Research


Published papers in refereed journals
Communications in refereed conferences
Other conference and seminar presentations
Working papers from universities and similar institutions
Research reports
Books
Published chapters in collective books
Case studies


Published papers in refereed international journals

  1. A. Bequé, K. Coussement, R. Gayler, S. Lessmann, Approaches for Credit Scorecard Calibration: An Empirical Analysis, Knowledge-based Systems, Forthcoming (2017).
  2. 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, Forthcoming (2017).
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. K. Coussement, Improving Customer Retention Management through Cost-sensitive Learning, European Journal of Marketing, 48 3/4 (2014), pp 477-495.
  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, Journal of Business Research, 67 (1) (2014), pp 2751-2758.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.

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Communications in refereed conferences

  1. 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).
  2. 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).
  3. 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).
  4. 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).
  5. 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).
  6. 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).
  7. 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).
  8. 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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. 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).
  14. 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).
  15. 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).
  16. 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).
  17. 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).
  18. 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).
  19. 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).
  20. 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).
  21. 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).
  22. K. Coussement, Improving Decision Tree Segmentation through Leaf Modeling in Direct Marketing, In Proceedings of Marketing Science (abstract), Istanbul (Turkey), July 11-13 (2013).
  23. 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.
  24. 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).
  25. 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).
  26. 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).
  27. 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).
  28. 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).
  29. 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).
  30. 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).
  31. 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).
  32. 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).
  33. 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).
  34. 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).
  35. 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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Other conference and seminar presentations

  1. K. Coussement, Text Mining in Marketing Analytics (invited), S. P. Jain Institute of Management & Research Research Seminar Series, Mumbai (India), December 12 (2016).
  2. K. Coussement, Understanding the Big Data Context in a Retail Context (invited), Big Data Days @ Leroy Merlin, Lezennes (France), November 3-4 (2016).
  3. 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).
  4. K. Coussement, MSc in Big Data Analytics for Business @ IESEG School of Management: An Overview (invited), AACSB Curriculum Development Series: Data Analytics Seminar, Amsterdam (The Netherlands), April 18-19 (2016).
  5. K. Coussement, P. Cronan, A. Strauss, Panel: Guests from Analytics Companies (invited), AACSB Curriculum Development Series: Data Analytics Seminar, Amsterdam (The Netherlands), April 18-19 (2016).
  6. K. Coussement, P. Cronan, A. Strauss, Program Development: Interactive session (invited), AACSB Curriculum Development Series: Data Analytics Seminar, Amsterdam (The Netherlands), April 18-19 (2016).
  7. K. Coussement, P. Cronan, A. Strauss, Data Analytics in Business Curricula: What it is, what it is not (invited), AACSB Curriculum Development Series: Data Analytics Seminar, Amsterdam (The Netherlands), April 18-19 (2016).
  8. 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).
  9. K. Coussement, Don’t Leave me this Way! Churn Prediction Modeling 2.0, Churn & Retention Management Conference (invited), Warsaw (Poland) , November 13-14 (2014).
  10. 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).
  11. 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).
  12. K. Coussement, K.W. De Bock, I Am Begging You! Customer Churn Prediction using Generalized Additive Models, CCMS Research Seminar Series, Namur (Belgium), October 26 (2012).
  13. K. Coussement, Nathalie Demoulin, Karine Charry, Marketing Research with SAS Enterprise Guide: A Practical Guide, SAS Forum France 2011, Paris (France), October 13-14 (2011).
  14. K. Coussement, K.W. De Bock, Please Don’t Go! An Empirical Investigation of Customer Churn Prediction using Generalized Additive Models, LEM Research Day, Lille (France), June 14 (2011).
  15. K. Coussement, Sticking Customers to your Company through Customer Churn Prediction! The Beneficial Effect of Generalized Additive Models, Rouen Business School, Rouen (France), February 17 (2011).
  16. K. Coussement, W. Buckinx, Increasing Marketing Relevance Through Personalized Offers, SAS Forum France 2010, Paris (France), October 14-15 (2010).
  17. K. Coussement, Text Mining and Customer Intelligence: Their Marriage Untangled!, SAS Forum France 2009 (invited talk), Paris (France), October 21-22 (2009).
  18. K.W. De Bock, K. Coussement, D. Van den Poel, GAMbag, GAMrfs and GAM Forest: Three Ensemble Classifiers Based on Generalized Additive Models, Faculty of Economics and Business Administration PhD Day (poster session), Ghent University, May 25 (2009).
  19. K. Coussement, GAMbag, GAMrfs and GAM Forest: Three Ensemble Classifiers Based on Generalized Additive Models, University College Brussels, April 28 (2009).
  20. K. Coussement, Customer Intelligence Untangled, IÉSEG School of Management, February 26 (2009).

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Working papers from universities and similar institutions

  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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Research reports

  1. K.W. De Bock, K. Coussement, D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification, R Reference Manual version 1.2 (2016).
  2. K.W. De Bock, K. Coussement, D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification, R 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).
  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) (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 book website or Google Books).
  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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Published chapters in collective books

  1. 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).
  2. 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).
  3. 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).
  4. 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).
  5. 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).
  6. 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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