Improved marketing decision making in a customer churn prediction context using generalized additive models

Using multivariate statistics

Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,882

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916

Generalized Additive Models

Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575

An introduction to variable and feature selection

GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354

Generalized Additive Models.

Koyak, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290538 · Cited by 7,717

Zero defections: quality comes to services.

Reichheld, F. F., & Sasser, W. E. (1990) · PubMed · Cited by 5,373

Projection Pursuit Regression

Friedman, J. H., & Stuetzle, W. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477729 · Cited by 2,116

Logistic Regression Using the SAS System : Theory and Application

Allison, P. D. (1999) · Cited by 2,087

Signal detection theory and ROC analysis

Egan, J. P. (1975) · Academic Press eBooks · Cited by 1,659

Switching barriers and repurchase intentions in services

Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000) · Journal of Retailing · doi:10.1016/s0022-4359(00)00024-5 · Cited by 1,571 · cited 1×

Additive Regression and Other Nonparametric Models

Stone, C. J. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349548 · Cited by 1,265

The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration

Reinartz, W., & Kumar, V. (2003) · Journal of Marketing · doi:10.1509/jmkg.67.1.77.18589 · Cited by 1,170

Adaptive Control Processes: A Guided Tour

Morgenstern, D., & Bellman, R. (1962) · Econometrica · doi:10.2307/1909901 · Cited by 1,018

Generalized Additive Models: Some Applications

Hastie, T. J., & Tibshirani, R. J. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478440 · Cited by 882

The mismanagement of customer loyalty.

Reinartz, W., & Kumar, V. (2002) · PubMed · Cited by 764

Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior

Athanassopoulos, A. D. (2000) · Journal of Business Research · doi:10.1016/s0148-2963(98)00060-5 · Cited by 642

Handling class imbalance in customer churn prediction

Burez, J., & den Poel, D. V. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.05.027 · Cited by 590

Knowledge management and data mining for marketing

Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001) · Decision Support Systems · doi:10.1016/s0167-9236(00)00123-8 · Cited by 575

Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Coussement, K., & den Poel, D. V. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.09.038 · Cited by 453

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Buckinx, W., & den Poel, D. V. (2004) · European Journal of Operational Research · doi:10.1016/j.ejor.2003.12.010 · Cited by 450

Customer attrition analysis for financial services using proportional hazard models

den Poel, D. V., & Larivière, B. (2003) · European Journal of Operational Research · doi:10.1016/s0377-2217(03)00069-9 · Cited by 443

An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry

Hwang, H.-S., Jung, T., & Suh, E. (2003) · Expert Systems with Applications · doi:10.1016/s0957-4174(03)00133-7 · Cited by 420

Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market

Kim, H.-S., & Yoon, C.-H. (2004) · Telecommunications Policy · doi:10.1016/j.telpol.2004.05.013 · Cited by 417

Bagging and Boosting Classification Trees to Predict Churn

Lemmens, A., & Croux, C. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.276 · Cited by 411

Generalized Additive Models; Some Applications

Hastie, T. J., & Tibshirani, R. J. (1985) · Lecture notes in statistics · doi:10.1007/978-1-4615-7070-7_8 · Cited by 407

Interdependence, Punitive Capability, and the Reciprocation of Punitive Actions in Channel Relationships

Kumar, N., Scheer, L. K., & Steenkamp, J. E. M. (1998) · Journal of Marketing Research · doi:10.2307/3151850 · Cited by 342

A Methodology for Linking Customer Acquisition to Customer Retention

Thomas, J. S. (2001) · Journal of Marketing Research · doi:10.1509/jmkr.38.2.262.18848 · Cited by 319

Competitive One-to-One Promotions

Shaffer, G., & Zhang, Z. J. (2002) · Management Science · doi:10.1287/mnsc.48.9.1143.172 · Cited by 292

Binary Response Models: Logits, Probits and Semiparametrics

Horowitz, J. L., & Savin, N. E. (2001) · The Journal of Economic Perspectives · doi:10.1257/jep.15.4.43 · Cited by 262

CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services

Burez, J., & den Poel, D. V. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.11.037 · Cited by 250

Generalized Additive Models

Hastie, T. J., & Tibshirani, R. J. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0297 · Cited by 196

Towards a holistic perspective of customer relationship management (CRM) implementation: A case study of the Housing and Development Board, Singapore

Teo, T. S. H., Devadoss, P., & Pan, S. L. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.01.007 · Cited by 167

7 Logistic discrimination

Anderson, J. A. (1982) · Handbook of statistics · doi:10.1016/s0169-7161(82)02010-0 · Cited by 149

Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers

Coussement, K., & den Poel, D. V. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.07.021 · Cited by 146

Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection TechniquesPreprint

Coussement, K., & den Poel, D. V. (2008) · RePEc: Research Papers in Economics · Cited by 123

Estimating Irregular Pricing Effects: A Stochastic Spline Regression Approach

Kalyanam, K., & Shively, T. S. (1998) · Journal of Marketing Research · doi:10.1177/002224379803500104 · Cited by 114

Toward a successful CRM: variable selection, sampling, and ensemble

Yongseog Kim, A. (2004) · Decision Support Systems · doi:10.1016/j.dss.2004.09.008 · Cited by 102

Carcinogenetic impact of alcohol intake on squamous cell carcinoma risk of the oesophagus in relation to tobacco smoking

Lee, C., Wu, D., Lee, J., Wu, I., Goan, Y., Kao, E., Huang, H., Chan, T., Chou, S., Chou, Y., et al. (2007) · European Journal of Cancer · doi:10.1016/j.ejca.2007.01.039 · Cited by 99

The Customer Concept: The Basis for a New Marketing Paradigm

Hoekstra, J. C., Leeflang, P. S. H., & Wittink, D. R. (1999) · Journal of Market-Focused Management · doi:10.1023/a:1009856028285 · Cited by 80

Identifying issues in customer relationship management at merck-medco

Torkzadeh, G., Chang, J. C.-J., & Hansen, G. W. (2006) · Decision Support Systems · doi:10.1016/j.dss.2005.10.003 · Cited by 56

Evaluation of prediction models for marketing campaigns

Rosset, S., Neuman, E., Eick, U., Vatnik, N., & Idan, I. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502581 · Cited by 37

Benders decomposition applied to multi-commodity, multi-mode distribution planning

Çakır, O. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.10.037 · Cited by 37

Habitat characteristics of crayfish (Paranephrops planifrons) in New Zealand streams using generalised additive models (GAMs)

Jowett, I. G., Parkyn, S. M., & Richardson, J. (2007) · Hydrobiologia · doi:10.1007/s10750-007-9108-z · Cited by 36

A Nonparametric Approach to Identifying Latent Relationships in Hierarchical Models

Shively, T. S., Allenby, G. M., & Kohn, R. (2000) · Marketing Science · doi:10.1287/mksc.19.2.149.11807 · Cited by 21

Data Mining & KDD

(2006) · Studies in computational intelligence · doi:10.1007/978-3-540-34351-6_9 · Cited by 7

Machine-Learning Techniques for Customer Retention: A Comparative Study

Sahar, F. N. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.090238 · Cited by 173

Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning

Coussement, K., & Bock, K. W. D. (2013) · Journal of Business Research · doi:10.1016/j.jbusres.2012.12.008 · Cited by 158

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

Bock, K. W. D., & den Poel, D. V. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.007 · Cited by 133

Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model

Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113325 · Cited by 125

A Survey on Churn Analysis in Various Business Domains

Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108

Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction

Coussement, K., Benoit, D. F., & den Poel, D. V. (2014) · Developments in marketing science: proceedings of the Academy of Marketing Science · doi:10.1007/978-3-319-10873-5_134 · Cited by 11

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Bock, K. W. D., & den Poel, D. V. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.014 · Cited by 73

Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models

Bock, K. W. D., & van den Poel, D. (2012) · HAL

Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry

Slavescu, E. O., & Panait, I. (2012) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection

Baumann, A., Lessmann, S., Coussement, K., & Bock, K. W. D. (2015) · Journal of the Association for Information Systems · doi:10.18151/7217273 · Cited by 17