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Koen W. De Bock
Koen W. De Bock
Professor of marketing analytics & digital marketing, Audencia Business School, Nantes, France
Bestätigte E-Mail-Adresse bei audencia.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
3022018
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
1582013
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
1432011
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
1092014
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
982010
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
832010
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
652012
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
562018
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
542021
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
412020
Maximize what matters: Predicting customer churn with decision-centric ensemble selection
A Baumann, S Lessmann, K Coussement, KW De Bock
242015
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
202020
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
202017
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
192019
Leveraging fine-grained transaction data for customer life event predictions
A De Caigny, K Coussement, KW De Bock
Decision Support Systems 130, 113232, 2020
172020
Ensembles of probability estimation trees for customer churn prediction
KWD Bock, DV Poel
International Conference on Industrial, Engineering and Other Applications …, 2010
162010
Advanced database marketing: innovative méthodologies and applications for managing Customer relationships
KW De Bock
Routledge, 2016
92016
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, 1-39, 2021
62021
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
62012
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
52021
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