Formation
- 2019 : Ph.D., Sales and Marketing, Marketing, University of Lille, France
- 2015 : Master, Economics and Mathematics Sciences, Marketing, Ghent University, Belgium
- 2014 : Master, Business Administration, Finance, Ghent University, Belgium
- 2013 : Bachelor, Business Administration, Business, Ghent University, Belgium
Expériences Professionnelles
Expérience en entreprise :
- 2015 - 2016, Business Analyst, Deloitte Touche Thomatsu, Brussels, Belgium
Articles publiés dans des revues à comité de lecture
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De Bock K. W., Coussement K., De Caigny A., (2024). Explainable Analytics in Operational Research, European Journal of Operational Research, 317 (2) 243-248.
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De Caigny A., De Bock K. W., Verboven S., (2024). Hybrid Black-Box Classification for Customer Churn Prediction with Segmented Interpretability Analysis, Decision Support Systems, 181 (2024) 114217.
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Sanchez Ramirez Juliana, Coussement Kristof, De Caigny Arno, Benoit Dries F., Guliyev Emil, (2024). Incorporating Usage Data for B2B Churn Prediction Modeling, Industrial Marketing Management, 120 (July) 191-2025.
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Idbenjra K., Coussement K., De Caigny A., (2024). Investigating the Beneficial Impact of Segmentation-based Modelling for Credit Scoring, Decision Support Systems, 179 (2024) 114170.
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Borchert P., Coussement K., De Weerdt J., De Caigny A., (2024). Industry-sensitive Language Modeling for Business, European Journal of Operational Research, 315 (2) 691-702.
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Mena G., Coussement K., De Bock K.W., 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.
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Beyer Diaz S., Coussement K., De Caigny A., Perez Armas L. F., Creemers S., (2024). Do the US President's Tweets Better Predict Oil Prices? An Empirical Examination Using Long Short-Term Memory Networks, International Journal of Production Research, 62 (6) 2158-2175.
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De Bock K. W., Coussement K., De Caigny A., Slowinski R., Baesens B., Boute R., Choi T.-M., Delen D., Kraus M., Lessmann S., Maldonado S., Martens D., Oskarsdottir M., Vairetti C., Verbeke W., Weber R., (2023). Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda, European Journal of Operational Research, forthcoming (2023) 1-20.
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Meire M., Coussement K., De Caigny A., Hoornaert S., (2022). Does it pay off to communicate like your online community? Evaluating the effect of content and linguistic style similarity on B2B brand engagement, Industrial Marketing Management, 106 (2022) 292-307.
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De Caigny A., Coussement K., Verbeke W., Idbenjra K., Phan M., (2021). Uplift Modeling And Its Implications For B2B Customer Churn Prediction: A Segmentation-Based Modeling Approach, Industrial Marketing Management, 99 (2021) 28-39.
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De Caigny A., De Bock K. W., (2021). Spline-Rule Ensemble Classifiers with Structured Sparsity Regularization for Interpretable Customer Churn Modeling, Decision Support Systems, 150 (113523) 1-14.
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Coussement K., Phan M., De Caigny A., Benoit D. F., Raes A., (2020). Predicting Student Dropout In Subscription-based Online Learning Environments: The Beneficial Impact Of The Logit Leaf Model, Decision Support Systems, 135 (August) 1-11.
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De Caigny A., Coussement K., De Bock K. W., (2020). Leveraging Fine-Grained Transaction Data for Customer Life Event Predictions, Decision Support Systems, 130 (March) 1-12.
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De Caigny A., (2019). Innovation in customer scoring for the financial services industry, 4OR: A Quarterly Journal of Operations Research, 18 (1) 381–382.
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De Caigny A., Coussement K., De Bock K., Lessmann S., (2019). Incorporating Textual Information in Customer Churn Prediction Models Based on a Convolutional Neural Network, International Journal of Forecasting, 36 (4) 1563-1578.
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De Caigny A., Coussement K., 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.
