Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Abolfazl Kazemi & Mohammad Esmaeil Babaei & Mahsa Oroojeni Mohammad Javad, 2015. "A data mining approach for turning potential customers into real ones in basket purchase analysis," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 19(2), pages 139-158.
- 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.
- K. Coussement & D. van den Poel, 2008. "Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques," Post-Print hal-00788096, HAL.
- McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- 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.
- 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.
- 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.
- Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017.
"Beyond average energy consumption in the French residential housing market: A household classification approach,"
Energy Policy, Elsevier, vol. 107(C), pages 82-95.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," Post-Print hal-01386095, HAL.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," Working Papers hal-02475511, HAL.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Post-Print hal-01586597, HAL.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," Working Papers hal-04141605, HAL.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," EconomiX Working Papers 2016-6, University of Paris Nanterre, EconomiX.
- Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," Post-Print hal-01386101, HAL.
- I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
- 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".
- 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.
- 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.
- 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.
- Slavescu, Ecaterina & Panait, Iulian, 2012. "Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry," MPRA Paper 44250, University Library of Munich, Germany.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
- 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.
- 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.
- Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.
- 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.
- 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.
More about this item
JEL classification:
- R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
- Z0 - Other Special Topics - - General
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:masjnl:v:11:y:2017:i:9:p:151. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.