Koen W. De Bock
Koen W. De Bock
Professor of marketing analytics & digital marketing, Audencia Business School, Nantes, France
Vahvistettu sähköpostiosoite verkkotunnuksessa audencia.com - Kotisivu
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Vuosi
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
1202011
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
1082013
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
1072018
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
832014
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
822010
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
652010
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
412012
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
342018
Targeting customers for profit: An ensemble learning framework to support marketing decision-making
S Lessmann, J Haupt, K Coussement, KW De Bock
Information Sciences, 2019
202019
Maximize what matters: Predicting customer churn with decision-centric ensemble selection
A Baumann, S Lessmann, K Coussement, KW De Bock
182015
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
152017
Ensembles of probability estimation trees for customer churn prediction
KW De Bock, D Van den Poel
International Conference on Industrial, Engineering and Other Applications …, 2010
142010
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
102020
Leveraging fine-grained transaction data for customer life event predictions
A De Caigny, K Coussement, KW De Bock
Decision Support Systems 130, 113232, 2020
52020
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
52019
Advanced database marketing: innovative méthodologies and applications for managing Customer relationships
KW De Bock
Routledge, 2016
52016
Quantile regression for database marketing: methods and applications
D Benoit, D Van den Poel
Advanced database marketing: innovative methodologies and applications for …, 2013
3*2013
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, 2020
22020
Advanced Database Marketing
K CousseMent, KW De BoCK
Marketing and Management, 1-7, 2016
22016
Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models
KW De Bock, D Van den Poel
HAL Post-Print, 2012
22012
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Artikkelit 1–20