Research


Refereed Journal Articles
Conference Proceedings
Invited Seminars
Working Papers
Software
Books
Book Chapters
Case Studies

Refereed Journal Articles

  1. P.N. Borchert, K. Coussement, J. De Weerdt, A. De Caigny, Industry-Sensitive Language Modeling for Business, European Journal of Operational Research, (2024, forthcoming).
  2. F. Zechiel, M. Blaurock, E. Weber, M. Buettgen, K. Coussement, How Tech Companies Advance Sustainability Through Artificial Intelligence: Developing and Evaluating an AI x Sustainability Strategy Framework, Industrial Marketing Management, (2024, forthcoming).
  3. M. Hasan, M.Z. Abedin, P. Hajek, K. Coussement, N. Sultan, B. Lucey, A Blending Ensemble Learning Model for Crude Oil Price Forecasting, Annals of Operations Research, (2024, forthcoming).
  4. K.W. De Bock, K. Coussement, A. De Caigny, R. Slowinski, B. Baesens, R.N. Boute, T.-M. Choi, D. Delen, M. Kraus, S. Lessmann, S. Maldonado, D. Martens, M. Oskarsdottir, C. Vairetti, W. Verbeke, R. Weber, Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda, European Journal of Operational Research, (2024, forthcoming).
  5. J. Weismueller, R.L. Gruner, P. Harrigan, K. Coussement, S. Wang, Information Sharing and Political Polarisation on Social Media: The Role of Falsehood and Partisanship, Information Systems Journal, (2024, forthcoming).
  6. S. Beyer Diaz, K. Coussement, A. De Caigny, L.F. Pérez Armas, S. Creemers, Do the US President’s Tweets Better Predict Oil Prices? An Empirical Examination Using Long Short-Term Memory Networks, International Journal of Production Research, (2024, forthcoming).
  7. G. Mena, K. Coussement, K.W. De Bock, A. De Caigny, S. Lessmann, Exploiting Time-Varying RFM Measures for Customer Churn Prediction with Deep Neural Networks, Annals of Operations Research, (2024, forthcoming).
  8. K. De Bock, K. Coussement, A. De Caigny, R. Słowiński, Explainable Analytics in Operational Research, European Journal of Operational Research, (2024, forthcoming).
  9. K. Topuz, A. Bajaj, K. Coussement, T.L. Urban, Interpretable Machine Learning and Explainable Artificial Intelligence, Annals of Operations Research, (2024, forthcoming).
  10. M.Z. Abedin, K. Coussement, M. Kraus, S. Maldonado, K. Topuz, Explainable A.I. for Enhanced Decision Making, Decision Support Systems, (2023, forthcoming).
  11. K. Idbenjra, K. Coussement, A. De Caigny, Investigating the Beneficial Impact of Segmentation-based Modelling for Credit Scoring, Decision Support Systems, 179 (April) (2024), pp 1-12 (DOI).
  12. D.F. Benoit, W.K. Tsang, K. Coussement, A. Raes, High-stake Student Drop-out Prediction Using Hidden Markov Models in Fully Asynchronous Subscription-based MOOCs, Technological Forecasting & Social Change, 198 (January) (2024), pp 1-9 (DOI).
  13. S.K. Roy, G. Singh, S. Sadeque, P. Harrigan, K. Coussement, Customer Engagement with Digitalized Interactive Platforms in Retailing, Journal of Business Research, 164, (2023), pp 1-15 (DOI).
  14. P.N. Borchert, K. Coussement, A. De Caigny, J. De Weerdt, Extending Business Failure Prediction Models With Textual Website Content Using Deep Learning, European Journal of Operational Research, 306 (1), (2023) (DOI).
  15. M. Phan, A. De Caigny, K. Coussement, A Decision Support Framework to Incorporate Textual Data for Early Student Dropout Prediction in Higher Education, Decision Support Systems, 168 (May) (2023), pp 1-13 (DOI).
  16. Y. Zhu, T. Tessitore, P. Harrigan, K. Coussement, Graphic Design in Advertising: A Guideline on the Use of Colour-evoked Emotion and Design Complexity in the Construction of Functional versus Experiential Advertisements, Journal of Advertising Research, March (3) (2023), pp 81-103 (DOI).
  17. M. Antioco, K. Coussement, C. Fletcher-Chen, C. Prange, What’s in a Word? Adopting a Linguistic-Style Analysis of Western MNCs’ Global Press Releases, Journal of World Business, 58 (2) (2023), pp 1-16 (DOI).
  18. M. Meire, K. Coussement, A. De Caigny, S. Hoornaert, 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 (October) (2022), pp 292-307 (DOI).
  19. K. Coussement, K.W. De Bock, S. Geuens, 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 (2022), pp 671-694 (DOI).
  20. L. Becker, K. Coussement, M. Buettgen, E. Weber, Leadership in Innovation Communities: The Impact of Transformational Leadership Language on Member Participation, Journal of Product Innovation Management, 39 (3) (2022), pp 371-393 (DOI).
  21. J. Weismueller, P. Harrigan, K. Coussement, T. Tessitore, What Makes People Share Political Content on Social Media? The Role of Emotion, Authority and Ideology, Computers in Human Behavior, 129 (April) (2022) (DOI).
  22. A. De Caigny, K. Coussement, W. Verbeke, K. Idbenjra, M. Phan, Uplift Modeling and its Implications for B2B Customer Churn Prediction: A Segmentation-based Modeling Approach, Industrial Marketing Management, 99 (November) (2021), pp 28-39 (DOI).
  23. K. Coussement, D.F. Benoit, Interpretable Data Science for Decision Making (Editorial), Decision Support Systems, 150, (2021), pp 1-6 (DOI).
  24. S. Lessmann, J. Haupt, K. Coussement, K.W. De Bock, Targeting Customers for Profit: An Ensemble Learning Framework to Support Marketing Decision-making, Information Sciences, 557 (May) (2021), pp 286-301 (DOI).
  25. P. Sulikowski, T. Zdziebko, K. Coussement, K. Dyczkowski, K. Kluza, K. Sachpazidu-Wójcicka, Gaze and Event Tracking for Evaluation of Recommendation Driven Purchase, Sensors, 21 (4) (2021), pp 1-21 (DOI).
  26. P. Harrigan, T. Daly, K. Coussement, J. Lee, G. Soutar, U. Evers, Identifying Influencers on Social Media, International Journal of Information Management, 56 (2021), pp 1-11 (DOI).
  27. A. De Caigny, K. Coussement, K.W. De Bock, S. Lessmann, Incorporating Textual Information in Customer Churn Prediction Models Based on a Convolutional Neural Network, International Journal of Forecasting, 36 (4) (2020), pp 1563-1578 (DOI).
  28. K. Coussement, M. Phan, A. De Caigny, D.F. Benoit, A. Raes, Predicting Student Dropout in Subscription-Based Online Learning Environments: The Beneficial Impact of the Logit Leaf Model, Decision Support Systems, 135 (2020), pp 1-11 (DOI, LLM R package).
  29. K.W. De Bock, K. Coussement, S. Lessmann, Cost-sensitive Business Failure Prediction When Misclassification Costs Are Uncertain: A Heterogeneous Ensemble Selection Approach, European Journal of Operational Research, 285 (2020), pp 612-630 (DOI, CMES R package).
  30. P. Harrigan, K. Coussement, C. Lancelot Miltgen, C. Ranaweera, The Future of Technology in Marketing; Utopia or Dystopia? (Editorial), Journal of Marketing Management, 36 (3-4) (2020), pp 211-215 (DOI).
  31. A. De Caigny, K. Coussement, K.W. De Bock, Leveraging Fine-Grained Transaction Data for Customer Life Event Predictions, Decision Support Systems, 130 (2020), pp 1-12 (DOI).
  32. D.A. Olaya, W. Verbeke, K. Coussement, A Survey and Benchmarking Study of Multi-treatment Uplift Modeling, Data Mining and Knowledge Discovery, 34 (2020), pp 273-308.(DOI).
  33. N. Demoulin, K. Coussement, Acceptance of Text-Mining Systems: The Signaling Role of Information Quality, Information and Management, 57 (1) (2020), pp 1-11 (DOI).
  34. S. Debaere, F. Devriendt, J. Brunneder, W. Verbeke, T. De Ruyck, K. Coussement, Reducing Inferior Member Community Participation Using Uplift Modeling: Evidence From A Field Experiment, Decision Support Systems, 123 (August) (2019), pp 1-12 (DOI).
  35. P.H. Kim, R. Kotha, S. Fourné, K. Coussement, Taking Leaps of Faith: Evaluation Criteria and Resource Commitments for Early-stage Inventions, Research Policy, 48 (6) (2019), pp 1429-1444 (DOI).
  36. S. Debaere, K. Coussement, T. De Ruyck, Multi-label Classification of Member Participation in Online Innovation Communities, European Journal of Operational Research, 270 (2) (2018), pp 761-774 (DOI).
  37. A. De Caigny, K. Coussement, K.W. De Bock, A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Trees, European Journal of Operational Research, 269 (2) (2018), pp 760-772 (DOI, LLM R package).
  38. M. Antioco, K. Coussement, Misreading of Consumer Dissatisfaction in Online Product Reviews: Writing Style as a Cause for Bias, International Journal of Information Management, 38 (2018), pp 301-310 (DOI).
  39. S. Geuens, K. Coussement, K.W. De Bock, A Framework for Configuring Collaborative Filtering-based Recommendations Derived from Purchase Data, European Journal of Operational Research, 265 (1) (2018), pp 208-218 (DOI).
  40. A. Bequé, K. Coussement, R. Gayler, S. Lessmann, Approaches for Credit Scorecard Calibration: An Empirical Analysis, Knowledge-based Systems, 134 (October) (2017), pp 213-227 (DOI).
  41. K. Coussement, S. Debaere, T. De Ruyck, Inferior Member Participation Identification in Innovation Communities: The Signaling Role of Linguistic Style Use, Journal of Product Innovation Management,  35 (5) (2017), pp 565-579 (DOI).
  42. K. Coussement, S. Lessmann, G. Verstraeten, A Comparative Analysis of Data Preparation Algorithms for Customer Churn Prediction: A Case Study in the Telecommunication Industry, Decision Support Systems, 95 (March) (2017), pp 27-36 (DOI).
  43. K. Coussement, D.F. Benoit, M. Antioco, A Bayesian Approach for Incorporating Expert Opinions into Decision Support Systems: A Case Study of Online Consumer-Satisfaction Detection, Decision Support Systems, 79 (November) (2015), pp 24-32 (DOI).
  44. K. Coussement, P. Harrigan, D.F. Benoit, Improving Direct Mail Targeting Through Customer Response Modelling, Expert Systems with Applications, 42 (22) (2015), pp 8403–8412 (DOI).
  45. K. Coussement, Improving Customer Retention Management through Cost-sensitive Learning, European Journal of Marketing, 48 3/4 (2014), pp 477-495 (DOI).
  46. K. Coussement, F.A.M. Van den Bossche, K.W. De Bock, Data Accuracy’s Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees, Journal of Business Research, 67 (1) (2014), pp 2751-2758 (DOI).
  47. K. Coussement, K.W. De Bock, Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning, Journal of Business Research, 66 (9) (2013), pp 1629-1636 (DOI).
  48. K. Coussement, W. Buckinx, A Probability-Mapping Algorithm for Calibrating the Posterior Probabilities: A Direct Marketing Application, European Journal of Operational Research, 214 (2011), pp 732-738 (DOI).
  49. K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, Computational Statistics & Data Analysis, 54 (6) (2010), pp 1535-1546 (DOI, GAMens R package).
  50. K. Coussement, D.F. Benoit and D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, Expert Systems with Applications, 37 (2010), pp 2132-2143 (DOI).
  51. K. Coussement, D. Van den Poel, Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, Expert Systems with Applications, 36 (3) (2009), pp 6127-6134 (DOI).
  52. K. Coussement, D. Van den Poel, Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction, Information and Management, 45 (3) (2008), pp 164-174 (DOI).
  53. K. Coussement, D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, Decision Support Systems, 44 (4) (2008), pp 370-382 (DOI).
  54. K. Coussement, D. Van den Poel, Churn Prediction in Subscription Services: An Application of Support Vector Machines while Comparing Two Parameter-selection Techniques, Expert systems with applications, 34 (1) (2008), pp 313-327 (DOI).

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Conference Proceedings

  1. F. Zechiel, K. Coussement, M. Büttgen, M. Blaurock, E. Weber, How Tech Companies Advance Sustainability Through Artificial Intelligence: Developing and Evaluating an AI x Sustainability Strategy Framework, In Proceedings of the 13th AMA Servsig Conference, Bordeaux (France), June 5-8 (2024).
  2. F. Zechiel, Y. Trautwein, K. Coussement, M. Meire, M. Büttgen, Opening the ‘Black Box’ of HRM Algorithmic Biases – How Companies’ Hiring Practices Induce Discrimination on Freelancing Platforms, In Proceedings of the 13th AMA Servsig Conference, Bordeaux (France), June 5-8 (2024).
  3. M. Kraus, N. Hambauer, K. Müller, P. Kröckel, N. Ulapane, A. De Caigny, K.W. De Bock, K. Coussement, Coupling Neural Networks Between Clusters for Better Personalized Care, In Proceedings of the Hawaii International Conference on System Sciences (HICSS), Waikiki (USA), January 3-6 (2024).
  4. P.N. Borchert, J. De Weerdt, K. Coussement, A. De Caigny. M.F. Moens, CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore (Singapore), December 6-10 (2023).
  5. J. Sanchez Ramirez, K. Coussement, A. De Caigny, D.F. Benoit, L. Waardenburg, To Use or Not to Use? Incorporating Usage Data for B2B Churn Prediction Modeling, In Proceedings of the 54th Annual Conference of the Decision Sciences Institute (abstract), Atlanta (USA), November 18-20 (2023).
  6. F. Zechiel, M. Blaurock, E. Weber, M. Buettgen, K. Coussement, Advancing Sustainability through Artificial Intelligence – A Strategic Framework, In Proceedings of the 8th Rostocker Dienstleistungstagung, Rostock (Germany), September 7-9 (2023).
  7. P. Borchert, K. Coussement, J. De Weerdt, A. De Caigny, Extracting Key Insights from Corporate Earnings Press Releases and Earnings Call Transcripts, In Proceedings of the 37th Operational Research Belgium Conference, Liege (Belgium), May 25-26 (2023).
  8. M. Phan, K. Coussement, K.W. De Bock, A. De Caigny, Hybrid Segmentation Approaches for Supervised Learning in R, In Proceedings of the 37th Operational Research Belgium Conference, Liege (Belgium), May 25-26 (2023).
  9. N. Hambauer, M. Kraus, K. Coussement, A. De Caigny, K.W. De Bock, Introducing LLM GAMs: Model Performance, Interpretability, and Sparsity in Marketing Analytics, In Proceedings of the 37th Operational Research Belgium Conference, Liege (Belgium), May 25-26 (2023).
  10. J. Sanchez Ramirez, K. Coussement, A. De Caigny, D.F. Benoit, L. Waardenburg, Incorporating Usage Data for B2B Churn Prediction Modeling, In Proceedings of the 37th Operational Research Belgium Conference, Liege (Belgium), May 25-26 (2023).
  11. Y. Zhu, P. Harrigan, K. Coussement, T. Tessitore, Assisting Ad Testing with Viewer Emotional Response Prediction: A Guideline and Method Development, In Proceedings of European Marketing Academy Conference, Odense (Denmark), May 23-26 (2023).
  12. M. Meire, K. Coussement, W. Standaert, A. De Caigny, M. Szerovay, How and When Does DEI Communication Impact Social Media Engagement? Evidence from the Professional Sports Industry, In Proceedings of Marketing Science: Diversity, Equity and Inclusion Conference, Dallas (Texas, USA), March 24-25 (2023).
  13. J. Weismueller, R.L. Gruner, P. Harrigan, K. Coussement, S. Wang, The Dark Side of Social Media: Misinformation, Partisanship, and Polarization, In Proceedings of the Australian and New Zealand Marketing Academy Conference 2022 (short paper), Perth (Australia), December 5-7 (2022) (best paper award).
  14. Y. Zhu, P. Harrigan, K. Coussement, T. Tessitore, The Effect Of Graphic Design Elements On The Effectiveness Of Functional Versus Experiential Advertisements, In Proceedings of the Australian and New Zealand Marketing Academy Conference 2022 (short paper), Perth (Australia), December 5-7 (2022).
  15. J. Roa, K. Coussement, S. Maldonado, R. Weber, Interpretation in Models for Crime Prediction, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  16. M. Phan, K.W. De Bock, K. Coussement, A. De Caigny, Modeling with Hybrid Segmentation Methods: A Statistical Library for R and Python, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  17. K. Idbenjra, K. Coussement, A. De Caigny, Investigating the Beneficial Impact of the Logit Leaf Model for Credit Scoring, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  18. S. Beyer Diaz, K. Coussement, A. De Caigny, Deep Learning for Life Event Prediction in the Financial Industry, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  19. P. Borchert, J. De Weerdt, K. Coussement, A. De Caigny, Learning industry-sensitive language in business communication – Insights in BusinessBERT, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  20. E. Guliyev, K. Coussement, A. De Caigny, Actionable Knowledge Discovery and Rule Mining in B2B Churn, In Proceedings of the 32nd European Conference on Operational Research (abstract), Espoo (Finland), July 3-6 (2022).
  21. Y. Zhu, P. Harrigan, K. Coussement, T. Tessitore, Explore Functional and Experiential Advertisement Construction from a Graphic Design Perspective, In Proceedings of the Academy of Marketing Science Annual Congress (short paper), Monterey (USA), May 25-27 (2022).
  22. S. Beyer Diaz, K. Coussement, A. De Caigny, Using Deep Learning for Life Event Prediction in the Financial Industry, In Proceedings of the 4th Analytics for Management and Economics Conference (abstract) (invited), Sint-Petersburg (Russia), December 8 (2021).
  23. M. Phan, A. De Caigny, K. Coussement, Student Dropout Prediction and Segmentation Using Feedback Text, In Proceedings of the 52nd Annual Conference of the Decision Sciences Institute (abstract), Houston (USA), November 17-20 (2021).
  24. S. Beyer Diaz, K. Coussement, A. De Caigny, Sequential Data for Recommendations in the Financial Industry, In Proceedings of the 52nd Annual Conference of the Decision Sciences Institute (abstract), Houston (USA), November 17-20 (2021).
  25. K. Idbenjra, K. Coussement, A. De Caigny, The Logit Leaf Model: Adding to the Interpretability of the Credit Scoring Predictions, In Proceedings of the 52nd Annual Conference of the Decision Sciences Institute (abstract), Houston (USA), November 17-20 (2021).
  26. E. Guliyev, K. Coussement, A. De Caigny, Actionable Knowledge Discovery and Rule Mining in B2B Churn, In Proceedings of the 52nd Annual Conference of the Decision Sciences Institute (abstract), Houston (USA), November 17-20 (2021).
  27. S. Beyer Diaz, K. Coussement, A. De Caigny, A Deep Learning Model for Cross-selling Recommendations in the Financial Service Sector, In Proceedings of the 31st European Conference on Operational Research (abstract), Athens (Greece), July 11-14 (2021).
  28. P. Borchert, J. De Weerdt, K. Coussement, A. De Caigny, BusinessBERT – A Pre-trained Language Model for Finance and Business Related Text, In Proceedings of the 31st European Conference on Operational Research (abstract), Athens (Greece), July 11-14 (2021).
  29. K. Idbenjra, K. Coussement, A. De Caigny, Investigating the Beneficial Impact of the Logit Leaf Model for Credit Scoring, In Proceedings of the 31st European Conference on Operational Research (abstract), Athens (Greece), July 11-14 (2021).
  30. E. Guliyev, K. Coussement, A. De Caigny, Actionable Knowledge Discovery and Rule Mining in B2B Churn, In Proceedings of the 31st European Conference on Operational Research (abstract), Athens (Greece), July 11-14 (2021).
  31. K.W. De Bock, A. De Caigny, K. Coussement, A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Trees” (EJOR 2018): A Review and Update, In Proceedings of the 31st European Conference on Operational Research (abstract), Athens (Greece), July 11-14 (2021).
  32. K. Idbenjra, K. Coussement, A. De Caigny, M. Phan, W. Verbeke, Uplift LLM: A New Uplift Model Algorithm Based on the Logit Leaf Model, In Proceedings of the 51st Annual Conference of the Decision Sciences Institute (abstract), San Francisco (USA), November 21-23 (2020).
  33. P. Borchert, K. Coussement, J. De Weerdt, A. De Caigny, Incorporating Textual Website Content in Business Failure Prediction Models, In Proceedings of the 51st Annual Conference of the Decision Sciences Institute (abstract), San Francisco (USA), November 21-23 (2020).
  34. S. Beyer Diaz, K. Coussement, A. De Caigny, L. Perez Armas, S. Creemers, Incorporating Donald Trump’s Tweets into LSTM for Oil Price Prediction, In Proceedings of the 51st Annual Conference of the Decision Sciences Institute (abstract), San Francisco (USA), November 21-23 (2020).
  35. M. Phan, A. De Caigny, K. Coussement, Improving Student Dropout Prediction By Integrating Feedback Textual Data, In Proceedings of the 51st Annual Conference of the Decision Sciences Institute (abstract), San Francisco (USA), November 21-23 (2020).
  36. P. Borchert, K. Coussement, J. De Weerdt, A. De Caigny, Incorporating Textual Website Content in Business Failure Prediction Models, In Proceedings of the 3rd Analytics for Management and Economics Conference (abstract) (invited), Sint-Petersburg (Russia), November 6 (2020).
  37. S. Beyer Diaz, K. Coussement, A. De Caigny, L. Perez Armas, S. Creemers, Incorporating Tweets into LSTM for Oil Price Prediction, In Proceedings of the 3rd Analytics for Management and Economics Conference (abstract) (invited), Sint-Petersburg (Russia), November 6 (2020).
  38. M. Phan, A. De Caigny, K. Coussement, Improving Student Dropout Prediction By Integrating Feedback Textual Data, In Proceedings of the 3rd Analytics for Management and Economics Conference (abstract) (invited), Sint-Petersburg (Russia), November 6 (2020).
  39. L. Becker, K. Coussement, M. Buettgen, E. Weber, In Proceedings of The Human Side of Innovation Management Paper Development Workshop (full paper), Bochum (Germany), October 19-20 (2020).
  40. A. De Caigny, K. Coussement, K.W. De Bock, Customer Life Event Prediction Using Deep Learning, In Proceedings of the 34th Annual Conference of the Belgian Operational Research Society (abstract), Lille (France), January 30-31 (2020).
  41. W.K. Tsang, A. Raes, K. Coussement, D.F. Benoit, Unravelling Drop-out in Fully Asynchronous Subscription-based MOOCs: When is Drop-out Failure or Success?, In Proceedings of the 34th Annual Conference of the Belgian Operational Research Society (abstract), Lille (France), January 30-31 (2020).
  42. K.W. De Bock, K. Coussement, C. Ciobanu, A. De Caigny, Integrating E-commerce Indicators in Multichannel Retail Chain Store Efficiency Analyses: A Robust Two-stage DEA Approach, In Proceedings of the 2019 Thought Leadership Conference on Metrics and Analytics in Retailing (full paper), Atlanta (USA), November 13-15 (2019).
  43. K. Coussement, Deep Learning in Customer Churn Prediction, In Proceedings of the 2nd Analytics for Management and Economics Conference (abstract) (invited), Sint-Petersburg (Russia), September 27-28 (2019).
  44. A. De Caigny, K. Coussement, K.W. De Bock, Customer Life Event Prediction, In Proceedings of the 30th European Conference on Operational Research (abstract), Dublin (Ireland), June 23-26 (2019).
  45. M. Phan, K. Coussement, D.F. Benoit, A. De Caigny, A. Raes, Detecting Online Student Dropout: A Machine Learning Approach, In Proceedings of the 30th European Conference on Operational Research (abstract), Dublin (Ireland), June 23-26 (2019).
  46. C. Ciobanu, K. Coussement, K.W. De Bock, Efficiency in Multi-channel Retail Chain Store: A Two-stage DEA Approach with Environmental Factors and Ecommerce Indicators, In Proceedings of the 29th European Conference on Operational Research (abstract), Valencia (Spain), July 8-11 (2018).
  47. A. De Caigny, K. Coussement, K.W. De Bock, S. Lessmann, Integrating Textual Information in Customer Churn Prediction Models: A Deep Learning Approach, In Proceedings of the 29th European Conference on Operational Research (abstract), Valencia (Spain), July 8-11 (2018).
  48. M. Phan, K. Coussement, D.F. Benoit, A. Raes, A. De Caigny, The Beneficial Effect of Ensemble Learning in Predicting Student Drop-out in Online Learning Environment, In Proceedings of the 29th European Conference on Operational Research (abstract), Valencia (Spain), July 8-11 (2018).
  49. C. Ciobanu, K. Coussement, K.W. De Bock, A Two-stage DEA Approach for Multi-channel Retail Chain Store Efficiency Analysis, In Proceedings of the International Conference on Data Envelopment Analysis (abstract), Aston (UK), April 16-18 (2018).
  50. S. Geuens, K. Coussement, K.W. De Bock, Beyond Clickthrough Rate: Measuring the True Impact of Personalized E-mail Product Recommendations, In Proceedings of the 3nd Business Analytics in Finance and Industry Conference (short paper), Santiago (Chile), January 17-19 (2018).
  51. A. De Caigny, K. Coussement, K.W. De Bock, A New Algorithm for Segmented Modeling: An Application in Customer Churn Prediction, In Proceedings of the Informs Annual Meeting (abstract), Houston (Texas, USA), October 22-25 (2017).
  52. S. Debaere, K. Coussement, T. De Ruyck, Inferior Member Participation Prevention in Online Research Communities, In Proceedings of the 21st Conference of the International Federation of Operational Research Societies (abstract), Quebec (Canada), July 17-21 (2017).
  53. A. De Caigny, K. Coussement, K.W. De Bock, Leaf Modeling: An Application in Customer Churn Prediction, In Proceedings of the 21st conference of the International Federation of Operational Research Societies (abstract), Quebec (Canada), July 17-21 (2017).
  54. S. Debaere, T. De Ruyck, S. Van Neck, K. Coussement, Minority Report in Research Communities: The ‘Participant’ Future Can Be Seen, In Proceedings of the General Online Research 2017 Conference, Berlin (Germany), March 15-17 (2017).
  55. S. Debaere, T. De Ruyck, K. Coussement, Minority Report in Research Communities: The “Participant” Future Can Be Seen, In Proceedings of Insight Innovation Exchange (short paper), Amsterdam (Netherlands), February 20-21 (2017).
  56. S. Debaere, S. van Neck, T. de Ruyk, K. Coussement, Minority Report in Market Research Online Communities: The Future Can Be Seen, Member Disengagement Can Be Prevented, The Seventh International Conference of the Association for Soft Computing, The University of Winchester, Winchester (United Kingdom), September 8-9 (2016).
  57. S. Geuens, K.W. De Bock, K. Coussement, Towards Better Online Personalization: A Framework for Empirical Evaluation and Real-life Validation of Hybrid Recommendation Systems, In Proceedings of the 19th Academy of Marketing Science World Marketing Congress (short paper), Paris (France), July 19-23 (2016).
  58. S. Debaere, K. Coussement, T. De Ruyck, Multi-label Learning for Churn Prediction in Online Research Communities, In Proceedings of the 28th European Conference on Operational Research (abstract), Poznan (Poland), July 3-6 (2016).
  59. K. Coussement, S. Debaere, T. De Ruyck, Building Healthy Innovation Communities through Churn Prediction, In Proceedings of the Innovation in Data-Rich Environments JPIM/MSI Research Workshop (full paper), Knoxville (Tennessee, USA),  June 8-10 (2016).
  60. S. Hoozée, L. Bouten, K. Coussement, M. Antioco, Impression Management in CSR-related Press Releases: an Empirical Investigation based upon Textual Characteristics, In Proceeding of the 6th CSEAR North America Conference (short paper), Illinois State University, Normal (Illinois, USA), June 1-2 (2016).
  61. S. Debaere, K. Coussement, T. De Ruyck, A Churn Prediction Decision Support System as an Effective Weapon to Sustain Healthy Online Research Communities, In Proceedings of the 2nd Business Analytics in Finance and Industry Conference (short paper), Santiago (Chile), December 14-16 (2015).
  62. S. Geuens, K. Coussement, K.W. De Bock, An Evaluation Framework for Collaborative Filtering on Purchase information in Recommendation Systems, In Proceedings of the 2nd Business Analytics in Finance and Industry Conference (short paper), Santiago (Chile), December 14-16 (2015).
  63. S. Geuens, K.W. De Bock, K. Coussement, Integrating Behavioral, Product, and Customer Data in Hybrid Recommendation Systems Based on Factorization Machines, In Proceedings of the 2nd Business Analytics in Finance and Industry Conference (short paper), Santiago (Chile), December 14-16 (2015).
  64. K. Coussement, N. Demoulin, Text Mining Adoption Drivers, In Proceedings of the 27th European Conference on Operational Research (abstract), Glasgow (UK), July 12-15 (2015).
  65. S. Debaere, K. Coussement, T. De Ruyck, Sustaining Structural Co-creation: Proactive Churn Identification in Innovation Communities, In Proceedings of the 22nd Innovation Product Development Management Conference (short paper), Copenhagen (Denmark), 14-16 June (2015).
  66. A. Baumann, S. Lessmann, K. Coussement, K. De Bock, Maximize What Matters: Predicting Customer Churn with Decision-Centric Ensemble Selection. In Proceedings of the 23rd European Conference on Information Systems, Münster (Germany), May 26-29 (2015).
  67. K. Coussement, P. Harrigan, T. Daly, J. Lee, G. Soutar, Identification of Market Mavens on Social Media. In Proceedings of the International Conference on Contemporary Thinking in Marketing: Big Data Analytics in Marketing (short paper), Mumbai (India), February 21 (2015).
  68. K. Coussement and G. Verstraeten, A Bagging-Based Undersampling Strategy for Classification: A Customer Churn Prediction Application. In Proceedings of the International Conference on Contemporary Thinking in Marketing: Big Data Analytics in Marketing (short paper), Mumbai (India), February 21 (2015).
  69. P. Harrigan, K. Coussement, T. Daly, J. Lee, G. Soutar, Identification of Market Mavens on Social Media. In Proceedings of the Australian & New Zealand Marketing Academy Conference (short paper), Brisbane (Australia), December 1-3 (2014).
  70. S. Geuens, K. Coussement, K. De Bock, Evaluating Collaborative Filtering Methods within a Binary Purchase Setting, In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (short paper), Nancy (France), September 15-19 (2014).
  71. K. Coussement and G. Verstraeten, A Bagging-based Undersampling Strategy for Classification : A Customer Churn Prediction Application. In Proceedings of INFORMS Conference of the International Federation of Operational Research Societies (abstract), Barcelona (Spain), July 13-18 (2014).
  72. D.F. Benoit, K. Coussement, M. Antioco, Improved Decision Making by Incorporating Expert Opinions into Statistical Models. In Proceedings of INFORMS Conference of the International Federation of Operational Research Societies (abstract), Barcelona (Spain), July 13-18 (2014).
  73. K.W. De Bock, S. Lessmann, K. Coussement, Multicriteria Optimization for Cost-Sensitive Ensemble Selection in Business Failure Prediction. In Proceedings of INFORMS Conference of the International Federation of Operational Research Societies (abstract), Barcelona (Spain), July 13-18 (2014).
  74. K. Coussement, Improving Decision Tree Segmentation through Leaf Modeling in Direct Marketing, In Proceedings of Marketing Science (abstract), Istanbul (Turkey), July 11-13 (2013).
  75. K. Coussement, A. Antioco, Warning About Negative Product Feedback: How Consumers Write it Influences What Managers Make of it, In Proceedings of the International Product Development Conference (full paper), Paris (France), June 23-25 (2013). Best Paper Runner-up Christer Karlsson 2013 Award.
  76. K. Coussement, S. Lessmann, K.W. De Bock, Ensemble Selection for Churn Prediction in the Telecommunication Industry. In Proceedings of Marketing Science (abstract), Boston (Massachusetts, USA), June 7-9 (2012).
  77. K.W. De Bock, K. Coussement, Remedying the Expiration of Churn Prediction Models with Multiple Classifier Algorithms. In Proceedings of Marketing Science (abstract), Boston (Massachusetts, USA), June 7-9 (2012).
  78. K. Coussement, M. Antioco, Managing Information Overload: The Case of Online Product Review Categorization. In Proceedings of Academy of Marketing Science Annual Conference (short paper), New Orleans (Louisiana, USA), May 15-20 (2012).
  79. K. Coussement, W. Buckinx, Calibration? Definition, Motivation and Insights Learned from a Direct Marketing Setting. In Proceedings of Marketing Science (abstract), Houston (Texas, USA), June 9-11 (2011).
  80. K. Coussement, D.F. Benoit, D. Van den Poel, Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction. In Proceedings of Academy of Marketing Science Annual Conference (short paper), Coral Gables (Florida, USA), May 24-27 (2011).
  81. K.W. De Bock, K. Coussement, D. Van den Poel, Ensemble Classification based on Generalized Additive Models. In Proceedings of Joint Statistical Meeting (ASA) (abstract), Vancouver (Canada), July 31-August 5 (2010).
  82. I. Ajili, K. Coussement, M. Limam, Effect of Incorporating Data Quality Matrices on Classification Mining. In Proceedings of Meeting on Statistics & Data Mining (short paper), Hammamet (Tunisia), March 11-12 (2010).
  83. K. Coussement, Customer Intelligence: Tapping the Vein of your Customers. In Proceedings of Meeting on Statistics & Data Mining (short paper invited talk), Hammamet (Tunisia), March 11-12 (2010).
  84. K. Coussement, D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features. In Proceedings of Marketing Science (abstract), Vancouver (Canada), June 12-14 (2008).
  85. K. Coussement, Employing SAS® Text Miner Methodology to Become a Customer Genius in Customer Churn Prediction Complaint E-mail Management. In Proceedings of SAS Global Forum 2008 (short paper – invited talk), San Antonio (Texas, USA), March 16-18 (2008).
  86. K. Coussement, D. Van den Poel, Combining Unstructured/Structured information into a traditional churn prediction model. In Proceedings of ISSPR 2007 (poster), Plymouth (UK), July 22-27 (2007).
  87. K. Coussement, D. Van den Poel, Integrating the voice of customers through call center emails into a churn predictions system. In Proceedings of Marketing Science (abstract), Singapore (Singapore), June 28-30 (2007).

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Invited Seminars

  1. K. Coussement, Towards a Data-Driven Organization: Empowering Leadership through Analytics, IESEG School of Management Executive Education Series for Lyreco, Lille (France), April 17 (2024).
  2. K. Coussement, Machine Learning for Central Banks, Austrian National Bank Inspiration Series, Vienna (Austria), March 21 (2024).
  3. K. Coussement, We Are Big Data: The Future of our Information Technology (invited), TIAS Business School, Tilburg (Netherlands), February, 27 (2024).
  4. K. Coussement, Text Analytics for Marketing/Business: Introduction, Publishing, Methods & Applications, Summer School HEC Liège, Liège (Belgium), September 11-12 (2023).
  5. K. Coussement, Text Analytics for Central Banks: Introduction, Methods & Applications, Summer School Workshop @ Joint Vienna Institute by Austrian National Bank, Vienna (Austria), August 28 – September 1 (2023).
  6. K. Coussement, The Pathway to Explainable and Responsible Business Analytics, IIT Madras Masterclass Series, IIT Madras, Chennai (India), April 4 (2023).
  7. K. Coussement, Moving Towards Explainable and Responsible Analytics (invited), Deakin MarTech Symposium Meeting 2023, Deakin Business School, Melbourne (Australia), March 2 (2023).
  8. K. Coussement, We Are Big Data: The Future of our Information Technology (invited), TIAS Business School, Tilburg (Netherlands), December, 13 (2022).
  9. K. Coussement, Towards Explainable Business Analytics Using Textual Data (invited), Data Science & Its Applications in Human & Social Sciences Conference, LEO & University of Orleans & MSH Val de Loire & Laboratoir Ligérien de Linguistique, Orléans (France), December 1 (2022).
  10. K. Coussement, The New Challenges of the Digital Transformation of Companies (invited), Ipsos R&D Series, Paris (France), October 6 (2022).
  11. K. Coussement, Kick-starting Your Analytical Journey (invited), University of Western Australia Big Data Seminar Series, Perth (Australia), April 7 (2022).
  12. K. Coussement, We Are Big Data: The Future of our Information Technology (invited), TIAS Business School, Tilburg (Netherlands), December, 22 (2021).
  13. K. Coussement, Interpretable Data Science for Decision Making: The Case for Customer Retention Management (invited keynote), 4th Analytics for Management and Economics Conference, Sint-Petersburg (Russia), December 8 (2021).
  14. K. Coussement, Do’s and Don’ts in Data Science Research (invited), Wilfrid Laurier University – Lazaridis Institute Research Series, Waterloo (Canada), May 26 (2021).
  15. K. Coussement, Customer Centricity in the Age of Big Data (invited), TIAS Business School, Tilburg (Netherlands), December, 15 (2020).
  16. K. Coussement, We Are Big Data: The Future of our Information Technology (invited), TIAS Business School, Tilburg (Netherlands), December, 17 (2019).
  17. K. Coussement, A Digestible Tutorial on Predictive Modeling Using Decision-tree Based Machine Learning Techniques (invited), University of Western Australia Doctoral Research Seminar Series (invited), Perth (Australia), April 3 (2019).
  18. K. Coussement, Learning Analytics: A Necessary Data-driven Tool in the Pedagogical Toolbox of Educators? (invited), University of Western Australia Research Seminar Series (invited), Perth (Australia), March 27 (2019).
  19. K. Coussement, We Are Big Data: The Future of our Information Technology (invited), TIAS Business School, Tilburg (Netherlands), December, 19 (2018).
  20. K. Coussement, What is the Strange Thing Called Hackathon, Pedagogical Café @ IESEG School of Management, November, 16 (2018).
  21. K. Coussement, T. Tse, Putting Data Analytics and AI into the Curriculum, AACSB EMEA Annual Conference (invited), Paris (France), October 29-31 (2018).
  22. K. Coussement, Finding the Hidden Secrets in Textual Customer Information through Text Mining, University of Western Australia Research Seminar Series (invited), Perth (Australia), March 14 (2018).
  23. K. Coussement, Customer Retention & Big Data Analytics: Are You Ready?, Enfocus Channel Event (invited), Naples (Italy), February 8 (2018).
  24. K. Coussement, Finding the Golden Nuggets in Textual Customer Information through Text Mining: The Why’s, What’s, How’s, Where’s, and Which’s Untangled, Wilfrid Laurier University – Lazaridis Institute Research Series (invited), Waterloo (Canada), September 22 (2017).
  25. K. Coussement, Text Mining in Marketing Analytics, S. P. Jain Institute of Management & Research Research Seminar Series (invited), Mumbai (India), December 12 (2016).
  26. K. Coussement, Understanding the Big Data Context in a Retail Context, Big Data Days @ Leroy Merlin (invited), Lezennes (France), November 3-4 (2016).
  27. K. Coussement, MSc in Big Data Analytics for Business @ IESEG School of Management: An Overview, AACSB Curriculum Development Series: Data Analytics Seminar (invited), Amsterdam (The Netherlands), April 18-19 (2016).
  28. K. Coussement, P. Cronan, A. Strauss, Panel: Guests from Analytics Companies, AACSB Curriculum Development Series: Data Analytics Seminar (invited), Amsterdam (The Netherlands), April 18-19 (2016).
  29. K. Coussement, P. Cronan, A. Strauss, Program Development: Interactive session, AACSB Curriculum Development Series: Data Analytics Seminar (invited), Amsterdam (The Netherlands), April 18-19 (2016).
  30. K. Coussement, P. Cronan, A. Strauss, Data Analytics in Business Curricula: What it is, what it is not, AACSB Curriculum Development Series: Data Analytics Seminar (invited), Amsterdam (The Netherlands), April 18-19 (2016).
  31. K. Coussement, MSc in Big Data Analytics for Business @ IESEG School of Management: An Overview (invited), AACSB Curriculum Development Series: Data Analytics Seminar, Tampa (United States), January 25-26 (2016).
  32. K. Coussement, Don’t Leave me this Way! Churn Prediction Modeling 2.0, Churn & Retention Management Conference (invited), Warsaw (Poland) , November 13-14 (2014).
  33. K. Coussement, Textual BIG data handling methods from social media: Introduction & marketing applications, University of Western Australia Research Seminar Series (invited), Perth (Australia), March 26 (2014).
  34. K. Coussement, Don’t Go Breaking my Heart! Customer Churn Prediction Screened, University of Western Australia Research Seminar Series (invited), Perth (Australia), June 17 (2013).
  35. K. Coussement, K.W. De Bock, I Am Begging You! Customer Churn Prediction using Generalized Additive Models, CCMS Research Seminar Series (invited), Namur (Belgium), October 26 (2012).
  36. K. Coussement, Nathalie Demoulin, Karine Charry, Marketing Research with SAS Enterprise Guide: A Practical Guide, SAS Forum France 2011 (invited), Paris (France), October 13-14 (2011).
  37. K. Coussement, K.W. De Bock, Please Don’t Go! An Empirical Investigation of Customer Churn Prediction using Generalized Additive Models, LEM Research Day (invited), Lille (France), June 14 (2011).
  38. K. Coussement, Sticking Customers to your Company through Customer Churn Prediction! The Beneficial Effect of Generalized Additive Models, Rouen Business School Seminar Series (invited), Rouen (France), February 17 (2011).
  39. K. Coussement, W. Buckinx, Increasing Marketing Relevance Through Personalized Offers, SAS Forum France 2010 (invited), Paris (France), October 14-15 (2010).
  40. K. Coussement, Text Mining and Customer Intelligence: Their Marriage Untangled!, SAS Forum France 2009 (invited), Paris (France), October 21-22 (2009).
  41. K. Coussement, GAMbag, GAMrfs and GAM Forest: Three Ensemble Classifiers Based on Generalized Additive Models, University College Brussels Research Series (invited), Brussels, (Belgium), April 28 (2009).
  42. K. Coussement, Customer Intelligence Untangled, IÉSEG Research Series (invited), IÉSEG School of Management, Lille (France), February 26 (2009).

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Working Papers

  1. K. Coussement, S. Lessmann, G. Verstraeten, Data Preparation Techniques for Customer Churn Prediction, IESEG Research Paper (2015-MKT-01), April 2015, p 47.
  2. K. Coussement, S. Lessmann, G. Verstraeten, Data Preparation Techniques for Customer Churn Prediction, LEM Research Paper (LEM-DP 2015-03), April 2015, p 47.
  3. S. Lessmann, K. Coussement, K.W. De Bock, Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection, IESEG Research paper (2013-MARK-06), October 2013, p 43.
  4. S. Lessmann, K. Coussement, K.W. De Bock, Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection, LEM Research paper (LEM-DP 2013-23), October 2013, p 43.
  5. K. Coussement, Improving Customer Retention Management through Cost-Sensitive Learning, IESEG Research paper (2013-MARK-01), February 2013, p 32.
  6. K. Coussement, Improving Customer Retention Management through Cost-Sensitive Learning, LEM Research paper (LEM DP 201303), February 2013, p 32.
  7. K. Coussement, F.A.M. Van den Bossche, K.W. De Bock, Data Accuracy’s Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees, IESEG Research paper (2012-MARK-02), October 2012, p 32.
  8. K. Coussement, F.A.M. Van den Bossche, K.W. De Bock, Data Accuracy’s Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees, LEM Research paper (LEM DP 201213), October 2012, p 32.
  9. K. Coussement, W. Buckinx, A Probability-Mapping Algorithm for Calibrating the Posterior Probabilities: A Direct Marketing Application, LEM Research paper (2011-06) May 2011, p 26.
  10. K. Coussement, W. Buckinx, A Probability-Mapping Algorithm for Calibrating the Posterior Probabilities: A Direct Marketing Application, IESEG Research paper (2011-MARK-01) May 2011, p 26.
  11. K.W. De Bock, K. Coussement, D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, IESEG Research paper (2010-MAN-01) January 2010, p 30.
  12. K.W. De Bock, K. Coussement, D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, HUB Research paper January 2010/02, p 30.
  13. K.W. De Bock, K. Coussement, D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, LEM Research paper (2010-02) January 2010, p 30.
  14. K.W. De Bock, K. Coussement, D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, UGent working paper series December 2009, p 30.
  15. K. Coussement, D.F. Benoit, D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, HUB Research paper August 2009/18, p 35.
  16. K. Coussement, D.F. Benoit, D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, UGent Working paper series July 2009, p 34.
  17. K. Coussement, D. Van den Poel, Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, UGent Working paper series July 2008, p 32.
  18. K. Coussement, D. Van den Poel, Integrating the voice of customers through call center emails into a decision support system for churn prediction, UGent Working paper series February 2008, p 27.
  19. K. Coussement, D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, UGent Working paper series September 2007, p 35.
  20. K. Coussement, D. Van den Poel, Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques, UGent Working paper series September 2006, p 55.

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Software

  1. A. De Caigny, K. Coussement, K.W. De Bock, LLM: Applies the Logit Leaf Model Classifier for Binary Classification, R Package & Reference Manual version 1.1.0 (2020) (LLM R package).
  2. K.W. De Bock, K. Coussement, S. Lessmann, CSMES: Cost-sensitive Multi-criteria Ensemble Selection for Uncertain Cost Conditions, R Package & Reference Manual version 1.0.0 (2020) (CSMES R package).
  3. K.W. De Bock, K. Coussement, D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification, R Package & Reference Manual version 1.2.1 (2018) (GAMens R package).
  4. A. De Caigny, K. Coussement, K.W. De Bock, LLM: Applies the Logit Leaf Model Classifier for Binary Classification, R Package & Reference Manual version 1.0 (2018) (LLM R package).
  5. K.W. De Bock, K. Coussement, D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification, R Package & Reference Manual version 1.2 (2016).
  6. K.W. De Bock, K. Coussement, D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification, R Package & Reference Manual version 1.1 (2010).

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Books

  1. K. Charry, K. Coussement, N. Demoulin, N. Heuvinck, Marketing Research with IBM SPSS Statistics (First Edition), Routledge – Taylor & Francis, Oxford (United Kingdom) (2016) (DOI, Google Books, Amazon).
  2. K. Coussement, P. Harrigan, All You Need Is True Love (With Your Customers)! A Customer Relationship Management Fairy Tale, University Press, Ghent (Belgium) (2014).
  3. K. Coussement, K.W. De Bock, Scott A. Neslin (Eds.), Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships, Gower Publishing, London (United Kingdom) (2013) (DOI, Google Books, Amazon) (translated in simplified Chinese. –, The China Enterprise Management Publishing House, Beijing (China) (2014)).
  4. K. Coussement, N. Demoulin, K. Charry, Marketing Research with SAS Enterprise Guide (First Edition), Gower Publishing, London (United Kingdom) (2011) (view DOI, Google Books, Amazon).
  5. K. Coussement, Issues in Customer Intelligence: Data and Method Creativity to Improve Marketing Decision Making, Doctoral Dissertation submitted to the Faculty of Economics & Business Administration, Ghent University Press (Belgium) (2008).

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Book Chapters

  1. K.W. De Bock, K. Coussement, D. Cielen, An Overview of Multiple Classifier Systems Based on Generalized Additive Models, in E.A. Cortés, M.G. Martinez, N.G. Rubio (eds) , Ensemble Classification Methods with Applications in R,  Wiley, (2018).
  2. K.W. De Bock, K. Coussement, 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, Paris (France) (2017).
  3. S. Debaere, K. Coussement, S. Van Neck, T. De Ruyck, Minority Report in Market Research Online Communities: The Future Can Be Seen, Member Disengagement Can Be Prevented, in Tim Macer et al.(Eds.), Association for Survey Computing (ASC) 2016:. Are We There Yet? Where Technological Innovation is Leading Research, ASC, Winchester (United Kingdom) (2016).
  4. N. Demoulin, K. Coussement, The Adoption of Text Mining Tools: A Strategic Choice for Companies with a Strong Focus on Client Focus (in French), In Survey Magazine (T2), Paris (France) (2016).
  5. O. Boujena, K. Coussement, K.W. De Bock, Data Driven Customer Centricity: CRM Predictive Analytics, in T. Tsiakis (Ed.), Trends and Innovations in Marketing Information Systems, IGI Global Publishing, Pennsylvania (USA) (2015).
  6. K. Coussement, K.W. De Bock, Textual Customer Data Handling for Quantitative Marketing Analysis, in K. Coussement, K.W. De Bock and Scott A. Neslin (Eds.), Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships, Gower Publishing, London (United Kingdom) (2013).
  7. K.W. De Bock, K. Coussement, Ensemble Learning in Database Marketing, in K. Coussement, K.W. De Bock and Scott A. Neslin (Eds.), Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships, Gower Publishing, London (United Kingdom) (2013).

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Case Studies

  1. K. Coussement, B. Vindevogel, Global.com: Building Analytical Capabilities in the Mobile Telecom Market, The Case Centre, teaching note 315-096-8 (2015), p 12.
  2. K. Coussement, B. Vindevogel, Global.com: Building Analytical Capabilities in the Mobile Telecom Market, The Case Centre, case study 315-096-1 (2015), p 7.
  3. K. Coussement, L. Zarantonello, Oh You Never Gonna Quit It: Chocolate! A Marketing Research Case Study. Part 2: Hypothesis Testing, Regression Analysis & Moderation/Mediation Analysis, The Case Centre, teaching note 513-081-1 (2013), p 19.
  4. K. Coussement, L. Zarantonello, Oh You Never Gonna Quit It: Chocolate! A Marketing Research Case Study. Part 2: Hypothesis Testing, Regression Analysis & Moderation/Mediation Analysis, The Case Centre, case study 513-081-1 (2013), p 16.
  5. K. Coussement, L. Zarantonello, Oh You Never Gonna Quit It: Chocolate! A Marketing Research Case Study. Part 1: Descriptive Statistics & Factor Analysis, The Case Centre, teaching note 513-080-8 (2013), p 22.
  6. K. Coussement, L. Zarantonello, Oh You Never Gonna Quit It: Chocolate! A Marketing Research Case Study. Part 1: Descriptive Statistics & Factor Analysis, The Case Centre, case study 513-080-1 (2013), p 14.
  7. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (C): One-to-one Marketing & Co-creation, European Case Clearing House (ECCH), teaching note 513-032-8 (2013), p 15.
  8. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (C): One-to-one Marketing & Co-creation, European Case Clearing House (ECCH), case study 513-032-1 (2013) p 22.
  9. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (B): Segmentation & Direct Marketing, European Case Clearing House (ECCH), teaching note 513-031-8 (2013), p 13.
  10. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (B): Segmentation & Direct Marketing, European Case Clearing House (ECCH), case study 513-031-1 (2013) p 25.
  11. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (A): Mass Marketing & Segmentation, European Case Clearing House (ECCH), teaching note 513-030-8 (2013), p 12.
  12. K. Coussement, P. Harrigan, The Princess and Her Quest for True Love! A CRM Fable: Part (A): Mass Marketing & Segmentation, European Case Clearing House (ECCH), case study 513-030-1 (2013) p 25.

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