| A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees A De Caigny, K Coussement, KW De Bock European Journal of Operational Research 269 (2), 760-772, 2018 | 614 | 2018 |
| Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning K Coussement, KW De Bock Journal of Business Research 66 (9), 1629-1636, 2013 | 224 | 2013 |
| An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction KW De Bock, D Van den Poel Expert Systems with Applications 38 (10), 12293-12301, 2011 | 187 | 2011 |
| Incorporating textual information in customer churn prediction models based on a convolutional neural network A De Caigny, K Coussement, KW De Bock, S Lessmann International Journal of Forecasting 36 (4), 1563-1578, 2020 | 159 | 2020 |
| Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees K Coussement, FAM Van den Bossche, KW De Bock Journal of Business Research 67 (1), 2751-2758, 2014 | 148 | 2014 |
| Predicting website audience demographics forweb advertising targeting using multi-website clickstream data KW De Bock, D Van den Poel Fundamenta Informaticae 98 (1), 49-70, 2010 | 109 | 2010 |
| Targeting customers for profit: An ensemble learning framework to support marketing decision-making S Lessmann, J Haupt, K Coussement, KW De Bock Information Sciences 557, 286-301, 2021 | 102 | 2021 |
| Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models KW De Bock, D Van den Poel Expert Systems with Applications 39 (8), 6816-6826, 2012 | 102 | 2012 |
| Ensemble classification based on generalized additive models KW De Bock, K Coussement, D Van den Poel Computational Statistics & Data Analysis 54 (6), 1535-1546, 2010 | 97 | 2010 |
| A framework for configuring collaborative filtering-based recommendations derived from purchase data S Geuens, K Coussement, KW De Bock European Journal of Operational Research 265 (1), 208-218, 2018 | 79 | 2018 |
| Explainable AI for operational research: A defining framework, methods, applications, and a research agenda KW De Bock, K Coussement, A De Caigny, R Słowiński, B Baesens, ... European Journal of Operational Research 317 (2), 249-272, 2024 | 58 | 2024 |
| Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach KW De Bock, K Coussement, S Lessmann European journal of operational research 285 (2), 612-630, 2020 | 51 | 2020 |
| Churn prediction with sequential data and deep neural networks. a comparative analysis CG Mena, A De Caigny, K Coussement, KW De Bock, S Lessmann arXiv preprint arXiv:1909.11114, 2019 | 48 | 2019 |
| Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling KW De Bock, A De Caigny Decision Support Systems 150, 113523, 2021 | 42 | 2021 |
| Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction Z Liu, P Jiang, KW De Bock, J Wang, L Zhang, X Niu Technological Forecasting and Social Change 198, 122945, 2024 | 40 | 2024 |
| Leveraging fine-grained transaction data for customer life event predictions A De Caigny, K Coussement, KW De Bock Decision Support Systems 130, 113232, 2020 | 32 | 2020 |
| Configurations of business founder resources, strategy, and environment determining new venture performance J Debrulle, P Steffens, KW De Bock, S De Winne, J Maes Journal of Small Business Management 61 (2), 1023-1061, 2023 | 29 | 2023 |
| Maximize what matters: Predicting customer churn with decision-centric ensemble selection A Baumann, S Lessmann, K Coussement, KW De Bock | 27 | 2015 |
| Exploiting time-varying RFM measures for customer churn prediction with deep neural networks G Mena, K Coussement, KW De Bock, A De Caigny, S Lessmann Annals of Operations Research 339 (1), 765-787, 2024 | 24 | 2024 |
| The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles KW De Bock Expert Systems with Applications 90, 23-39, 2017 | 24 | 2017 |