistical Association 56 1961 52 64
22. J. Egan Signal Detection Theory and ROC analysis Series in Cognition and Perception 1975 Academic Press New York
23. A. Eiben, A. Koudijs, and F. Slisser Genetic modeling of customer retention Lecture Notes in Computer Science 1391 1998 178 186
24. T. Fawcett, and F. Provost Adaptive fraud detection Data Mining and Knowledge Discovery 1-3 1997 291 316
25. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy Advances in Knowledge Discovery and Data Mining 1996 AAAI/MIT Press
26. Frank, E.; Witten, I.; 1998. Generating accurate rule sets without global optimization. In: Proceedings of the 15th International Conference on Machine Learning, pp. 144-151.
27. Y. Freund, and R. Schapire Large margin classification using the perceptron algorithm Machine Learning 37 3 1999 277 296 (Pubitemid 32210619)
28. Freund, Y.; Trigg, L.; 1999. The alternating decision tree learning algorithm. In: Proceedings of the 16th International Conference on Machine Learning, pp. 124-133.
29. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1940 86 92
30. J. Ganesh, M. Arnold, and K. Reynolds Understanding the customer base of service providers: An examination of the differences between switchers and stayers Journal of Marketing 64 3 2000 65 87
31. N. Glady, B. Baesens, and C. Croux A modified pareto/NBD approach for predicting customer lifetime value Expert Systems with Applications 36 2 2009 2062 2071
32. S. Gupta, D. Hanssens, B. Hardie, W. Kahn, V. Kumar, N. Lin, N. Ravishanker, and S. Sriram Modeling customer lifetime value Journal of Service Research 9 2 2006 139 155 (Pubitemid 44545801)
33. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning, Data Mining, Inference, and Prediction 2001 Springer
34. S. Hung, D. Yen, and H. Wang Applying data mining to telecom churn management Expert Systems with Applications 31 2006 515 524 (Pubitemid 43831631)
35. J. Hur, and J. Kim A hybrid classification method using error pattern modeling Expert Systems with Applications 34 1 2008 231 241 (Pubitemid 46756253)
36. H. Hwang, T. Jung, and E. Suh An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry Expert Systems with Applications 26 2004 181 188
37. W. Krzanowski, and D. Hand ROC curves for continuous data 2009 CRC/Chapman and Hall
38. 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
39. N. Landwehr, M. Hall, and F. Eibe Logistic model trees Machine Learning 59 1 2005 161 205 (Pubitemid 40890416)
40. B. Lariviere, and D. Van den Poel Predicting customer retention and profitability by using random forest and regression forest techniques Expert Systems with Applications 29 2 2005 472 484 (Pubitemid 40970656)
41. A. Lemmens, and C. Croux Bagging and boosting classification trees to predict churn Journal of Marketing Research 43 2 2006 276 286 (Pubitemid 43813059)
42. S. Lessmann, B. Baesens, C. Mues, and S. Pietsch Benchmarking classification models for software defect prediction: A proposed framework and novel findings IEEE Transactions on Software Engineering 34 4 2008 485 496
43. E. Lima, C. Mues, and B. Baesens Domain knowledge integration in data mining using decision tables: Case studies in churn prediction Journal of the Operational Research Society 60 8 2009 1096 1106
44. D. Martens, M. De Backer, R. Haesen, M. Snoeck, J. Vanthienen, and B. Baesens Classification with ant colony optimization IEEE Transactions on Evolutionary Computation 11 5 2007 651 665 (Pubitemid 47527996)
45. D. Martens, J. Vanthienen, W. Verbeke, and B. Baesens Performance of classification models from a user perspective Decision Support Systems 51 2011 782 793
46. R. Mizerski An attribution explanation of the disproportionate influence of unfavourable information Journal of Consumer Research 9 1982 301 310
47. M. Mozer, R. Wolniewicz, D. Grimes, E. Johnson, and H. Kaushansky Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry IEEE Transactions on Neural Networks 11 3 2000 690 696
48. A. Nanavati, R. Singh, D. Chakraborty, K. Dasgupta, S. Mukherjea, G. Das, S. Gurumurthy, and A. Joshi Analyzing the structure and evolution of massive telecom graphs IEEE Transactions on Knowledge and Data Engineering 20 5 2008 703 718
49. Nemenyi, P.; 1963. Distribution-free multiple comparisons. Ph.D. thesis, Princeton University.
50. S. Neslin, S. Gupta, W. Kamakura, J. Lu, and C. Mason Detection defection: Measuring and understanding the predictive accuracy of customer churn models Journal of Marketing Research 43 2 2006 204 211 (Pubitemid 43813052)
51. M. Paulin, J. Perrien, R. Ferguson, A. Salazar, and L. Seruya Relational norms and client retention: External effectiveness of commercial banking in Canada and Mexico International Journal of Bank Marketing 16 1 1998 24 31
52. G. Piatetsky-Shapiro, and B. Masand Estimating campaign benefits and modeling lift. 1999 185 193
53. S. Piramuthu Evaluating feature selection methods for learning in data mining applications European Journal of Operational Research 156 2 2004 483 494
54. Provost, F.; 2005. From data mining to data science: Evaluation for predictive modeling. Course Notes for B20.3336.30: Data Mining for Business Intelligence, Stern School of Business, New York University, New York, NY, USA.
55. Provost, F.; Jensen, D.; 1999. Evaluating machine learning, knowledge discovery, and data mining. In: Tutorial presented at the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99) and at the Sixteenth National Conference on Artificial Intelligence (AAAI-99).
56. F. Provost, T. Fawcett, and R. Kohavi The case against accuracy estimation for comparing induction algorithms Proceedings of the Fifteenth International Conference on Machine Learning 1998 Morgan Kaufmann 445 453
57. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
58. E. Rasmusson Complaints can build relationships Sales and Marketing Management 151 9 1999 89 90
59. F. Reichheld Learning from customer defections Harvard Business Review 74 2 1996 56 69
60. R. Rust, and A. Zahorik Customer satisfaction, customer retention, and market share Journal of Retailing 69 2 1993 193 215
61. D. Stum, and A. Thiry Building customer loyalty Training and Development Journal 45 4 1991 34 36
62. J. Suykens, and J. Vandewalle Least squares support vector machine classifiers Neural Processing Letters 9 3 1999 293 300
63. J. Swets, and R. Pickett Evaluation of Diagnostic Systems: Methods from Signal Detection Theory 1982 Academic Press New York
64. P. Tan, M. Steinbach, and V. Kumar Introduction to Data Mining 2006 Addison Wesley Boston, MA
65. L. Thomas, D. Edelman, and J. Crook Credit Scoring and its Applications 2002 SIAM
66. 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
67. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag, New York, Inc.; New York, NY, USA
68. 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 2011 2354 2364
69. C. Wei, and I. Chiu Turning telecommunications call details to churn prediction: A data mining approach Expert Systems with Applications 23 2002 103 112
70. V. Zeithaml, L. Berry, and A. Parasuraman The behavioural consequences of service quality Journal of Marketing 60 2 1996 31 46