Koen DE BOCK

COUSSEMENT, K., DE BOCK, K. W., & NESLIN, S. (2014). Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships (translated in simplified Chinese). Beijing: The China Enterprise Management Publishing House.

COUSSEMENT, K., DE BOCK, K. W., & NESLIN, S. (2013). Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships. Routledge.

DE BOCK, K. W., COUSSEMENT, K., CIELEN, D. (2018). An Overview of Multiple Classifier Systems Based on Generalized Additive Models. dans Alfaro Cortes, E., Gamez Martinez, M, and Garcia Rubio, N. (Eds.), Ensemble Classification Methods with Applications in R. John Wiley & Sons.

FLORES, L., & DE BOCK, K. W. (2018). L’analyse des données appliquée à la publicité. dans Allary, J. et Balusseau, V. (Eds.), La publicité à l'heure de la data - Adtech et programmatique expliquées par les experts. Dunod.

DE BOCK, K. W., & COUSSEMENT, K. (2016). Special Session: Big Data Analytics for Marketing (Contributed Session by the IÉSEG Center for Marketing Analytics (ICMA)). dans Rossi, P. (Eds.), Marketing at the Confluence between Entertainment and Analytics. Developments in Marketing Science: Proceedings of the 2016 Academy of Marketing Science (AMS) World Marketing Congress. Springer.

BOUJENA, O., COUSSEMENT, K., & DE BOCK, K. W. (2015). Data Driven Customer Centricity: CRM Predictive Analytics. dans Tsiakis, T. (Eds.), Handbook of Research on Innovations in Marketing Information Systems. IGI Global.

DE BOCK, K. W., & COUSSEMENT, K. (2013). Ensemble Learning in Database Marketing. dans Coussement, K., De Bock, K.W. and Neslin, S.A. (Eds.), Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships. Routledge.

COUSSEMENT, K., & DE BOCK, K. W. (2013). Text Mining for Database Marketing. dans Coussement, K., De Bock, K.W. and Neslin, S.A. (Eds.), Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships. Routledge.

DE BOCK, K. W., & VAN DEN POEL, D. (2010). Ensembles of probability estimation trees for customer churn prediction. dans García-Pedrajas N., Herrera F., Fyfe C., Benítez J.M., Ali M. (Eds.), Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science 6097 (pp. 57-66). Springer.

Forthcoming

MENA, G., COUSSEMENT, K., DE BOCK, K., DE CAIGNY, A., LESSMANN, S. (2023) . Exploiting Time-Varying RFM Measures for Customer Churn Prediction with Deep Neural Networks, Annals of Operations Research

2023

DEBRULLE, J., STEFFENS, P., DE BOCK, K., DE WINNE, S., MAES, J. (2023) . Configurations of Business Founder Resources, Strategy and Environment Determining New Venture Performance, Journal of Small Business Management, 61 (2), 1023-1061

2022

COUSSEMENT, K., DE BOCK, K., GEUENS, S. (2022) . A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer, Annals of Operations Research, 315 (2022), 671–694

2021

DE BOCK, K., DE CAIGNY, A. (2021) . Spline-Rule Ensemble Classifiers with Structured Sparsity Regularization for Interpretable Customer Churn Modeling, Decision Support Systems, 150 (November 2021), Article N°113523

LESSMANN, S., HAUPT, J., COUSSEMENT, K., DE BOCK, K. (2021) . Targeting customers for profit: An ensemble learning framework to support marketing decision-making, Information Sciences, 557 (May 2021), 286-301

2020

DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K., LESSMANN, S. (2020) . Incorporating Textual Information in Customer Churn Prediction Models Based on a Convolutional Neural Network, International Journal of Forecasting, 36 (4), 1563-1578

DE BOCK, K., COUSSEMENT, K., LESSMANN, S. (2020) . Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach, European Journal of Operational Research, 285 (2), 612-630

DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2020) . Leveraging Fine-Grained Transaction Data for Customer Life Event Predictions, Decision Support Systems, 130 (March 2020), Article 113232

2018

DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2018) . A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees, European Journal of Operational Research, 269 (2), 760-772

GEUENS, S., COUSSEMENT, K., DE BOCK, K. (2018) . A framework for configuring collaborative filtering-based recommendations derived from purchase data, European Journal of Operational Research, 265 (1), 208-218

2017

DE BOCK, K. (2017) . The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles, Expert Systems with Applications, 90 (30 December 2017), 23-39

2014

COUSSEMENT, K., VAN DEN BOSSCHE, F., DE BOCK, K. (2014) . Data Accuracy’s Impact on Segmentation Performance: Comparing RFM, Logistic Regression and Decision Trees, Journal of Business Research, 67 (1), 2751–2758

2013

COUSSEMENT, K., DE BOCK, K. (2013) . Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning, Journal of Business Research, 66 (9), 1629-1636

2012

DE BOCK, K., VAN DEN POEL, D. (2012) . Reconciling Performance and Interpretability in Customer Churn Prediction Modeling Using Ensemble Learning Based on Generalized Additive Models, Expert Systems with Applications, 39 (8), 6816-6826

2011

DE BOCK, K., VAN DEN POEL, D. (2011) . An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction, Expert Systems with Applications, 38 (10), 12293-12301

2010

DE BOCK, K., COUSSEMENT, K., VAN DEN POEL, D. (2010) . Ensemble Classification Based on Generalized Additive Models, Computational Statistics and Data Analysis, 54 (6), 1535-1546

DE BOCK, K., VAN DEN POEL, D. (2010) . Predicting website audience demographics for web advertising targeting using multi-website clickstream data, Fundamenta Informaticae, 98 (1), 49-70