Leveraging fine-grained transaction data for customer life event predictions
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DOI: 10.1016/j.dss.2019.113232
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- 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.
- K. Coussement & F.A.M. van den Bossche & K.W. de Bock, 2012. "Data Accuracy's Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees," Post-Print hal-00788060, HAL.
- 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.
- K. Coussement & D. van den Poel, 2008. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Post-Print hal-00788087, HAL.
- Buckinx, Wouter & Van den Poel, Dirk, 2005.
"Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting,"
European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
- W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
- 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.
- K.W. de Bock & D. van den Poel, 2012. "Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models," Post-Print hal-00800148, HAL.
- DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
- 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.
- Peppard, Joe, 2000. "Customer Relationship Management (CRM) in financial services," European Management Journal, Elsevier, vol. 18(3), pages 312-327, June.
- Eva Ascarza & Peter S. Fader & Bruce G. S. Hardie, 2017. "Marketing Models for the Customer-Centric Firm," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 297-329, Springer.
- Van den Poel, Dirk & Lariviere, Bart, 2004.
"Customer attrition analysis for financial services using proportional hazard models,"
European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
- D. Van Den Poel & B. Larivière, 2003. "Customer Attrition Analysis For Financial Services Using Proportional Hazard Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/164, Ghent University, Faculty of Economics and Business Administration.
- Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
- Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
- Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
- 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.
- K. Coussement & D. van den Poel, 2008.
"Integrating the voice of customers through call center emails into a decision support system for churn prediction,"
Post-Print
hal-00788086, HAL.
- 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.
- Lee, Euehun & Moschis, George P. & Mathur, Anil, 2001. "A study of life events and changes in patronage preferences," Journal of Business Research, Elsevier, vol. 54(1), pages 25-38, October.
- 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.
- Kristof Coussement & Paul Harrigan & Dries Benoit, 2015. "Improving direct mail targeting through customer response modeling," Post-Print hal-02990995, HAL.
- Verhoef, P.C. & Donkers, A.C.D., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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Keywords
Life event prediction; Predictive modeling; Pseudo-social networks; Customer relationship management (CRM); Big data; Data science;All these keywords.
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