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Kristof Coussement

Personal Details

First Name:Kristof
Middle Name:
Last Name:Coussement
Suffix:
RePEc Short-ID:pco265
http://www.kristofcoussement.com
3 Rue de la Digue F-59000 Lille France

Affiliation

(50%) IESEG School of Management
Université Catholique de Lille

Lille, France
http://www.ieseg.fr/
RePEc:edi:iesegfr (more details at EDIRC)

(50%) Lille Économie et Management (LEM)

Lille, France
http://lem.univ-lille.fr/
RePEc:edi:laborfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.
  2. K. W. De Bock & K. Coussement & D. Van Den Poel & -, 2009. "Ensemble classification based on generalized additive models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/625, Ghent University, Faculty of Economics and Business Administration.
  3. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.
  4. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
  5. K. Coussement & D. Van Den Poel, 2007. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/481, Ghent University, Faculty of Economics and Business Administration.
  6. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.

Articles

  1. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
  2. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
  3. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
  4. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Text mining in Wikipedia (English)
    2. Інтелектуальний аналіз тексту in Wikipedia (Ukranian)
  2. K. Coussement & D. Van Den Poel, 2007. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/481, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Text mining in Wikipedia (English)
    2. Інтелектуальний аналіз тексту in Wikipedia (Ukranian)

Working papers

  1. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. Matthias Bogaert & Michel Ballings & Martijn Hosten & Dirk Van den Poel, 2017. "Identifying Soccer Players on Facebook Through Predictive Analytics," Decision Analysis, INFORMS, vol. 14(4), pages 274-297, December.
    4. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    5. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
    6. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    7. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    8. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    9. Gaurav Gupta & Himanshu Aggarwal, 2016. "Analysing customer responses to migrate strategies in making retailing and CRM effective," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 92-127.
    10. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
    11. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    12. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    13. K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
    14. Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    15. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).

  2. K. W. De Bock & K. Coussement & D. Van Den Poel & -, 2009. "Ensemble classification based on generalized additive models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/625, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. Hugo Proença & João C. Neves, 2017. "Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 85-107, April.
    4. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    5. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
    6. Mustafa Karaköse & Özgün Yücel, 2024. "Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines," Energies, MDPI, vol. 17(21), pages 1-12, October.
    7. Adler, Werner & Brenning, Alexander & Potapov, Sergej & Schmid, Matthias & Lausen, Berthold, 2011. "Ensemble classification of paired data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1933-1941, May.
    8. Christmann, Andreas & Hable, Robert, 2012. "Consistency of support vector machines using additive kernels for additive models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 854-873.
    9. Murat Gök, 2015. "An ensemble of -nearest neighbours algorithm for detection of Parkinson's disease," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1108-1112, April.
    10. Narayanaswamy Balakrishnan & Majid Mojirsheibani, 2015. "A simple method for combining estimates to improve the overall error rates in classification," Computational Statistics, Springer, vol. 30(4), pages 1033-1049, December.
    11. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    12. K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
    13. Jasmit Shah & Somnath Datta & Susmita Datta, 2014. "A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics," Computational Statistics, Springer, vol. 29(6), pages 1749-1767, December.
    14. Mojirsheibani, Majid & Kong, Jiajie, 2016. "An asymptotically optimal kernel combined classifier," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 91-100.
    15. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.

  3. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Antioco, Michael & Coussement, Kristof, 2018. "Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias," International Journal of Information Management, Elsevier, vol. 38(1), pages 301-310.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. Aysun Kapucugil İkiz & Güzin Özdağoğlu, 2015. "Text Mining as a Supporting Process for VoC Clarification," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(1), pages 25-40, June.
    4. Park, Sangwon & Kim, Dae-Young, 2017. "Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 381-388.
    5. Kwon, Heeyeul & Kim, Jieun & Park, Yongtae, 2017. "Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology," Technovation, Elsevier, vol. 60, pages 15-28.
    6. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    7. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Alasdair Reid, 2023. "Closing the Affordable Housing Gap: Identifying the Barriers Hindering the Sustainable Design and Construction of Affordable Homes," Sustainability, MDPI, vol. 15(11), pages 1-27, May.
    9. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    10. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    11. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    12. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    13. Mirjana Pejić Bach & Živko Krstić & Sanja Seljan & Lejla Turulja, 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-27, February.
    14. Iva Salov & Aleksandra Krajnovic & Ante Panjkota, 2017. "Relation between Data Mining and Business Fields in the Four Dimensional CRM Model," MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017,, University of Primorska Press.
    15. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    16. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    17. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    18. Zhiyong Zhou & Jianhui Huang & Yao Lu & Hongcai Ma & Wenwen Li & Jianhong Chen, 2022. "A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    19. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    20. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    21. Capponi, Giovanna & Corrocher, Nicoletta & Zirulia, Lorenzo, 2021. "Personalized pricing for customer retention: Theory and evidence from mobile communication," Telecommunications Policy, Elsevier, vol. 45(1).
    22. Nathalie Demoulin & Kristof Coussement, 2018. "Acceptance of text-mining systems: The signaling role of information quality," Post-Print hal-02111772, HAL.
    23. Fawad Ahmed & Yuan Jian Qin & Luis Martínez, 2019. "Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses," Sustainability, MDPI, vol. 11(11), pages 1-28, May.
    24. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
    25. Md. Abdul Moktadir & Ashish Dwivedi & Akib Rahman & Charbel Jose Chiappetta Jabbour & Sanjoy Kumar Paul & Razia Sultana & Jitender Madaan, 2020. "An investigation of key performance indicators for operational excellence towards sustainability in the leather products industry," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3331-3351, December.
    26. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    27. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.

  4. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
    4. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    5. Senol Emir & Hasan Dincer & Umit Hacioglu & Serhat Yuksel, 2016. "Random Regression Forest Model using Technical Analysis Variables: An application on Turkish Banking Sector in Borsa Istanbul (BIST)," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(3), pages 85-102, April.
    6. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
    7. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    8. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    9. Magdalena Swart & Gerhard Roodt, 2015. "Market segmentation variables as moderators in the prediction of business tourist retention," Service Business, Springer;Pan-Pacific Business Association, vol. 9(3), pages 491-513, September.
    10. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    11. K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
    12. Ingrida Vaiciulyte & Zivile Kalsyte & Leonidas Sakalauskas & Darius Plikynas, 2017. "Assessment of market reaction on the share performance on the basis of its visualization in 2D space," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 309-318, March.
    13. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
    14. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2019. "Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions," Papers 1909.03792, arXiv.org, revised Sep 2019.
    15. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    16. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2020. "Tehran stock exchange prediction using sentiment analysis of online textual opinions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 22-37, January.
    17. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    18. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.

  5. K. Coussement & D. Van Den Poel, 2007. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/481, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. Jae-hyuck Lee & HaeOk Choi, 2020. "An Analysis of Public Complaints to Evaluate Ecosystem Services," Land, MDPI, vol. 9(3), pages 1-11, February.
    3. Yan, Nina & Xu, Xun & Tong, Tingting & Huang, Liujia, 2021. "Examining consumer complaints from an on-demand service platform," International Journal of Production Economics, Elsevier, vol. 237(C).
    4. HaeOk Choi, 2020. "Geospatial Data Approach for Demand-Oriented Policies of Land Administration," Land, MDPI, vol. 9(1), pages 1-12, January.
    5. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    6. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    8. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    9. Weng-Kun Liu & Chia-Chun Yen, 2016. "Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    10. Vairetti, Carla & Aránguiz, Ignacio & Maldonado, Sebastián & Karmy, Juan Pablo & Leal, Alonso, 2024. "Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1108-1118.
    11. Piera Centobelli & Roberto Cerchione & Emilio Esposito & Shashi, 2020. "Evaluating environmental sustainability strategies in freight transport and logistics industry," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1563-1574, March.
    12. Stefan Debortoli & Oliver Müller & Jan Brocke, 2014. "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 289-300, October.
    13. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.

  6. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    2. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    3. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.
    4. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    5. Fatemeh Akhyani & Alireza Komeili Birjandi & Reza Sheikh & Shib Sankar Sana, 2022. "New approach based on proximity/remoteness measurement for customer classification," Electronic Commerce Research, Springer, vol. 22(2), pages 267-298, June.
    6. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
    7. M. Ballings & D. Van Den Poel, 2012. "Kernel Factory: An Ensemble of Kernel Machines," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/825, Ghent University, Faculty of Economics and Business Administration.
    8. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    9. Udoinyang G. Inyang & Okure O. Obot & Moses E. Ekpenyong & Aliu M. Bolanle, 2017. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification," Modern Applied Science, Canadian Center of Science and Education, vol. 11(9), pages 151-151, September.
    10. Baumann, Elias & Kern, Jana & Lessmann, Stefan, 2019. "Usage Continuance in Software-as-a-Service," IRTG 1792 Discussion Papers 2019-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Parag C. Pendharkar, 2011. "Probabilistic Approaches For Credit Screening And Bankruptcy Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 177-193, October.
    12. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2009. "Identifying winners of competitive events: A SVM-based classification model for horserace prediction," European Journal of Operational Research, Elsevier, vol. 196(2), pages 569-577, July.
    13. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2013. "Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques," International Journal of Information Management, Elsevier, vol. 33(4), pages 627-641.
    14. Yen-Chun Chou & Howard Hao-Chun Chuang, 2018. "A predictive investigation of first-time customer retention in online reservation services," Service Business, Springer;Pan-Pacific Business Association, vol. 12(4), pages 685-699, December.
    15. Bikram Karmakar & Peng Liu & Gourab Mukherjee & Hai Che & Shantanu Dutta, 2022. "Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 102-133, January.
    16. E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
    17. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
    18. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    19. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    20. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
    21. Slãvescu Ecaterina Oana & Panait Iulian, 2012. "Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 1156-1160, May.
    22. Yookyung Boo & Youngjin Choi, 2021. "Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    23. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    24. V. L. Miguéis & D. Van Den Poel & A.S. Camanho & Joao Falcao E Cunha, 2012. "Predicting Partial Customer Churn Using Markov for Discrimination for Modeling First Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/806, Ghent University, Faculty of Economics and Business Administration.
    25. K. Coussement & D. van den Poel, 2009. "Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers," Post-Print halshs-00581595, HAL.
    26. Manojit Chattopadhyay & Subrata Kumar Mitra, 2017. "Applicability and effectiveness of classifications models for achieving the twin objectives of growth and outreach of microfinance institutions," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 451-474, December.
    27. V. L. Miguéis & D. Van Den Poel & A.S. Camanho & J. Falcao E Cunha, 2012. "Modeling Partial Customer Churn: On the Value of First Product-Category Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/790, Ghent University, Faculty of Economics and Business Administration.
    28. Edouard Ribes & Karim Touahri & Benoît Perthame, 2017. "Employee turnover prediction and retention policies design: a case study," Working Papers hal-01556746, HAL.
    29. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2010. "Alternative methods of predicting competitive events: An application in horserace betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 518-536, July.
    30. Muhammad Azeem & Muhammad Usman & A. C. M. Fong, 2017. "A churn prediction model for prepaid customers in telecom using fuzzy classifiers," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(4), pages 603-614, December.
    31. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    32. Jiayin Qi & Li Zhang & Yanping Liu & Ling Li & Yongpin Zhou & Yao Shen & Liang Liang & Huaizu Li, 2009. "ADTreesLogit model for customer churn prediction," Annals of Operations Research, Springer, vol. 168(1), pages 247-265, April.
    33. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    34. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    35. D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.
    36. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    37. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    38. Pedro Sobreiro & Pedro Guedes-Carvalho & Abel Santos & Paulo Pinheiro & Celina Gonçalves, 2021. "Predicting Fitness Centre Dropout," IJERPH, MDPI, vol. 18(19), pages 1-11, October.
    39. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
    40. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    41. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    42. Elias Baumann & Jana Kern & Stefan Lessmann, 2022. "Usage Continuance in Software-as-a-Service," Information Systems Frontiers, Springer, vol. 24(1), pages 149-176, February.
    43. K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
    44. Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    45. Tuğrul Çavdar & Zhaleh Sadreddini & Erkan Güler, 2018. "Pre-reservation based spectrum allocation for cognitive radio network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(4), pages 723-743, August.
    46. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    47. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    48. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    49. Xinmiao Li & Jing Li & Yukeng Wu, 2015. "A Global Optimization Approach to Multi-Polarity Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.

Articles

  1. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.

    Cited by:

    1. Chen, Yanhong & Liu, Luning & Zheng, Dequan & Li, Bin, 2023. "Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    2. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
    3. Wang, Ying & Lan, Jiahui & Pan, Jialing & Fang, Lin, 2024. "How do consumers’ attitudes differ across their basic characteristics toward live-streaming commerce of green agricultural products: A preliminary exploration based on correspondence analysis, logis," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    4. Lingfeng Dong & Ting Ji & Jie Zhang, 2022. "Effects of Conversation Politeness on Hiring Decision in Online Labor Markets: An Inverted U-Shaped Relationship Exploration," Sustainability, MDPI, vol. 14(22), pages 1-11, November.
    5. Horvat Ivan & Pejić Bach Mirjana & Merkač Skok Marjana, 2014. "Decision Tree Approach to Discovering Fraud in Leasing Agreements," Business Systems Research, Sciendo, vol. 5(2), pages 61-71, September.
    6. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
    7. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    8. Azarnoush Ansari & Arash Riasi, 2016. "Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(7), pages 1-59, June.
    9. Chen, Song & Qiu, Yongqin & Li, Jingmao & Fang, Kan & Fang, Kuangnan, 2023. "Precision marketing for financial industry using a PU-learning recommendation method," Journal of Business Research, Elsevier, vol. 160(C).
    10. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    11. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.

  2. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.

    Cited by:

    1. Ma, Rui & Mao, Di & Cao, Dongmei & Luo, Shuai & Gupta, Suraksha & Wang, Yichuan, 2024. "From vineyard to table: Uncovering wine quality for sales management through machine learning," Journal of Business Research, Elsevier, vol. 176(C).
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
    4. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    5. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
    6. Rehman, Obaid Ur & Zhou, Zihan & Wu, Kai & Li, Wen, 2024. "From courtrooms to corporations: The effect of bankruptcy court establishment on firm acquisitions," Finance Research Letters, Elsevier, vol. 61(C).
    7. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    8. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
    9. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
    10. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
    11. Meire, Matthijs, 2021. "Customer comeback: Empirical insights into the drivers and value of returning customers," Journal of Business Research, Elsevier, vol. 127(C), pages 193-205.
    12. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    13. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
    14. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    15. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    16. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
    17. Wee How Khoh & Ying Han Pang & Shih Yin Ooi & Lillian-Yee-Kiaw Wang & Quan Wei Poh, 2023. "Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    18. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    19. Steven Debaere & Floris Devriendt & Johanna Brunneder & Wouter Verbeke & Tom de Ruyck & Kristof Coussement, 2019. "Reducing inferior member community participation using uplift modeling: Evidence from a field experiment," Post-Print hal-02990787, HAL.
    20. Graham, Byron & Bonner, Karen, 2022. "One size fits all? Using machine learning to study heterogeneity and dominance in the determinants of early-stage entrepreneurship," Journal of Business Research, Elsevier, vol. 152(C), pages 42-59.
    21. Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021. "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 43-71, January.
    22. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    23. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    24. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2017. "‘You will like it!’ using open data to predict tourists' response to a tourist attraction," Tourism Management, Elsevier, vol. 60(C), pages 430-438.
    25. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    26. Wei Liu & Zongshui Wang & Hong Zhao, 2020. "Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 735-757, December.
    27. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.

  3. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.

    Cited by:

    1. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    2. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    3. Somayeh Moazeni & Boris Defourny & Monika J. Wilczak, 2020. "Sequential Learning in Designing Marketing Campaigns for Market Entry," Management Science, INFORMS, vol. 66(9), pages 4226-4245, September.
    4. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    6. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.

  4. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    See citations under working paper version above.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MKT: Marketing (4) 2006-11-12 2007-10-20 2008-04-04 2009-11-27
  2. NEP-ICT: Information and Communication Technologies (2) 2007-10-20 2008-04-04
  3. NEP-ECM: Econometrics (1) 2010-04-17
  4. NEP-FOR: Forecasting (1) 2009-11-27

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