photo_cv
Department
Département
Marketing
Teaching areas
Domaine d’enseignement
Digital Marketing
Marketing Analytics
Research areas
Domaine de recherche
Digital Marketing
Marketing Analytics
Explainable AI
AI and Machine Learning
AI-supported and Data-Driven Marketing and CRM
Predictive and Causal Analytics
Skills
Digital Marketing
Digital Advertising (SMA, SEA, Display)
Web Analytics
AI & Machine Learning
Explainable AI (XAI)
Predictive & Causal Analytics
Language models (SLM, LLM)
Biography
Biographie
Dr. Koen W. De Bock is a Full Professor of Marketing Analytics and Digital Marketing at Audencia Business School in Nantes, France. He is also Head of Research at Audencia's marketing department. Koen holds a PhD in Applied Economics from Ghent University (2010), an MSc in Marketing Analytics from Ghent University (2006), and both an MSc and BSc in Applied Economics from the University of Antwerp (2005, 2003).
Prior to his current role, Dr. De Bock served as Assistant Professor (2010–2015) and Associate Professor (2015–2016) at IÉSEG School of Management, where he co-founded and managed the IÉSEG Center for Marketing Analytics (ICMA). He has also been a visiting professor at institutions such as Vlerick Business School (Belgium), TIAS School for Business & Society (Netherlands), Stellenbosch Business School (South Africa), and Southwestern University of Finance and Economics (SWUFE) in China.
His research focuses on the applications of predictive AI, machine learning, explainable AI (XAI), and generative AI (GenAI) in marketing, customer relationship management (CRM), and risk management. He has published extensively in leading academic journals—including Information Sciences; Decision Support Systems; Journal of Business Research; European Journal of Operational Research; and Annals of Operations Research—and recently served as managing guest editor for multiple special journal issues. Dr. De Bock is also Senior Editor of the journal Applied Operations & Analytics.
Dr. De Bock welcomes inquiries from prospective PhD students interested in areas like AI-driven marketing analytics, predictive modeling, customer intelligence, and explainable AI.
He additionally invites company and industry partners to collaborate on applied research projects, consultancy, or innovation initiatives aimed at leveraging advanced analytics and AI in marketing, CRM, and risk management.
Visit the personal website of Koen W. De Bock, connect with Koen W. De Bock on LinkedIn or via e-mail.
Diplomas
Ph.D., Applied Economics
Ghent University, Ghent
(2010)
M.Sc., Marketing Analytics
Ghent University, Ghent
(2006)
M.Sc., Applied Economics
University of Antwerp, Antwerp
(2005)
B.Sc., Applied Economics
University of Antwerp, Antwerp
(2003)
Experiences
Associate Professor of Marketing Analytics & Digital Marketing
Audencia Business School, Nantes
2017 - 2019
Associate Professor of Marketing Analytics & Digital Marketing
IESEG School of Management, Lille
2015 - 2016
Co-founder and manager of the IESEG Center for Marketing Analytics (ICMA)
IESEG School of Management, Lille
2011 - 2016
Assistant Professor of Marketing
IESEG School of Management, Lille
2010 - 2015
Research and Teaching assistant
Ghent University, Ghent
2006 - 2010
Visiting out
2017 - 2018
Stellenbosch Business School, : Visiting professor (MBA)
2017 - 2026
TIAS School of Business & Society, : Visiting professor (MBA, IMScBA)
2017 - 2017
Southwestern University of Finance and Economics (SWUFE), China (PRC) : Visiting professor (DBA)
Publications
Published
CIELEN D., DE BOCK K., FLORES L., (2026). Exploring Zero-Shot SLM Ensembles as an Alternative to LLMs for Sentiment Analysis.
Information Fusion, 126 Part B (February 2026), 103666
LIU Z., DE BOCK K., ZHANG L., (2025). Explainable Profit-Driven Hotel Booking Cancellation Prediction based on Heterogeneous Stacking-Based Ensemble Classification.
European Journal of Operational Research, 321, 284-301
DE BOCK K., BOGAERT M., DU JARDIN P., (2025). Ensemble Learning for Operations Research and Business Analytics.
Annals of Operations Research, 353 (October 2025), 419-448
MENA G., COUSSEMENT K., DE BOCK K., DE CAIGNY A., LESSMANN S., (2024). Exploiting Time-Varying RFM Measures for Customer Churn Prediction with Deep Neural Networks.
Annals of Operations Research, 339 (1), 765-787
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
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., DOUMPOS, M., VERBEKE, W., ZOPOUNIDIS, C. (2026). Cost-sensitive and Multicriteria Business Failure Prediction. In Choi, T.-M. (Eds.), Encyclopedia of Operations Management. Elsevier.
DE BOCK, K. W., COUSSEMENT, K., CIELEN, D. (2018). An Overview of Multiple Classifier Systems Based on Generalized Additive Models. In 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. In 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)). In 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. In 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. In 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. In 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. In 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.
DE LANGE S., BOGAERT M., DE BOCK K., VAN DEN POEL D., (2025). The dual quest for interpretability and performance in credit scoring via spline-rule ensembles.
The Belgian Operational Research Society, 144-145
BERK AYTAÇ E., OZOGUR-AKYUZ S., DE BOCK K., (2024). Interpretable Ensemble Learners for Explainable Business Analytics.
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 BOCK K., (2020). Controlling for clicks: Integrating Digital metrics in Multichannel Retail Chain Store Efficiency Analytics.
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.
Scientific activities
Member of the scientific board of an academic association
41st International Conference of the French Marketing Association
2024
Member of the editorial board of an academic journal
Senior editor at Applied Operations and Analytics
2024
Editorial board member at Discover Analytics
2023
Guest Editor of an academic journal
Journal of Business Research: Special Issue on Applications of Machine Learning and Generative AI in Shaping the Future of Marketing
2023
Annals of Operations Research: Special Issue on Ensemble Learning for Operations Research & Business Analytics
2023
European Journal of Operational Research: Special Issue on Explainable Analytics for Operational Research
2022
Awards and honors
Best paper award at the 57th Hawaii International Conference on System Sciences (HICSS 2024)Best paper award for Coupling Neural Networks Between Clusters for Better Personalized Care at the 57th Hawaii International Conference on System Sciences (HICSS 2024), 2024
European Journal of Operational Research - 2022 Editors’ Award for Excellence in Reviewing, 2022
European Journal of Operational Research - 2020 Editor's Award for Excellence in Reviewing, 2020
Doctoral supervision
(2025) S. DE LANGE : Explainable AI for business analytics (ongoing).
(2023) D. CIELEN : Enhancing marketing analytics with generative AI (ongoing).
(2025) Y. HE : Explainable AI through counterfactual explanations and narratives.
(2023) K. IDBENJRA : Essays on Segmented-Modeling Approaches for BusinessAnalytical Applications.