Associate Professor
Ph.D., Sales and Marketing, Marketing - University of Lille
Track: Marketing
LEM Member
  • 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
Professional Experiences
Professional Experience :
  • 2015 - 2016, Business Analyst, Deloitte Touche Thomatsu, Brussels, Belgium
Published Papers in Refereed Journals
  • 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.
  • 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.
  • 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.
Show all
  • 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.
  • De Caigny A., (2019). Innovation in customer scoring for the financial services industry, 4OR: A Quarterly Journal of Operations Research, n.a. (n.a.) n.a..
  • 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.
  • 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.
Research fields
  • Marketing Analytics
  • Economics
  • Quantitative Methods