Koen DE BOCK

Associate Professor

  • Marketing Department

Curriculum vitae

PhD in Applied Economics (Marketing)
University of Ghent, Belgium (2010)

MSc in Marketing Analysis
University of Ghent, Belgium (2006)

MSc in Applied Economics
University of Antwerp, Belgium (2005)

BSc in Applied Economics
University of Antwerp, Belgium (2003)

Publications

Forthcoming

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

2018

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 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

COUSSEMENT, K., DE BOCK, K., & 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., & 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. 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.

CIOBANU, C., COUSSEMENT, K., & DE BOCK, K. (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. (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., & 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. (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., & 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. (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. (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., 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. (2017). A New Algorithm for Segmented Modeling: An Application in Customer Churn Prediction., INFORMS Annual Meeting 2017.

, GEUENS, S., COUSSEMENT, K., & DE BOCK, K. (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. (2016). Enhancing rule ensembles with smoothing splines and constrained feature selection: an application in bankruptcy prediction., 28th European Conference on Operational Research (EURO 2016).

BAUMANN, A., LESSMANN, S., COUSSEMENT, K., & DE BOCK, K. (2015). Maximize what matters: Predicting customer churn with decision-centric ensemble selection., 23rd European Conference on Information Systems (ECIS'15).

DE BOCK, K., 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. (2015). Multi-Criteria-Optimized Rule Extraction For Artificial Neural Networks and Its Application In Customer Scoring., 27th European Conference on Operational Research (EURO 2015).

DE BOCK, K. (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).

GEUENS, S., COUSSEMENT, K., & DE BOCK, K. (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., 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. (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., 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., & 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. (2012). Ensemble Selection for Churn Prediction in the Telecommunications Industry., INFORMS Marketing Science 2012.

DE BOCK, K., & 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., & VAN DEN POEL, D. (2010). Ensemble Classification based on Generalized Additive Models., 2010 Joint Statistical Meeting (JSM 2010).

DE BOCK, K., & VAN DEN POEL, D. (2010). Customer Churn Prediction using Ensemble Classifiers based on Generalized Additive Models., 34th Annual Conference of the German Classification Society (GfKl).

DE BOCK, K., & 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., & 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).

Scientific activities

Reviewer for an academic journal :

  • Academy of Marketing Science
  • Journal of Business Research
  • European Journal of Operational Research
  • Pattern Recognition Letters
  • Expert Systems with Applications