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Département
Département
Département Marketing
Domaine(s) d’enseignement
Domaine d’enseignement
Marketing
Domaine(s) de recherche
Domaine de recherche
Marketing
Recherche opérationnelle
Systèmes d'information
Biographie
Biographie
Koen W. DE BOCK est titulaire d'un doctorat de l'Université de Gand (Belgique) et est professeur titulaire en marketing analytique et marketing digital à Audencia. Il a été professeur invité à Vlerick (Belgique), TIAS (Pays-Bas) et à la Stellenbosch Business School (Afrique du Sud). Ses recherches portent sur les applications de l'IA prédictive, de l'apprentissage automatique (ML), de l'IA explicable (xAI) et de l'IA générative (genAI) dans le marketing, la gestion de la relation client (CRM) et la gestion des risques. Le Dr. De Bock a collaboré avec des entreprises multinationales dans divers secteurs et a publié dans des revues académiques de premier plan, notamment Information Sciences, Decision Support Systems, Journal of Business Research, European Journal of Operational Research, et Annals of Operations Research. Récemment, il a été rédacteur en chef invité pour des numéros spéciaux du Journal of Business Research, European Journal of Operational Research, et Annals of Operations Research. Il est rédacteur senior de la revue Applied Operations & Analytics.
Section CV
Formation
Ph.D., Applied Economics
University of Ghent, Gand
(2010)
M.Sc., Marketing Analytics
University of Ghent, Gand
(2006)
M.Sc., Applied Economics
University of Antwerp, Anvers
(2005)
B.Sc., Applied Economics
University of Antwerp, Antwerp
(2003)
Expériences professionnelles
Professeur associé
IESEG School of Management, France
2015 - 2016
Co-fondateur et responsable ESEG Center for Marketing Analytics (ICMA)
IESEG School of Management, France
2011 - 2016
Professeur assistant
IESEG School of Management, France
2010 - 2015
Assistant Recherche et Enseignement
University of Ghent, Ghent, Belgique
2006 - 2010
Visiting out
Depuis 2013
TIAS School of Business & Society, : Professeur invité
Depuis 2011
Stellenbosch Business School, : Professeur invité
2017 - 2017
Southwestern University of Finance and Economics (SWUFE), Chine (RPC) : Professeur invité
2017 - 2017
TIAS School of Business & Society, : Visiting Teaching
2017 - 2017
Stellenbosch Business School, : Visiting Teaching
2018 - 2018
TIAS School of Business & Society, : Visiting Teaching
2017 - 2017
TIAS School of Business & Society, : Visiting Teaching
2018 - 2018
Stellenbosch Business School, : Visiting teaching
2019 - 2019
TIAS School of Business & Society, : Visiting teaching
2019 - 2019
TIAS School of Business & Society, : Visiting teaching
2020 - 2020
TIAS School of Business & Society, : Visiting teaching
2020 - 2020
TIAS School of Business & Society, : Visiting teaching
2020 - 2020
Stellenbosch Business School, : Visiting teaching
2022 - 2022
TIAS School of Business & Society, : Visiting teaching
2022 - 2022
TIAS School of Business & Society, : Visiting teaching
2022 - 2022
TIAS School of Business & Society, : Visiting teaching
Publications
Publié
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.
À paraître
LIU, Z., DE BOCK, K., ZHANG, L. (2024). Explainable Profit-Driven Hotel Booking Cancellation Prediction based on Heterogeneous Stacking-Based Ensemble Classification. European Journal of Operational Research
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.
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., CHOI, T.-M. (2026) Cost-sensitive and Multicriteria Business Failure Prediction. Elsevier
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. 32nd European Conference on Operational Research (EURO 2022).
DE BOCK, K. (2021). Spline-Rule Ensemble Classifiers for Comprehensible Marketing and Risk Analytics. 31st European Conference on Operational Research (EURO 2021).
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). 31st European Conference on Operational Research (EURO 2021).
DE BOCK, K. (2021). Pursuing Interpretability in Business Analytics with Spline-Rule Ensemble Models. Analytics for Management and Economics Conference (AMEC 2021).
DE BOCK, K. (2020). Controlling for clicks: Integrating Digital metrics in Multichannel Retail Chain Store Efficiency Analytics. Audencia Marketing Department Research Seminar.
DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2020). Customer Life Event Prediction Using Deep Learning. 34th Annual Conference of the Belgian Operational Research Society (ORBEL).
DE CAIGNY, A., COUSSEMENT, K., DE BOCK, K. (2019). Customer Life Event Prediction. 30th European Conference on Operational Research (EURO 2019).
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. 2019 Thought Leadership Conference on Metrics and Analytics in Retailing.
CIOBANU, C., COUSSEMENT, K., DE BOCK, K. W. (2018). Efficiency in multi-channel retail chain store: a two-stage DEA approach with environmental factors and e-commerce indicators. 29th European Conference on Operational Research (EURO 2018).
DE CAIGNY, A., COUSSEMENT, K., & DE BOCK, K. W. (2018). Integrating textual information in customer churn prediction models: A deep learning approach., 29th European Conference on Operational Research (EURO 2018).
KARADAYI ATAS, P., DE BOCK, K. W., & OZOGUR-AKYUZ, S. (2018). A Novel Ensemble Pruning Approach for ANN-based Churn Prediction Ensemble Models., 29th European Conference on Operational Research (EURO 2018).
CIOBANU, C., COUSSEMENT, K., & DE BOCK, K. W. (2018). A two-stage DEA approach for multi-channel retail chain store efficiency analysis., International Conference on Data Envelopment Analysis (DEA40).
GEUENS, S., DE BOCK, K. W., & COUSSEMENT, K. (2018). Beyond clickthrough rate: measuring the true impact of personalized e-mail product recommendations., Business Analytics for Finance and Industry (BAFI) Conference 2018.
DE CAIGNY, A., COUSSEMENT, K., & DE BOCK, K. W. (2018). Leaf modeling: An application in customer churn prediction., 21st Conference of the International Federation of Operational Research Societies (IFORS 2017).
GEUENS, S., COUSSEMENT, K., & DE BOCK, K. W. (2018). An Evaluation Framework for Collaborative Filtering on Purchase Information in Recommendation Systems., 2nd Conference on Business Analytics in Finance and Industry (BAFI 2015).
DEBRULLE, J., STEFFENS, P., DE WINNE, S., DE BOCK, K. W., MAES, J., & SELS, L. (2018). Exploring the deeper grounds of new venture performance: Adopting rule ensembles to identify configurations of founder resources, business strategy, and environmental conditions., Australian Centre for Entrepreneurship Research Exchange (ACERE) 2018.
DE CAIGNY, A., COUSSEMENT, K., & DE BOCK, K. W. (2017). A New Algorithm for Segmented Modeling: An Application in Customer Churn Prediction., INFORMS Annual Meeting 2017.
GEUENS, S., COUSSEMENT, K., DE BOCK, K. W. (2016). Towards better online personalization: a framework for empirical evaluation and real-life validation of hybrid recommendation systems. World Marketing Congress of the Academy of Marketing Science.
DE BOCK, K. W. (2016). Enhancing rule ensembles with smoothing splines and constrained feature selection: an application in bankruptcy prediction., 28th European Conference on Operational Research (EURO 2016).
DE BOCK, K. W. (2015). The Black Box Revelation: An Empirical Evaluation of Rule Ensembles for Bankruptcy Prediction., 2nd Conference on Business Analytics in Finance and Industry (BAFI 2015).
DE BOCK, K. W., GEUENS, S., & COUSSEMENT, A. (2015). Integrating Behavioral, Product, and Customer Data in Hybrid Recommendation Systems Based on Factorization Machines., 2nd Conference on Business Analytics in Finance and Industry (BAFI 2015).
DE BOCK, K. W. (2015). Multi-Criteria-Optimized Rule Extraction For Artificial Neural Networks and Its Application In Customer Scoring., 27th European Conference on Operational Research (EURO 2015).
BAUMANN, A., LESSMANN, S., COUSSEMENT, K., & DE BOCK, K. W. (2015). Maximize what matters: Predicting customer churn with decision-centric ensemble selection., 23rd European Conference on Information Systems (ECIS'15).
GEUENS, S., COUSSEMENT, K., & DE BOCK, K. W. (2014). Evaluating Collaborative Filtering: Methods within a Binary Purchase Setting., 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD).
DE BOCK, K. W., LESSMANN, S., & COUSSEMENT, K. (2014). Multicriteria optimization for cost-sensitive ensemble selection in business failure prediction., 20th Conference of the International Federation of Operational Research Societies (IFORS 2014).
DE BOCK, K. W. (2013). Deploying Dynamic Ensemble Selection To Tackle Concept Drift in Predictive Customer Analytics., 26th European Conference on Operational Research (EURO 2013).
DEBRULLE, J., DE BOCK, K. W., DE WINNE, S., & SELS, L. (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., Babson College Entrepreneurship Research Conference (BCERC 2013).
DE BOCK, K. W., & COUSSEMENT, K. (2012). Remedying the Expiration of Churn Prediction Models with Multiple Classifier Algorithms., INFORMS Marketing Science 2012.
COUSSEMENT, K., LESSMANN, S., & DE BOCK, K. W. (2012). Ensemble Selection for Churn Prediction in the Telecommunications Industry., INFORMS Marketing Science 2012.
DE BOCK, K. W., & VAN DEN POEL, D. (2011). Strategies for Extracting Knowledge from Ensemble Classifiers Based on Generalized Additive Models., 2011 Joint Statistical Meeting (JSM 2011).
DE BOCK, K. W., & VAN DEN POEL, D. (2010). Ensemble Classification based on Generalized Additive Models., 2010 Joint Statistical Meeting (JSM 2010).
DE BOCK, K. W., & VAN DEN POEL, D. (2010). Customer Churn Prediction using Ensemble Classifiers based on Generalized Additive Models., 34th Annual Conference of the German Classifiction Society (GfKI).
DE BOCK, K. W., & VAN DEN POEL, D. (2010). Ensembles of probability estimation trees for customer churn prediction., 23rd International Conference for Industrial Engi,neering and other Applications of Applied Intelligent Systems (IEA-AIE 2010).
DE BOCK, K. W., & VAN DEN POEL, D. (2009). Demographic Classification of Anonymous Web Site Visitors Using Click Stream Information: A Practical Method for Supporting Online Advertising., 2009 Joint Statistical Meetings (JSM 2009).
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