1. Published journal articles

  • Dwivedi, Y.K., Balakrishnan, J., Mishra, A., De Bock, K.W., Al-Busaidi, A., 2024, The Role of Embodiment, Experience, and Self-Image Expression in Creating Continuance Intention in the Metaverse, Forthcoming at Technological Forecasting and Social Change.
  • De Caigny, A., De Bock, K.W., Verboven, S., 2024, Hybrid Black-Box Classification for Customer Churn Prediction
    with Segmented Interpretability Analysis    , Forthcoming at Decision Support Systems.
  • De Bock, K.W., Coussement, K., De Caigny, A., 2024, Editorial to the Special Issue: Explainable AI for Operational Research, Forthcoming at European Journal of Operational Research. 
  • De Bock, K.W., Coussement, K., De Caigny, A., Słowiński, 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, Forthcoming at European Journal of Operational Research. Link.
  • Liu, Z., Ping, J., De Bock, K.W., Wang, J., Zhang, L., Niu, X., 2023, Extreme Gradient Boosting Trees with Efficient Bayesian Optimization for Profit-Driven Customer Churn Prediction. Forthcoming at Technological Forecasting and Social Change. Link.
  • Mena, C.G, Coussement, K., De Bock, K.W., De Caigny, A., and Lessmann, S., 2023, Exploiting Time-Varying RFM Measures for Customer Churn Prediction with Deep Neural Networks. Forthcoming at Annals of Operations Research. Link.
  • De Bock, K.W. and De Caigny, A., 2021, Spline-Rule Ensemble Classifiers with Structured Sparsity Regularization for Interpretable Customer Churn Modeling. Forthcoming at Decision Support Systems.
  • Coussement, K., De Bock, K.W. and Geuens, S., 2021, A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer. Forthcoming at Annals of Operations Research.
  • Debrulle, J., Steffens, P., De Bock, K.W., De Winne, S. and Maes, J., 2021, Configurations of Business Founder Resources, Strategy, and Environment Determining New Venture Performance. Journal of Small Business Management.
  • Lessmann, S., Haupt, J., Coussement, K., and De Bock, K.W., 2019 (forthcoming), Targeting customers for profit: An ensemble learning framework to support marketing decision making. Information Sciences. Link.
  • De Bock, K.W., Lessmann, S. And Coussement, K., 2020, Cost-Sensitive Business Failure Prediction When Misclassification Costs Are Uncertain: a Heterogeneous Ensemble Selection Approach. European Journal of Operational Research, 285 (2), pp. 612-630. Link.
  • De Caigny, A., Coussement, K. and De Bock, K.W., 2020, Leveraging Fine-Grained Transaction Data for Customer Life Event Prediction. Decision Support Systems, 130 (March 2020). Link.
  • De Caigny, A., Coussement, K. and De Bock, K.W. Lessmann, S., 2019 (forthcoming), Incorporating Textual Information in Customer Churn Prediction Models Based on a Convolutional Neural Network. International Journal of Forecasting. Link.
  • De Caigny, A., Coussement, K. and 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), pp. 760-772. Link.
  • Geuens, S., Coussement, K. and De Bock, K.W., 2018, A framework for configuring collaborative filtering-based recommendations derived from purchase data. European Journal of Operational Research, 265 (1), pp. 208-218. Link.
  • De Bock, K.W., 2017, The Best of Two Worlds: Balancing Model Strength and Comprehensibility in Business Failure Prediction Using Rule Ensembles. Expert Systems With Applications, 60, pp. 23-39. Link.
  • Coussement, K., Van Den Bossche, F. A. M. and 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), pp.  2751–2758. Link.
  • Coussement, K. and 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), pp. 1629–1636. Link.
  • De Bock, K.W. and 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), pp. 6816–6826. Link.
  • De Bock, K.W. and Van den Poel, D., 2011, An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction, Expert Systems With Applications, 38 (10), pp. 12293-12301. Link.
  • De Bock, K.W. Coussement, K. and Van den Poel, D., 2010, Ensemble Classification Based on Generalized Additive Models, Computational Statistics & Data Analysis, 54 (6), pp. 1535-1546. Link.
  • De Bock, K.W. and Van den Poel, D., 2010, Predicting website audience demographics for web advertising targeting using multi-website clickstream data, Fundamenta Informaticae, 98 (1), pp. 49-97. Link.

2. Books

  • Coussement, K., De Bock, K.W. and Neslin, S.A., 2013, Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships, Routledge, London, UK. More information can be found via Routledge’s web site.
    • The book has been translated in simplified Chinese and published by The China Enterprise Management Publishing House, Beijing (China).
  • De Bock, K.W., 2010, Enhancing Database Marketing with Ensemble Learning, Ph.D. Thesis, University Press.

3. Book chapters

  • De Bock, K.W., Coussement, K. and 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.), 2018, Ensemble Classification Methods with Applications in R. Wiley & Sons, New York, USA (link).
  • Flores, L. and De Bock, K.W., 2018, L’analyse des données appliquée à la publicité. In: Allary, J. et Balusseau, V. (Eds.), 2018, La publicité à l’heure de la data – Adtech et programmatique expliquées par les experts, Dunod, Paris, France (link)
  • De Bock K.W., Coussement K., 2017, 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 Academy of Marketing Science. Springer, Cham, USA.
  • Boujena, O., Coussement, K. and De Bock, K.W., 2015, Data Driven Customer Centricity: CRM Predictive Analytics, in T. Tsiakis (Ed.), Handbook of Research on Innovations in Marketing Information Systems, IGI Global Publishing, Pennsylvania, USA.
  • De Bock, K.W. and 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, Gower/Ashgate, London, UK.
  • Coussement, K. and 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, Gower/Ashgate, London, UK.
  • De Bock, K.W. and Van den Poel, D., 2010, Ensembles of probability estimation trees for customer churn prediction, Lecture Notes in Artificial Intelligence, 6097, pp. 57-66.

4. Communications and presentations in refereed conferences

  • Kraus, M., Hambauer, N., Müller, K., Kröckel, P., Ulapane, N., De Caigny, A., De Bock, K.W., Coussement, K., 2024, Coupling Neural Networks Between Clusters for Better Personalized Care, Proc. Hawaii International Conference on System Sciences (HICSS), Waikiki, USA.
  • Hambauer, N., Kraus, M., Coussement, K., De Bock, K.W. and De Caigny, A., 2023, Introducing LLM GAMs: Model performance, interpretability, and sparsity in marketing analytics, Proc. 37th Annual Conference of the Belgian Operational Research Society (ORBEL 37), Liège, Belgium.
  • Phan, T. H. M., Coussement, K., De Bock, K.W. and De Caigny, A., 2023, Hybrid segmentation approaches for supervised learning in R, Proc. 37th Annual Conference of the Belgian Operational Research Society (ORBEL 37), Liège, Belgium.
  • Phan, T. H. M., Coussement, K., De Bock, K.W. and De Caigny, A., 2022, Modeling with Hybrid Segmentation Methods: A Statistical Library for R and Python, Proc. 32nd European Conference on Operational Research (EURO 2022), Espoo, Finland.
  • De Bock, K.W., 2021, Pursuing Interpretability in Business Analytics with Spline-Rule Ensemble Models, Proc. Analytics for Management and Economics Conference (AMEC 2021), Saint-Petersburg, Russian Federation (online event).
  • De Bock, K.W., 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), Proc. 31st European Conference on Operational Research (EURO 2021), Athens, Greece (online event).
  • De Bock, K.W., 2021, Spline-Rule Ensemble Classifiers for Comprehensible Marketing and Risk Analytics, Proc. 31st European Conference on Operational Research (EURO 2021), Athens, Greece (online event).
  • De Caigny, A., Coussement, K. and De Bock, K.W., 2020, Customer Life Event Prediction Using Deep Learning, In Proceedings of the 34th Annual Conference of the Belgian Operational Research Society (ORBEL 36), Lille, France.
  • De Bock, K.W., Coussement, K., De Caigny, A. and Ciobanu, C., 2019, Integrating E-commerce Indicators in Multichannel Retail Chain Store Efficiency Analyses: A Robust Two-stage DEA Approach. Proc. 2019 Thought Leadership Conference on Metrics and Analytics in Retailing, Atlanta, USA.
  • De Caigny, A., Coussement, K. and De Bock, K.W., 2019, Customer Life Event Prediction, Proc. 30th European Conference on Operational Research (EURO 2019), Dublin, Ireland.
  • Ciobanu, C., Coussement, K. and De Bock, K.W., 2018, Efficiency in multi-channel retail chain store: a two-stage DEA approach with environmental factors and e-commerce indicators. Proc. 29th European Conference on Operational Research (EURO 2018), Valencia, Spain.
  • De Caigny, A., Coussement, K. and De Bock, K.W., 2018, Integrating textual information in customer churn prediction models: A deep learning approach. Proc. 29th European Conference on Operational Research (EURO 2018), Valencia, Spain.
  • Ozogur-Akyuz, S., De Bock, K.W. and Karadayi Atas, P., 2018, A Novel Ensemble Pruning Approach for ANN-based Churn Prediction Ensemble Models. Proc. 29th European Conference on Operational Research (EURO 2018), Valencia, Spain.
  • Ciobanu, C., Coussement, K. and De Bock, K.W., 2018, A two-stage DEA approach for multi-channel retail chain store efficiency analysis. Proc. International Conference on Data Envelopment Analysis (DEA40), Birmingham, United Kingdom.
  • Geuens, S., De Bock, K.W. and Coussement, K., 2018, Beyond clickthrough rate: measuring the true impact of personalized e-mail product recommendations. Proc. Business Analytics for Finance and Industry (BAFI) Conference 2018, Santiago, Chile.
  • Debrulle J., Steffens P., De Winne S., De Bock K.W., Maes J. and 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, Proc. Australian Centre for Entrepreneurship Research Exchange (ACERE) 2018, Brisbane, Australia
  • De Caigny, A., Coussement,K. and De Bock, K.W., 2017, A New Algorithm for Segmented Modeling: An Application in Customer Churn Prediction, Proc. INFORMS Annual Meeting 2017, Houston, TX, USA.
  • De Caigny, A., Coussement, K. and De Bock, K.W., 2017, Leaf modeling: An application in customer churn prediction. Proc. 21st Conference of the International Federation of Operational Research Societies (IFORS 2017), Québec, Canada.
  • Geuens, S., Coussement, K. and De Bock, K.W., 2016, Towards better online personalization: a framework for empirical evaluation and real-life validation of hybrid recommendation systems, Proc. World Marketing Congress of the Academy of Marketing Science, Paris, France.
  • De Bock, K.W., 2016, Enhancing rule ensembles with smoothing splines and constrained feature selection: an application in bankruptcy prediction, Proc. 28th European Conference on Operational Research (EURO 2016), Poznan, Poland.
  • 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), Santiago, Chile.
  • Geuens, S., Coussement, K. and De Bock, K.W., 2015An Evaluation Framework for Collaborative Filtering on Purchase Information in Recommendation Systems, 2nd Conference on Business Analytics in Finance and Industry (BAFI 2015), Santiago, Chile.
  • Geuens, S., Coussement, K. and De Bock, K.W., 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), Santiago, Chile.
  • De Bock, K.W., 2015, Multi-Criteria-Optimized Rule Extraction For Artificial Neural Networks and Its Application In Customer Scoring, Proc. 27th European Conference on Operational Research (EURO 2015), Glasgow, UK.
  • Baumann, A., Lessmann, S., Coussement, K., De Bock, K.W., 2015, Maximize what matters: Predicting customer churn with decision-centric ensemble selection, in Proc. 23rd European Conference on Information Systems (ECIS’15), Münster, Germany.
  • Geuens, S., Coussement, K. and De Bock, K.W., 2014, Evaluating Collaborative Filtering: Methods within a Binary Purchase Setting. ECML/PKDD Conference, Nancy, France.
  • De Bock, K.W., Lessmann, S. And 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) (abstract), Barcelona, Spain.
  • 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) (abstract), Rome, Italy.
  • Debrulle, J., De Bock, K.W., De Winne, S. and 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) (abstract), Lyon, France.
  • De Bock, K.W. and Coussement, K., 2012, Remedying the Expiration of Churn Prediction Models with Multiple Classifier Algorithms. Proc. INFORMS Marketing Science 2012 (abstract), Boston, MA, USA.
  • Coussement, K., De Bock, K.W. and Lessmann, S., Ensemble Selection for Churn Prediction in the Telecommunications Industry. Proc. INFORMS Marketing Science 2012 (abstract), Boston, MA, USA.
  • De Bock, K.W., and Van den Poel, D., 2010, Strategies for Extracting Knowledge from Ensemble Classifiers Based on Generalized Additive Models. Proc. 2011 Joint Statistical Meeting (JSM 2011; ASA) (abstract), Miami, FL, USA.
  • De Bock, K.W., Coussement, K. and Van den Poel, D., 2010, Ensemble Classification based on Generalized Additive Models. Proc. 2010 Joint Statistical Meeting (JSM 2010; ASA) (abstract), Vancouver, Canada.
  • De Bock, K.W. and Van den Poel, D., 2010, Customer Churn Prediction using Ensemble Classifiers based on Generalized Additive Models. Proc. 34th Annual Conference of the German Classification Society (GfKl), Karlsruhe, Germany.
  • De Bock, K.W. and Van den Poel, D., 2010, Ensembles of probability estimation trees for customer churn prediction, Proc. 23rd International Conference for Industrial, Engineering and other Applications of Applied Intelligent Systems (IEA-AIE 2010), Cordoba, Spain.
  • De Bock, K.W. and Van den Poel, D., 2009, Demographic Classification of Anonymous Web Site Visitors Using Click Stream Information: A Practical Method for Supporting Online Advertising, Proceedings of the 2009 Joint Statistical Meetings (JSM 2009), Washington, DC, USA.

5. Other presentations

  • De Bock, K.W., 2020, Controlling for clicks: Integrating Digital metrics in Multichannel Retail Chain Store Efficiency Analytics, Marketing department research seminar (February 2020), Audencia Business School, Nantes France.
  • De Bock, K.W., 2018, Measure, Analyze, Optimize, Repeat: Marketing Analytics to Optimize the Customer journey. Keynote at Business Club – Internationalization Through Digitalization Workshop at VOKA (Flemish Chambers of Commerce and Industry), Ghent, Belgium.
  • De Bock, K.W., 2018, Assessing E-Mail Recommender System Performance Throughout the Purchase Funnel, Marketing department research seminar (February 2018), Audencia Business School, Nantes France.
  • De Bock, K.W., 2016, An Empirical Analysis of the Impact of Data Accuracy on Customer Segmentation Performance, Invited research presentation, Audencia Business School, Nantes, France.
  • Coussement, K. and De Bock, K.W., 2015, Comprendre ce que change vraiment le Big Data, Conférence EACP Marketvox : Etes-vous prêts à accueillir le Big Data, Paris, France.
  • De Bock, K.W., 2015, Organizing online team peer evaluation. Petit déjeuner pédagogique du CAP (Comité Académique et Pédagogique), University Catholique de Lille, Lille, France.
  • De Bock, K.W., 2014, Organizing online team peer evaluation. Pedagogical café, IESEG School of Management, Lille, France.
  • De Bock, K.W. and Coussement, K., 2012, I am Begging You! – Customer Churn Prediction Using Generalized Additive Models, Research Seminar Series of the Center of Excellence on Consumers & Marketing Strategy (CCMS, Louvain School of Management Research Instititute), Université de Namur, Namur, Belgium.
  • De Bock, K.W., 2012, L’Union Fait La Force!  – Recent Developments and Applications of Ensemble Models for Customer Intelligence, SAS Forum France 2012, Paris, France.
  • De Bock, K.W., 2012, Merging models in search for predictive synergy: an introduction to ensemble learning for enhanced customer intelligence, SAS Analytics 2012, Cologne, Germany.
  • De Bock, K.W., 2011, The Future of Internet Marketing is Inbound: On Deploying Search and Word-Of-Mouse To Accomplish Online Marketing Objectives, Beyond Business Borders: USB Alumni Refresher Event, Bellville, Cape Town, South Africa.
  • De Bock, K.W., 2011, When Predictive Models Join Forces: on the How and Why of Ensemble Learning for Customer Intelligence, SAS Forum Belux, Louvain-La-Neuve, Belgium.
  • Coussement, K. and De Bock, K.W., 2011, Please Don’t Go! An Empirical Investigation of Generalized Additive Models for Customer Churn Prediction, LEM Research Day, Lille, France.
  • De Bock, K.W., 2010, Boosting Customer Intelligence: An introduction to ensemble learning for enhanced predictive database marketing, Keynote presentation at the BAQMaR 2010 Conference, Ghent, Belgium.