Kristof COUSSEMENT

Kristof COUSSEMENT
Full Professor
Ph.D. in Applied Economics - Ghent University
Academic Director
Filière : Marketing
Membre du LEM
CV complet k.coussement@ieseg.fr
Curriculum Vitae
Principales Publications
Domaine de Recherche
Formation
  • 2013 : HDR, Business Administration, Marketing, University of Paris Dauphine, France
  • 2008 : Ph.D. in Applied Economics, Ghent University, Belgium
Expériences Professionnelles
Expérience académique :
  • 2015 - maintenant, Full Professor of Business Analytics, IÉSEG School of Management, , France
  • 2014 - maintenant, Academic Director of MSc. in Big Data Analytics for Business, IÉSEG School of Management, , France
  • 2011 - maintenant, Director of IESEG Center for Marketing Analytics (ICMA), IÉSEG School of Management, , France
  • 2011 - 2015, Associate Professor of Business Analytics, IÉSEG School of Management, , France
  • 2009 - 2011, Assistant Professor of Business Analytics, IÉSEG School of Management, , France
  • 2008 - 2009, Assistant Professor of Business Analytics, KU Leuven, Leuven, Belgium
  • 2008 - 2008, Post-doctoral Researcher, Ghent University, Ghent, Belgium
Articles publiés dans des revues à comité de lecture
  • 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.
  • Benoit D., Tsang W. K., Coussement K., Raes A., (2024). High-stake Student Drop-out Prediction Using Hidden Markov Models in Fully Asynchronous Subscription-based MOOCs, Technological Forecasting and Social Change, 198 (January) 123009.
  • Idbenjra K., Coussement K., De Caigny A., (2024). Investigating the Beneficial Impact of Segmentation-based Modelling for Credit Scoring, Decision Support Systems, 179 (April) 1-12.
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  • Hasan M., Abedin M., Hajek P., Coussement K., Sultan N., Lucey B., (2024). A Blending Ensemble Learning Model for Crude Oil Price Forecasting, Annals of Operations Research, forthcoming (2024) 1-10.
  • Zhu Y., Tessitore T., Harrigan P., Coussement K., (2023). A Guide to Graphic Design For Functional versus Experiential Ads: Color-Evoked Emotion and Design Complexity Can Enhance Effectiveness, Journal of Advertising Research, 63 (1) 81-104.
  • Antioco M., Coussement K., Fletcher-Chen C., Prange C., (2023). What’s in a Word? Adopting a Linguistic-Style Analysis of Western MNCs’ Global Press Releases, Journal of World Business, 58 (2) 101414.
  • 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.
  • Roy S., Singh G., Sadeque S., Harrigan P., Coussement K., (2023). Customer Engagement with Digitalized Interactive Platforms in Retailing, Journal of Business Research, 164 (2023) 114001.
  • Becker L., Coussement K., Buettgen M., Weber E., (2022). Leadership in Innovation Communities: The Impact of Transformational Leadership Language on Member Participation, Journal of Product Innovation Management, 39 (3) 371-393.
  • Coussement K., De Bock K. W., 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 (2) 671-694.
  • Weismueller J., Harrigan P., Coussement K., Tessitore T., (2022). What makes people share political content on social media? The role of emotion, authority and ideology, Computers in Human Behavior, 129 (1) 107-150.
  • 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.
  • Coussement K., Benoit Dries, (2021). Interpretable Data Science for Decision Making, Decision Support Systems, 150 (November) 1-6.
  • Harrigan P., Daly T., Coussement K., Lee J., Soutar G., Evers U., (2021). Identifying Influencers on Social Media, International Journal of Information Management, 56 (February) 1-11.
  • 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.
  • Lessmann S., Haupt J., Coussement K., De Bock K.W., (2021). Targeting Customers for Profit: An Ensemble Learning Framework to Support Marketing Decision-making, Information Sciences, 557 (May) 286-301.
  • Sulikowski P., Zdziebko T., Coussement K., Dyczkowski K., Kluza K., Sachpazidu-Wójcicka K., (2021). Gaze and Event Tracking for Evaluation of Recommendation Driven Purchase, Sensors, 21 (4) 1381.
  • Harrigan P., Coussement K., Lancelot Miltgen C., Ranaweera C., (2020). The Future of Technology in Marketing; Utopia or Dystopia?, Journal of Marketing Management, 36 (3-4) 211-214.
  • De Bock K., Coussement K., Lessmann S., (2020). Cost-Sensitive Multicriteria Ensemble Selection: A Framework For Business Failure Prediction When Misclassification Costs Are Uncertain, European Journal of Operational Research, 285 (2) 612-630.
  • Demoulin N., Coussement K., (2020). Acceptance of Text-Mining Systems: The Signaling Role of Information Quality, Information and Management, 57 (1) 1-11.
  • Olaya D., Coussement K., Verbeke W., (2020). A Survey and Benchmarking Study of Multitreatment Uplift Modeling, Data Mining and Knowledge Discovery, 34 (1) 273-308.
  • 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.
  • 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.
  • Kim P.H., Kotha R., Fourné S., Coussement K., (2019). Taking Leaps of Faith: Evaluation Criteria and Resource Commitments for Early-stage Inventions, Research Policy, 48 (6) 1429-1444.
  • Debaere S., Devriendt F., Brunneder J., Verbeke W., De Ruyck T., Coussement K., (2019). Reducing Inferior Member Community Participation Using Uplift Modeling: Evidence From A Field Experiment, Decision Support Systems, 123 (August) 1-12.
  • 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.
  • Antioco Michael, Coussement K., (2018). Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias, International Journal of Information Management, 38 (1) 301-310.
  • 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.
  • 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.
  • Debaere S., Coussement K., De Ruyck T., (2018). Multi-label Classification of Member Participation in Online Innovation Communities, European Journal of Operational Research, 270 (2) 761-774.
  • Coussement K., Lessmann S., Verstraeten G., (2017). A Comparative Analysis of Data Preparation Algorithms for Customer Churn Prediction: A Case Study in the Telecommunication Industry , Decision Support Systems, 95 (March) 27-36.
  • Bequé A., Coussement K., Gayler R., Lessmann S., (2017). Approaches for Credit Scorecard Calibration: An Empirical Analysis, Knowledge-Based Systems, 134 (15) 213-227.
  • Coussement K., Debaere S., De Ruyck T., (2017). Inferior Member Participation Identification in Innovation Communities: The Signaling Role of Linguistic Style Use, Journal of Product Innovation Management, 34 (5) 565-579.
  • Coussement K., Harrigan P., Benoit D., (2015). Improving Direct Mail Targeting Through Customer Response Modelling, Expert Systems with Applications, 42 (22) 8403–8412.
  • Coussement K., Benoit D., Antioco M., (2015). A Bayesian Approach for Incorporating Expert Opinions into Decision Support Systems: A Case Study of Online Consumer-Satisfaction Detection, Decision Support Systems, 79 (November) 24-32.
  • Coussement K., Van den Bossche F., De Bock K. W., (2014). Data Accuracy’s Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees, Journal of Business Research, 67 (1) 2751-2758.
  • Coussement K., (2014). Improving Customer Retention Management through Cost-sensitive Learning, European Journal of Marketing, 48 (3/4) 477 - 495.
  • Coussement K., 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.
  • Coussement K., Buckinx W., (2011). A Probability-mapping Algorithm for Calibrating the Posterior Probabilities: A Direct Marketing Application, European Journal of Operational Research, 214 (3) 732-738.
  • De Bock K. W., Coussement K., Van den Poel D., (2010). Ensemble Classification Based on Generalized Additive Models, Computational Statistics & Data Analysis, 54 (6) 1535-1546.
  • Coussement K., Benoit D.F., Van den Poel D., (2010). Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, Expert Systems with Applications, 37 (3) 2132-2143.
  • Coussement K., Van den Poel D., (2009). Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, Expert Systems with Applications, 37 (3) 2132-2143.
  • Coussement K., Van den Poel D., (2008). Integrating the Voice of Customers Through Call Center Emails into a Decision Support System for Churn Prediction, Information and Management, 45 (3) 164-174.
  • Coussement K., Van den Poel D., (2008). Churn Prediction in Subscription Services: An Application of Support Vector Machines while Comparing Two Parameter-selection Techniques, Expert Systems with Applications, 34 (1) 313-327.
  • Coussement K., Van den Poel D., (2008). Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, Decision Support Systems, 44 (4) 370-382.
Ouvrages
  • Charry K., Coussement K., Demoulin N., Heuvinck N., (2016) Marketing Research with IBM SPSS Statistics, Routledge, London.
  • Coussement K., Harrigan P., (2014) All You Need Is True Love (With Your Customers)! A Customer Relationship Management Fairy Tale, Ghent University Press, Ghent.
  • Coussement K., De Bock K. W., Neslin S.A., (2013) Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships , Gower Publishing, London.
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  • Coussement K., Demoulin N., Charry K., (2011) Marketing Research with SAS Enterprise Guide , Gower Publishing, Farnham.
Chapitres de livres
  • Coussement K., Benoit D., Poel Dirk Van den, (2015), Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction, in: The Sustainable Global Marketplace.
  • Boujena O., Coussement K., De Bock K., (2015), Data Driven Customer Centricity: CRM Predictive Analytics, in: Trends and Innovations in Marketing Information Systems.
  • Coussement K., De Bock K. W., (2013), Textual Customer Data Handling for Quantitative Marketing Analysis, in: Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships.
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  • Coussement K., De Bock K. W., (2013), Ensemble Learning in Database Marketing , in: Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships .
Domaines de Recherche
  • Deep Learning
  • Machine Learning
  • Text Mining
  • Big Data
  • Data Science
  • Analytics
  • Natural Language Processing