photo_cv
Département
Département
Département Marketing
Section CV
Formation
Ph.D., Applied Economics
University of Ghent, Gand 2010M.Sc., Marketing Analytics
University of Ghent, Gand 2006M.Sc., Applied Economics
University of Antwerp, Anvers 2005B.Sc., Applied Economics
University of Antwerp, Antwerp 2003Expériences professionnelles
Professeur associé
IESEG School of Management, France 2015 - 2016Co-fondateur et responsable ESEG Center for Marketing Analytics (ICMA)
IESEG School of Management, France 2011 - 2016Professeur assistant
IESEG School of Management, France 2010 - 2015Assistant Recherche et Enseignement
University of Ghent, Ghent, Belgique 2006 - 2010Publications
Publié
DE BOCK, K., Coussement, K., Caigny, A. D., Slowiński, R., Baesens, B., Boute, R. N., Choi, T.-M., Delen, D., Kraus, M., Lessmann, S., Maldonado, S., Martens, D., Óskarsdóttir, M., Vairetti, C., Verbeke, W., Weber, R. (2024). Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda. European Journal of Operational Research, 317 (2), 249-272.
À paraître
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
Publié
LIU, Z., PING, J., DE BOCK, K., WANG, J., ZHANG, L., NIU, X. (2024). Extreme Gradient Boosting Trees with Efficient Bayesian Optimization for Profit-Driven Customer Churn Prediction. Technological Forecasting and Social Change, 198 (January 2024), 122945.
Dwivedi, Y. K., Balakrishnan, J., Mishra, A., DE BOCK, K., Al-Busaidi, A. S. (2024). The role of embodiment, experience, and self-image expression in creating continuance intention in the metaverse. Technological Forecasting and Social Change, 203, 123402.
De Caigny, A., DE BOCK, K., Verboven, S. (2024). Hybrid black-box classification for customer churn prediction with segmented interpretability analysis. Decision Support Systems, 181, 114217.
DE BOCK, K., COUSSEMENT, K., DE CAIGNY, A. (2024). Explainable Analytics for Operational Research. European Journal of Operational Research, 317 (2), 243-248.
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.
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.
LESSMANN, Stefan, HAUPT, Johannes, COUSSEMENT, Kristof, DE BOCK, K. (2021). Targeting customers for profit: An ensemble learning framework to support marketing decision-making. Information Sciences, 557 (May 2021), 286-301.
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.
DE CAIGNY, Arno, COUSSEMENT, Kristof, DE BOCK, K., LESSMANN, Stefan (2020). Incorporating Textual Information in Customer Churn Prediction Models Based on a Convolutional Neural Network. International Journal of Forecasting, 36 (4), 1563-1578.
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.
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.
GEUENS, Stijn, COUSSEMENT, Kristof, DE BOCK, K. W. (2018). A framework for configuring collaborative filtering-based recommendations derived from purchase data. European Journal of Operational Research, 265 (1), 208-218.
DE CAIGNY, Arno, COUSSEMENT, Kristof, DE BOCK, K. W. (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.
DE BOCK, K. W. (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.
COUSSEMENT, Kristof, VAN DEN BOSSCHE, Filip, DE BOCK, K. W. (2014). Data Accuracy’s Impact on Segmentation Performance: Comparing RFM, Logistic Regression and Decision Trees. Journal of Business Research, 67 (1), 2751–2758.
COUSSEMENT, Kristof, DE BOCK, K. W. (2013). Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning. Journal of Business Research, 66 (9), 1629-1636.
DE BOCK, K. W., VAN DEN POEL, Dirk (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.
DE BOCK, K. W., VAN DEN POEL, Dirk (2011). An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems with Applications, 38 (10), 12293-12301.
DE BOCK, K. W., COUSSEMENT, Kristof, VAN DEN POEL, Dirk (2010). Ensemble Classification Based on Generalized Additive Models. Computational Statistics and Data Analysis, 54 (6), 1535-1546.
DE BOCK, K. W., VAN DEN POEL, Dirk (2010). Predicting website audience demographics for web advertising targeting using multi-website clickstream data. Fundamenta Informaticae, 98 (1), 49-70.
DE BOCK, K. W., COUSSEMENT, Kristof, NESLIN, Scott (2014). Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships (translated in simplified Chinese). The China Enterprise Management Publishing House
DE BOCK, K. W., COUSSEMENT, Kristof, NESLIN, Scott (2013). Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships. Routledge
DE BOCK, K. W., COUSSEMENT, Kristof, CIELEN, Davy (2018). An Overview of Multiple Classifier Systems Based on Generalized Additive Models. John Wiley & Sons
FLORES, Laurent, DE BOCK, K. W. (2018). L’analyse des données appliquée à la publicité. Dunod
DE BOCK, K. W., COUSSEMENT, Kristof (2016). Special Session: Big Data Analytics for Marketing (Contributed Session by the IÉSEG Center for Marketing Analytics (ICMA)). Springer
BOUJENA, Othman, COUSSEMENT, Kristof, DE BOCK, K. W. (2015). Data Driven Customer Centricity: CRM Predictive Analytics. IGI Global
DE BOCK, K. W., COUSSEMENT, Kristof (2013). Ensemble Learning in Database Marketing. Routledge
COUSSEMENT, Kristof, DE BOCK, K. W. (2013). Text Mining for Database Marketing. Routledge
DE BOCK, K. W., VAN DEN POEL, Dirk (2010). Ensembles of probability estimation trees for customer churn prediction. Springer, 57-66.
PHAN, T. H. M., COUSSEMENT, K., DE BOCK, K., DE CAIGNY, A. (2022). Modeling with Hybrid Segmentation Methods: A Statistical Library for R and Python.
DE BOCK, K. (2021). Spline-Rule Ensemble Classifiers for Comprehensible Marketing and Risk Analytics.
DE BOCK, K., DE CAIGNY, A., COUSSEMENT, K. (2021). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (EJOR 2018): A review and update (invited talk).
DE BOCK, K. (2021). Pursuing Interpretability in Business Analytics with Spline-Rule Ensemble Models.
DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2020). Customer Life Event Prediction Using Deep Learning.
DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2019). Customer Life Event Prediction.
DE BOCK, K., COUSSEMENT, K., DE CAIGNY, A., CIOBANU, C. (2019). Integrating E-commerce Indicators in Multichannel Retail Chain Store Efficiency Analyses: A Robust Two-stage DEA Approach.
COUSSEMENT, Kristof, DE BOCK, K. W., CIOBANU, Cristina (2018). Efficiency in multi-channel retail chain store: a two-stage DEA approach with environmental factors and e-commerce indicators.
COUSSEMENT, Kristof, DE BOCK, K. W., DE CAIGNY, Arno (2018). Integrating textual information in customer churn prediction models: A deep learning approach.
DE BOCK, K. W., KARADAYI ATAS, Pinar, OZOGUR-AKYUZ, Süreyya (2018). A Novel Ensemble Pruning Approach for ANN-based Churn Prediction Ensemble Models.
CIOBANU, Cristina, COUSSEMENT, Kristof, DE BOCK, K. W. (2018). A two-stage DEA approach for multi-channel retail chain store efficiency analysis.
GEUENS, Stijn, DE BOCK, K. W., COUSSEMENT, Kristof (2018). Beyond clickthrough rate: measuring the true impact of personalized e-mail product recommendations.
COUSSEMENT, Kristof, DE BOCK, K. W., DE CAIGNY, Arno (2018). Leaf modeling: An application in customer churn prediction.
COUSSEMENT, Kristof, DE BOCK, K. W., GEUENS, Stijn (2018). An Evaluation Framework for Collaborative Filtering on Purchase Information in Recommendation Systems.
DEBRULLE, Jonas, STEFFENS, Paul, DE WINNE, S., DE BOCK, K. W., MAES, Johan, SELS, Luc (2018). Exploring the deeper grounds of new venture performance: Adopting rule ensembles to identify configurations of founder resources, business strategy, and environmental conditions.
COUSSEMENT, Kristof, DE BOCK, K. W., DE CAIGNY, Arno (2017). A New Algorithm for Segmented Modeling: An Application in Customer Churn Prediction.
GEUENS, Stijn, COUSSEMENT, Kristof, DE BOCK, K. W. (2016). Towards better online personalization: a framework for empirical evaluation and real-life validation of hybrid recommendation systems.
DE BOCK, K. W. (2016). Enhancing rule ensembles with smoothing splines and constrained feature selection: an application in bankruptcy prediction.
DE BOCK, K. W. (2015). The Black Box Revelation: An Empirical Evaluation of Rule Ensembles for Bankruptcy Prediction.
GEUENS, Stijn, COUSSEMENT, A., DE BOCK, K. W. (2015). Integrating Behavioral, Product, and Customer Data in Hybrid Recommendation Systems Based on Factorization Machines.
DE BOCK, K. W. (2015). Multi-Criteria-Optimized Rule Extraction For Artificial Neural Networks and Its Application In Customer Scoring.
BAUMANN, Annika, LESSMANN, Stefan, COUSSEMENT, Kristof, DE BOCK, K. W. (2015). Maximize what matters: Predicting customer churn with decision-centric ensemble selection.
COUSSEMENT, Kristof, DE BOCK, K. W., GEUENS, Stijn (2014). Evaluating Collaborative Filtering: Methods within a Binary Purchase Setting.
DE BOCK, K. W., LESSMANN, Stefan, COUSSEMENT, Kristof (2014). Multicriteria optimization for cost-sensitive ensemble selection in business failure prediction.
DE BOCK, K. W. (2013). Deploying Dynamic Ensemble Selection To Tackle Concept Drift in Predictive Customer Analytics.
DE BOCK, K. W., DEBRULLE, Jonas, DE WINNE, S., SELS, Luc (2013). Getting Off On The Right Foot: Identifying Persistent Configurations Of Initial Resources, Strategy And Environment That Enable Start-Ups To Achieve A Sustainable Competitive Advantage.
DE BOCK, K. W., COUSSEMENT, Kristof (2012). Remedying the Expiration of Churn Prediction Models with Multiple Classifier Algorithms.
LESSMANN, Stefan, DE BOCK, K. W., COUSSEMENT, Kristof (2012). Ensemble Selection for Churn Prediction in the Telecommunications Industry.
DE BOCK, K. W., VAN DEN POEL, Dirk (2011). Strategies for Extracting Knowledge from Ensemble Classifiers Based on Generalized Additive Models.
DE BOCK, K. W., VAN DEN POEL, Dirk (2010). Ensemble Classification based on Generalized Additive Models.
DE BOCK, K. W., VAN DEN POEL, Dirk (2010). Customer Churn Prediction using Ensemble Classifiers based on Generalized Additive Models.
DE BOCK, K. W., VAN DEN POEL, Dirk (2010). Ensembles of probability estimation trees for customer churn prediction.
DE BOCK, K. W., VAN DEN POEL, Dirk (2009). Demographic Classification of Anonymous Web Site Visitors Using Click Stream Information: A Practical Method for Supporting Online Advertising.
DE BOCK, K. (2020). Controlling for clicks: Integrating Digital metrics in Multichannel Retail Chain Store Efficiency Analytics.
Supervision doctorat
Depuis 2023, D. CIELEN : Amélioration de l'analytique marketing prédictive avec l'IA générative, Université Paris II Panthéon Assas, France
Depuis 2023, K. IDBENJRA : Essais sur les Approches de Modélisation Segmentée pour lesApplications Analytiques des Affaires, , France