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Koen W. De Bock
Koen W. De Bock
Professor of marketing analytics & digital marketing, Audencia Business School, Nantes, France
Geverifieerd e-mailadres voor audencia.com - Homepage
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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
4992018
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
1992013
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
1782011
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
1372014
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
1192020
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
1082010
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
932010
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
892012
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
782021
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
702018
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
362020
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
362019
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
272021
Maximize what matters: Predicting customer churn with decision-centric ensemble selection
A Baumann, S Lessmann, K Coussement, KW De Bock
272015
Leveraging fine-grained transaction data for customer life event predictions
A De Caigny, K Coussement, KW De Bock
Decision Support Systems 130, 113232, 2020
262020
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
232017
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
192023
Ensembles of probability estimation trees for customer churn prediction
KW De Bock, D Van den Poel
Trends in Applied Intelligent Systems: 23rd International Conference on …, 2010
182010
Advanced database marketing: innovative methodologies and applications for managing customer relationships
KW De Bock
Routledge, 2016
112016
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
72024
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Artikelen 1–20