Incorporating textual information in customer churn prediction models based on a convolutional neural network
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ijforecast.2019.03.029
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011.
"Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction,"
International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
- Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
- 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.
- West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
- Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
- 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.
- 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.
- Huang, Chun-Yao, 2012. "To model, or not to model: Forecasting for customer prioritization," International Journal of Forecasting, Elsevier, vol. 28(2), pages 497-506.
- West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803.
- Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner, 2012. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective," International Journal of Forecasting, Elsevier, vol. 28(2), pages 507-518.
- K. Coussement & K.W. de Bock & S.A. Neslin, 2013. "Advanced database marketing : innovative méthodologies and applications for managing Customer relationships," Post-Print hal-00821524, HAL.
- 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.
- Haenlein, Michael & Kaplan, Andreas M. & Beeser, Anemone J., 2007. "A Model to Determine Customer Lifetime Value in a Retail Banking Context," European Management Journal, Elsevier, vol. 25(3), pages 221-234, June.
- 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.
- Colin Bell & Kevin P. Jones, 1979. "Towards everyday language information retrieval systems via minicomputers," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 30(6), pages 334-339, November.
- Laukkanen, Tommi, 2016. "Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking," Journal of Business Research, Elsevier, vol. 69(7), pages 2432-2439.
- 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.
- 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.
- Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
- Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
- 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.
- Kristof Coussement & Stefan Lessmann & Geert Verstraeten, 2017. "A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry," Post-Print hal-01745261, HAL.
- 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.
- Dudyala Anil Kumar & V. Ravi, 2008. "Predicting credit card customer churn in banks using data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 4-28.
- Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
- 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.
- Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
- 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.
- Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
- Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
- Kristof Coussement & D.F. Benoit & M. Antioco, 2015. "A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection," Post-Print hal-02990768, HAL.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
- Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).
- 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.
- David Hason Rudd & Huan Huo & Md. Rafiqul Islam & Guandong Xu, 2023. "Churn Prediction via Multimodal Fusion Learning: Integrating Customer Financial Literacy, Voice, and Behavioral Data [Prédiction du churn par apprentissage fusionné multimodal : intégration de la l," Post-Print hal-04320145, HAL.
- Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
- Christopher Gerling & Stefan Lessmann, 2023. "Multimodal Document Analytics for Banking Process Automation," Papers 2307.11845, arXiv.org, revised Nov 2023.
- Gary Mena & Kristof Coussement & Koen W. Bock & Arno Caigny & Stefan Lessmann, 2024. "Exploiting time-varying RFM measures for customer churn prediction with deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 765-787, August.
- Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
- Ahn, Jungkyu & Ahn, Yongkil, 2023. "What drives the TIPS–Treasury bond mispricing?," Journal of Empirical Finance, Elsevier, vol. 74(C).
- Muhammad Zafran Muhammad Zaly Shah & Anazida Zainal & Taiseer Abdalla Elfadil Eisa & Hashim Albasheer & Fuad A. Ghaleb, 2023. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
- 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.
- K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.
- Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
- Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
- Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
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.- 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.
- Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
- K. W. De Bock & D. Van Den Poel, 2011.
"An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction,"
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium
11/717, Ghent University, Faculty of Economics and Business Administration.
- 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.
- Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Amin, Adnan & Shah, Babar & Khattak, Asad Masood & Lopes Moreira, Fernando Joaquim & Ali, Gohar & Rocha, Alvaro & Anwar, Sajid, 2019. "Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods," International Journal of Information Management, Elsevier, vol. 46(C), pages 304-319.
- 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".
- Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
More about this item
Keywords
Customer relationship management; Text mining; Predictive modeling; Deep learning; Financial services industry;All these keywords.
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:eee:intfor:v:36:y:2020:i:4:p:1563-1578. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.