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Dirk Van den Poel

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, 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)
  2. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Sequence alignment in Wikipedia (English)
    2. هم‌ترازسازی توالی in Wikipedia (Persian)
    3. Dizi hizalaması in Wikipedia (Turkish)
    4. Alineamiento de secuencias in Wikipedia (Spanish)
    5. Aliñamento de secuencias in Wikipedia (Galician)
    6. Sekuentzien lerrokatze in Wikipedia (Basque)
  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.

    Mentioned in:

    1. Text mining in Wikipedia (English)
    2. Інтелектуальний аналіз тексту in Wikipedia (Ukranian)
  4. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. 聚类分析 in Wikipedia (Chinese)
    2. هم‌ترازسازی توالی in Wikipedia (Persian)
    3. Dizi hizalaması in Wikipedia (Turkish)
    4. Customer attrition in Wikipedia (English)
    5. Sequence alignment in Wikipedia (English)
    6. Alineamiento de secuencias in Wikipedia (Spanish)
    7. Sekuentzien lerrokatze in Wikipedia (Basque)
    8. Aliñamento de secuencias in Wikipedia (Galician)
  5. 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.

    Mentioned in:

    1. Customer attrition in Wikipedia (English)
  6. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Mercadotecnia de bases de datos in Wikipedia (Spanish)
    2. Database marketing in Wikipedia (English)
  7. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Customer attrition in Wikipedia (English)
    2. Subscription business model in Wikipedia (English)
  8. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Clickstream in Wikipedia (Portuguese)
  9. W. Buckinx & G. Verstraeten & D. Van Den Poel, 2005. "Predicting Customer Loyalty Using The Internal Transactional Database," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/324, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. 忠诚度管理 in Wikipedia (Chinese)
    2. Loyalty business model in Wikipedia (English)
  10. B. Vindevogel & D. Van Den Poel & G. Wets, 2004. "Why promotion strategies based on market basket analysis do not work," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/262, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Loss leader in Wikipedia (English)
    2. Hàng bán câu khách in Wikipedia (Vietnamese)
    3. Produit d'appel in Wikipedia (French)
  11. B. Baesens & G. Verstraeten & D. Van Den Poel & M. Egmont-Petersen & P. Van Kenhove & J. Vanthienen, 2002. "Bayesian Network Classifiers for Identifying the Slope of the Customer - Lifecycle of Long-Life Customers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/154, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Artificial intelligence marketing in Wikipedia (English)
    2. Mercadotecnia de bases de datos in Wikipedia (Spanish)
    3. Database marketing in Wikipedia (English)
    4. Artificial intelligence marketing in Wikipedia (Italian)
  12. D. VAN DEN POEL & Jan J. DE SCHAMPHELAERE & G. WETS, 2003. "Direct and Indirect Effects of Retail Promotions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/202, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Loss leader in Wikipedia (English)
    2. Hàng bán câu khách in Wikipedia (Vietnamese)
    3. Produit d'appel in Wikipedia (French)
  13. D. Van Den Poel, 2003. "Predicting Mail-Order Repeat Buying: Which Variables Matter?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/191, Ghent University, Faculty of Economics and Business Administration.

    Mentioned in:

    1. Mercadotecnia de bases de datos in Wikipedia (Spanish)
    2. Database marketing in Wikipedia (English)

Working papers

  1. Matthijs Meire & Kelly Hewett & Michel Ballings & V. Kumar & Dirk van den Poel, 2019. "The Role of Marketer-Generated Content in Customer Engagement Marketing," Post-Print hal-02509303, HAL.

    Cited by:

    1. Lim, Weng Marc & Rasul, Tareq, 2022. "Customer engagement and social media: Revisiting the past to inform the future," Journal of Business Research, Elsevier, vol. 148(C), pages 325-342.
    2. Rajan, Bharath & Salunkhe, Uday & Kumar, V., 2023. "Understanding customer engagement in family firms: A conceptual framework," Journal of Business Research, Elsevier, vol. 154(C).
    3. Hartmann, Jochen & Heitmann, Mark & Siebert, Christian & Schamp, Christina, 2023. "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 75-87.
    4. Linda D. Hollebeek & V. Kumar & Rajendra K. Srivastava & Moira K. Clark, 2023. "Moving the stakeholder journey forward," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 23-49, January.
    5. Zheng, Zuolong & Li, Ziying & Zhang, Xuwen & Liang, Sai & Law, Rob & Lei, Jiasu, 2023. "Substitution or complementary effects between hosts and neighbors’ information disclosure: Evidence from Airbnb," Journal of Business Research, Elsevier, vol. 161(C).
    6. Bazi, Saleh & Filieri, Raffaele & Gorton, Matthew, 2023. "Social media content aesthetic quality and customer engagement: The mediating role of entertainment and impacts on brand love and loyalty," Journal of Business Research, Elsevier, vol. 160(C).
    7. Yuping Liu-Thompkins & Shintaro Okazaki & Hairong Li, 2022. "Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1198-1218, November.
    8. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris, 2022. "Examining the role of cross-cultural factors in the international market on customer engagement and purchase intention," Journal of International Management, Elsevier, vol. 28(3).
    9. Gandhi, Mohina & Kar, Arpan Kumar, 2022. "How do Fortune firms build a social presence on social media platforms? Insights from multi-modal analytics," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    10. Goic, Marcel & Rojas, Andrea & Saavedra, Ignacio, 2021. "The Effectiveness of Triggered Email Marketing in Addressing Browse Abandonments," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 118-145.
    11. Eigenraam, Anniek W. & Eelen, Jiska & Verlegh, Peeter W.J., 2021. "Let Me Entertain You? The Importance of Authenticity in Online Customer Engagement," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 53-68.
    12. Blanca I. Hernández-Ortega & Michael A. Stanko & Rishika Rishika & Francisco-Jose Molina-Castillo & José Franco, 2022. "Brand-generated social media content and its differential impact on loyalty program members," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 1071-1090, September.
    13. Wen Zhang & Yuting Yang & Huigang Liang, 2023. "A Bibliometric Analysis of Enterprise Social Media in Digital Economy: Research Hotspots and Trends," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    14. Wang, Fei & Xu, Haifeng & Hou, Ronglin & Zhu, Zhen, 2023. "Designing marketing content for social commerce to drive consumer purchase behaviors: A perspective from speech act theory," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    15. Dhaoui, Chedia & Webster, Cynthia M., 2021. "Brand and consumer engagement behaviors on Facebook brand pages: Let's have a (positive) conversation," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 155-175.
    16. Waqas, Muhammad & Salleh, Noor Akma Mohd & Hamzah, Zalfa Laili, 2021. "Branded Content Experience in Social Media: Conceptualization, Scale Development, and Validation," Journal of Interactive Marketing, Elsevier, vol. 56(C), pages 106-120.
    17. Markus Blut & Viktorija Kulikovskaja & Marco Hubert & Christian Brock & Dhruv Grewal, 2023. "Effectiveness of engagement initiatives across engagement platforms: A meta-analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(5), pages 941-965, September.
    18. Penttinen, Valeria, 2023. "Hi, I’m taking over this account! Leveraging social media takeovers in fostering consumer-brand relationships," Journal of Business Research, Elsevier, vol. 165(C).
    19. Daphne W. Yiu & William P. Wan & Kelly Xing Chen & Xiaocong Tian, 2022. "Public sentiment is everything: Host-country public sentiment toward home country and acquisition ownership during institutional transition," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(6), pages 1202-1227, August.
    20. Ren, Shengnan & Karimi, Sahar & Bravo Velázquez, Alberto & Cai, Jianfeng, 2023. "Endorsement effectiveness of different social media influencers: The moderating effect of brand competence and warmth," Journal of Business Research, Elsevier, vol. 156(C).
    21. Jordan W. Moffett & Judith Anne Garretson Folse & Robert W. Palmatier, 2021. "A theory of multiformat communication: mechanisms, dynamics, and strategies," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 441-461, May.
    22. Emini, Adelina & Zeqiri, Jusuf, 2021. "The Impact of Social Media Marketing on Purchase Intention in a Transition Economy: The Mediating Role of Brand Awareness and Brand Engagement," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2021), Hybrid Conference, Zagreb, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Zagreb, Croatia, 9-10 September 2021, pages 256-266, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    23. Jifeng Mu & Jonathan Zhang & Abhishek Borah & Jiayin Qi, 2022. "Creative Appeals in Firm-Generated Content and Product Performance," Information Systems Research, INFORMS, vol. 33(1), pages 18-42, March.
    24. 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.
    25. Ho, Xuan Huong & Nguyen, Dong Phong & Cheng, Julian Ming Sung & Le, Angelina Nhat Hanh, 2022. "Customer engagement in the context of retail mobile apps: A contingency model integrating spatial presence experience and its drivers," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    26. Kulikovskaja, Viktorija & Hubert, Marco & Grunert, Klaus G. & Zhao, Hong, 2023. "Driving marketing outcomes through social media-based customer engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    27. Xiuping Zhang & Jaewon Choi, 2022. "The Importance of Social Influencer-Generated Contents for User Cognition and Emotional Attachment: An Information Relevance Perspective," Sustainability, MDPI, vol. 14(11), pages 1-18, May.

  2. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Ossi Ylijoki, 2018. "Guidelines for assessing the value of a predictive algorithm: a case study," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 19-26, March.
    2. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    3. Peruchi, Diego Falcão & de Jesus Pacheco, Diego Augusto & Todeschini, Bruna Villa & ten Caten, Carla Schwengber, 2022. "Moving towards digital platforms revolution? Antecedents, determinants and conceptual framework for offline B2B networks," Journal of Business Research, Elsevier, vol. 142(C), pages 344-363.
    4. Meyer, Anne & Glock, Katharina & Radaschewski, Frank, 2021. "Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization," Omega, Elsevier, vol. 105(C).
    5. 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.

  3. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.

  4. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Irina V. Efimenko & Vladimir F. Khoroshevsky, 2017. "Peaks, Slopes, Canyons and Plateaus: Identifying Technology Trends Throughout the Life Cycle," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 1-28, April.
    2. Zbyslaw Dobrowolski, 2020. "Forensic Auditing and Weak Signals: A Cognitive Approach and Practical Tips," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 247-259.
    3. Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
    4. Kim, Jieun & Lee, Changyong, 2017. "Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 59-76.
    5. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    6. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    7. Zbyslaw Dobrowolski & Grzegorz Drozdowski & Monika Dobrowolska & Janusz Sobon & Dariusz Sobon, 2021. "Economic Calculus and Weak Signals: Prevention Against Foggy Bottom," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 165-174.
    8. Sun Hi Yoo & DongKyu Won, 2018. "Simulation of Weak Signals of Nanotechnology Innovation in Complex System," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    9. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    10. Kim, Hyunuk & Ahn, Sang-Jin & Jung, Woo-Sung, 2019. "Horizon scanning in policy research database with a probabilistic topic model," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 588-594.
    11. Bildosola, Iñaki & Río-Bélver, Rosa María & Garechana, Gaizka & Cilleruelo, Ernesto, 2017. "TeknoRoadmap, an approach for depicting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 25-37.

  5. M. De Beule & D. Van Den Poel & N. Van De Weghe, 2013. "An extended Huff-model for robustly benchmarking and predicting retail network performance," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/866, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Jay Mittal, 2017. "Valuing Visual Accessibility of Scenic Landscapes in a Single Family Housing Market: A Spatial Hedonic Approach," ERES eres2017_1, European Real Estate Society (ERES).
    2. Wieland, Thomas, 2014. "Räumliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Berücksichtigung von Agglomerationseffekten: Theoretische Erklärungsansätze, modellanalytische Zugänge und eine empirisch-ökonome," MPRA Paper 77163, University Library of Munich, Germany.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    4. Wieland, Thomas, 2015. "Nahversorgung im Kontext raumökonomischer Entwicklungen im Lebensmitteleinzelhandel: Konzeption und Durchführung einer GIS-gestützten Analyse der Strukturen des Lebensmitteleinzelhandels und der Nahve," MPRA Paper 77145, University Library of Munich, Germany.

  6. V. L. Miguéis & D. F. Benoit & D. Van Den Poel, 2012. "Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/803, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Yibei Li & Ximei Wang & Boualem Djehiche & Xiaoming Hu, 2019. "Credit Scoring by Incorporating Dynamic Networked Information," Papers 1905.11795, arXiv.org, revised Oct 2019.
    2. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).
    3. Vera L. Miguéis & Ana S. Camanho & José Borges, 2017. "Predicting direct marketing response in banking: comparison of class imbalance methods," Service Business, Springer;Pan-Pacific Business Association, vol. 11(4), pages 831-849, December.

  7. 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.

    Cited by:

    1. 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.
    2. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    11. 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.
    12. 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).
    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.

  8. 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.

    Cited by:

    1. 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.
    2. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    11. 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).
    12. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.

  9. B. Verhelst & D. Van Den Poel, 2012. "Implicit Contracts and Price Stickiness: Evidence from Customer-Level Scanner Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/776, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Levy, Daniel & Young, Andrew, 2019. "Promise, Trust and Betrayal: Costs of Breaching an Implicit Contract," MPRA Paper 94148, University Library of Munich, Germany.
    2. Benjamin Verhelst & Dirk Van den Poel, 2014. "Deep habits in consumption: a spatial panel analysis using scanner data," Empirical Economics, Springer, vol. 47(3), pages 959-976, November.
    3. Lein, Sarah Marit & Beck, Günter W., 2015. "Microeconometric evidence on demand-side real rigidity and implications for monetary non-neutrality," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113144, Verein für Socialpolitik / German Economic Association.
    4. Lein, Sarah & Beck, Günter, 2020. "Price elasticities and demand-side real rigidities in micro data and in macro models," CEPR Discussion Papers 14303, C.E.P.R. Discussion Papers.

  10. 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.

    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. Ahmet Tuz & Begum Sertyesilisik, 2020. "Finding and Minding the Gaps in State-Of-The-Art Lean and Green Marketing in the Construction Industry," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 32(2), pages 187-203.
    3. 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.
    4. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
    5. 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.
    6. Ohiomah, Alhassan & Andreev, Pavel & Benyoucef, Morad & Hood, David, 2019. "The role of lead management systems in inside sales performance," Journal of Business Research, Elsevier, vol. 102(C), pages 163-177.
    7. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.

  11. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    2. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    3. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    4. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    5. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.

  12. 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.

    Cited by:

    1. 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.
    2. Łapczyński Mariusz, 2014. "Hybrid C&RT-Logit Models In Churn Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 37-52, December.
    3. Uroš Droftina & Mitja Å tular & Andrej Košir, 2015. "A diffusion model for churn prediction based on sociometric theory," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 341-365, September.
    4. Katerina Shapoval & Thomas Setzer, 2018. "Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(2), pages 151-166, April.
    5. Miguel Angel de la Llave Montiel & Fernando López, 2020. "Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1643-1665, December.

  13. 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.

    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. 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.
    3. Dogah, Kingsley E. & Premaratne, Gamini, 2018. "Sectoral exposure of financial markets to oil risk factors in BRICS countries," Energy Economics, Elsevier, vol. 76(C), pages 228-256.
    4. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    5. 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.
    6. 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.
    7. Pal, Abhipsa & Herath, Tejaswini & De', Rahul & Raghav Rao, H., 2021. "Why do people use mobile payment technologies and why would they continue? An examination and implications from India," Research Policy, Elsevier, vol. 50(6).
    8. 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.
    9. Arthur J. Lin & Hai-Yen Chang & Sun-Weng Huang & Gwo-Hshiung Tzeng, 2021. "Criteria affecting Taiwan wealth management banks in serving high-net-worth individuals during COVID-19: a DEMATEL approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(4), pages 274-294, December.
    10. 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.
    11. Jiaming Liu & Chong Wu & Yongli Li, 2019. "Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 851-872, February.
    12. Yanhao Wei & Pinar Yildirim & Christophe Van den Bulte & Chrysanthos Dellarocas, 2016. "Credit Scoring with Social Network Data," Marketing Science, INFORMS, vol. 35(2), pages 234-258, March.
    13. 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.

  14. G. A. Verhaert & D. Van Den Poel, 2012. "The Role of Seed Money and Threshold Size in Optimizing Fundraising Campaigns: Past Behavior Matters!," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/815, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Minguez, Ana & Javier Sese, F., 2022. "Why do you want a relationship, anyway? Consent to receive marketing communications and donors’ willingness to engage with nonprofits," Journal of Business Research, Elsevier, vol. 148(C), pages 356-367.
    2. Matthew Donazzan & Nisvan Erkal & Boon Han Koh, 2016. "Impact of Rebates and Refunds on Contributions to Threshold Public Goods: Evidence from a Field Experiment," Southern Economic Journal, John Wiley & Sons, vol. 83(1), pages 69-86, July.
    3. Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2023. "Machine Learning and Fundraising: Applications of Artificial Neural Networks," Working Papers 2023: 33, Department of Economics, University of Venice "Ca' Foscari".
    4. Corina Haita-Falah, 2021. "Bygones in a public project," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 57(2), pages 229-256, August.

  15. 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.

    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.

  16. 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.

    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. 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.
    3. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.

  17. 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.

    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. 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.
    3. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Hemlata Jain & Ajay Khunteta & Sumit Srivastava, 2021. "Telecom churn prediction and used techniques, datasets and performance measures: a review," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(4), pages 613-630, April.
    9. 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).

  18. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    2. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    3. Young-Joo Lee & Ji-Young Park, 2018. "Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 653-667, March.
    4. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.

  19. 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.

    Cited by:

    1. 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.
    2. Takanobu Nakahara & Katsutoshi Yada, 2012. "Analyzing consumers’ shopping behavior using RFID data and pattern mining," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 355-365, December.
    3. 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.
    4. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    5. 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.
    6. 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.
    7. 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.
    8. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    9. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    10. Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
    11. Lázár, Ede, 2015. "Customer Churn Prediction Embedded in an Analytical CRM Model," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 258-264, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.

  20. B. Verhelst & D. Van Den Poel, 2012. "Deep Habits in Consumption: A Spatial Panel Analysis Using Scanner Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/823, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Daeha Cho & Kwang Hwan Kim, 2013. "Deep Habits, Rule-of-Thumb Consumers, and Fiscal Policy," Korean Economic Review, Korean Economic Association, vol. 29, pages 305-327.
    2. Inge van den Bijgaart, 2018. "Too Slow a Change? Deep Habits, Consumption Shifts and Transitory Tax Policy," CESifo Working Paper Series 6958, CESifo.
    3. Giovanni MELINA & Stefania VILLA, 2012. "Fiscal policy and lending relationships," Working Papers of Department of Economics, Leuven ces12.06, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    4. Yicong Li & Qiran Zhao & Tianchang Zhai & Wei Si, 2023. "Structural transition of protein intake in urban China: Stage characteristics and driving forces," Agribusiness, John Wiley & Sons, Ltd., vol. 39(S1), pages 1559-1577, December.
    5. Tzu-Ming Liu, 2020. "Habit formation or word of mouth: What does lagged dependent variable in tourism demand models imply?," Tourism Economics, , vol. 26(3), pages 461-474, May.
    6. van den Bijgaart, Inge, 2016. "Essays in environmental economics and policy," Other publications TiSEM 298bee2a-cb08-4173-9fe1-8, Tilburg University, School of Economics and Management.
    7. van den Bijgaart, I.M., 2017. "Too slow a change? Deep habits, consumption shifts and transitory tax," Working Papers in Economics 701, University of Gothenburg, Department of Economics.
    8. Xuepin Wu & Jiru Han, 2021. "Psychological Needs, Physiological Needs and Regional Comparison Effects," Sustainability, MDPI, vol. 13(16), pages 1-21, August.

  21. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    2. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.

  22. Dirk Van den Poel & Benjamin Verhelst, 2011. "Price Rigidity in Europe and the US: A Comparative Analysis Using Scanner Data," 2011 Meeting Papers 524, Society for Economic Dynamics.

    Cited by:

    1. Paolo Pasimeni, 2014. "An Optimum Currency Crisis," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 11(2), pages 173-204, December.
    2. Benjamin Verhelst & Dirk Van den Poel, 2014. "Deep habits in consumption: a spatial panel analysis using scanner data," Empirical Economics, Springer, vol. 47(3), pages 959-976, November.
    3. Bocionek, Milena & Anders, Sven M. & Kiesel, Kristin, 2012. "Estimating price rigidity in vertically differentiated food product categories with private labels," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124529, Agricultural and Applied Economics Association.
    4. B. Verhelst & D. Van Den Poel, 2012. "Implicit Contracts and Price Stickiness: Evidence from Customer-Level Scanner Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/776, Ghent University, Faculty of Economics and Business Administration.
    5. Luca Cacchiarelli & Alessandro Sorrentino, 2019. "Pricing Strategies in the Italian Retail Sector: The Case of Pasta," Social Sciences, MDPI, vol. 8(4), pages 1-13, April.

  23. 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.

    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. 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.
    3. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    4. Byungun Yoon & Taeyeoun Roh & Hyejin Jang & Dooseob Yun, 2019. "Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support," Sustainability, MDPI, vol. 11(16), pages 1-26, August.
    5. 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.
    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. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    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. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    10. Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    11. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    12. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    13. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    14. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.

  24. 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.

    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. 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.
    3. 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.
    4. Blaser, Rico & Fryzlewicz, Piotr, 2016. "Random rotation ensembles," LSE Research Online Documents on Economics 62182, London School of Economics and Political Science, LSE Library.
    5. 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.
    6. 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.
    7. Soyoung Park & Se-Yeong Hamm & Jinsoo Kim, 2019. "Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
    8. 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.
    9. 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.
    10. Adebiyi Sulaimon Olanrewaju & Oyatoye Emmanuel Olateju & Mojekwu Joseph Nnamdi, 2015. "Predicting Customer Churn and Retention Rates in Nigeria’s Mobile Telecommunication Industry Using Markov Chain Modelling," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 3(1), pages 67-80, December.
    11. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    12. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    13. 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).

  25. D. F. Benoit & D. Van Den Poel, 2010. "Binary quantile regression: A Bayesian approach based on the asymmetric Laplace density," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/662, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    2. R. Alhamzawi & K. Yu & D. F. Benoit, 2011. "Bayesian adaptive Lasso quantile regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/728, Ghent University, Faculty of Economics and Business Administration.
    3. Bresson, Georges & Lacroix, Guy & Arshad Rahman, Mohammad, 2020. "Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada," IZA Discussion Papers 12928, Institute of Labor Economics (IZA).
    4. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    5. Ivan Jeliazkov & Shubham Karnawat & Mohammad Arshad Rahman & Angela Vossmeyer, 2023. "Flexible Bayesian Quantile Analysis of Residential Rental Rates," Papers 2305.13687, arXiv.org, revised Sep 2023.

  26. K.W. de Bock & D. van den Poel, 2010. "Predicting website audience demographics for web advertising targeting using multi website clickstream data," Post-Print hal-00800168, HAL.

    Cited by:

    1. Moritz Zahn & Stefan Feuerriegel & Niklas Kuehl, 2022. "The Cost of Fairness in AI: Evidence from E-Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 335-348, June.
    2. Yaxi Liu & Dayu Cheng & Tao Pei & Hua Shu & Xianhui Ge & Ting Ma & Yunyan Du & Yang Ou & Meng Wang & Lianming Xu, 2020. "Inferring gender and age of customers in shopping malls via indoor positioning data," Environment and Planning B, , vol. 47(9), pages 1672-1689, November.
    3. 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.
    4. 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.

  27. G. A. Verhaert & D. Van Den Poel, 2010. "Empathy as Added Value in Predicting Donation Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/692, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Septianto, Felix & Tjiptono, Fandy, 2019. "The interactive effect of emotional appeals and past performance of a charity on the effectiveness of charitable advertising," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 189-198.
    2. Joerg Dietz & Emmanuelle Kleinlogel, 2014. "Wage Cuts and Managers’ Empathy: How a Positive Emotion Can Contribute to Positive Organizational Ethics in Difficult Times," Journal of Business Ethics, Springer, vol. 119(4), pages 461-472, February.
    3. Chuan, Amanda & Samek, Anya Savikhin, 2014. "“Feel the Warmth” glow: A field experiment on manipulating the act of giving," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 198-211.
    4. Nguyen, Thi Nguyet Que & Tran, Quan Ha Minh & Chylinski, Mathew, 2020. "Empathy and delight in a personal service setting," Australasian marketing journal, Elsevier, vol. 28(1), pages 11-17.
    5. Paramita, Widya & Septianto, Felix & Tjiptono, Fandy, 2020. "The distinct effects of gratitude and pride on donation choice and amount," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    6. Shohfi, Thomas D. & White, Roger M., 2022. "Does native country turmoil predict immigrant workers’ honesty in markets?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 150-164.
    7. Homer, Pamela Miles, 2021. "When sadness and hope work to motivate charitable giving," Journal of Business Research, Elsevier, vol. 133(C), pages 420-431.
    8. Ana C. Martinez-Levy & Dario Rossi & Giulia Cartocci & Marco Mancini & Gianluca Flumeri & Arianna Trettel & Fabio Babiloni & Patrizia Cherubino, 2022. "Message framing, non-conscious perception and effectiveness in non-profit advertising. Contribution by neuromarketing research," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 19(1), pages 53-75, March.
    9. Stenstrom, Eric P. & Saad, Gad & Hingston, Sean T., 2018. "Menstrual cycle effects on prosocial orientation, gift giving, and charitable giving," Journal of Business Research, Elsevier, vol. 84(C), pages 82-88.
    10. Dave Webb & Janine Wong, 2014. "Exploring Antecedents of Charitable Giving and Their Impact on Subjective Well-Being in Singapore," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(1), pages 65-87, May.
    11. G. A. Verhaert & D. Van Den Poel, 2012. "The Role of Seed Money and Threshold Size in Optimizing Fundraising Campaigns: Past Behavior Matters!," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/815, Ghent University, Faculty of Economics and Business Administration.
    12. Nakagawa, Koichi & Kosaka, Genjiro, 2022. "What social issues do people invest in? An examination based on the empathy–altruism hypothesis of prosocial crowdfunding platforms," Technovation, Elsevier, vol. 114(C).
    13. Barbara Culiberg, 2015. "The Role of Moral Philosophies and Value Orientations in Consumer Ethics: a Post-Transitional European Country Perspective," Journal of Consumer Policy, Springer, vol. 38(3), pages 211-228, September.
    14. Luis Pérez y Pérez & Pilar Egea, 2019. "About Intentions to Donate for Sustainable Rural Development: An Exploratory Study," Sustainability, MDPI, vol. 11(3), pages 1-14, February.
    15. Simon, Mark & Stanton, Steven J. & Townsend, Janell D. & Kim, John, 2019. "A multi-method study of social ties and crowdfunding success: Opening the black box to get the cash inside," Journal of Business Research, Elsevier, vol. 104(C), pages 206-214.
    16. Paramita, Widya & Septianto, Felix & Winahjoe, Sari & Purwanto, B.M. & Candra, Ika Diyah, 2020. "Sharing is (not) caring? The interactive effects of power and psychological distance on tolerance of unethical behavior," Australasian marketing journal, Elsevier, vol. 28(3), pages 42-49.
    17. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    18. Eric Van Steenburg & Nwamaka A. Anaza & Ahmed Ashhar & Andres Barrios & Ashley R. Deutsch & Meryl P. Gardner & Preeti Priya & Abhijit Roy & Anu Sivaraman & Kimberly A. Taylor, 2022. "The new world of philanthropy: How changing financial behavior, public policies, and COVID‐19 affect nonprofit fundraising and marketing," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(3), pages 1079-1105, September.
    19. Emerson Wagner Mainardes & Rozélia Laurett & Nívea Coelho Pereira Degasperi & Sarah Venturim Lasso, 2016. "What motivates an individual to make donations of money and / or goods?," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 13(1), pages 81-99, April.
    20. Hopkins, Christopher D. & Shanahan, Kevin J. & Raymond, Mary Anne, 2014. "The moderating role of religiosity on nonprofit advertising," Journal of Business Research, Elsevier, vol. 67(2), pages 23-31.
    21. Gugenishvili, Ilia & Nyström, Anna-Greta, 2023. "Virtual reality and charitable giving – the role of space, presence, and attention," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277967, International Telecommunications Society (ITS).
    22. Chen, Tong & Razzaq, Amar & Qing, Ping & Cao, Binbin, 2021. "Do you bear to reject them? The effect of anthropomorphism on empathy and consumer preference for unattractive produce," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    23. Septianto, Felix & Kemper, Joya A. & Chiew, Tung Moi, 2020. "The interactive effects of emotions and numerical information in increasing consumer support to conservation efforts," Journal of Business Research, Elsevier, vol. 110(C), pages 445-455.
    24. van Rijn, Jordan & Barham, Bradford & Sundaram-Stukel, Reka, 2017. "An experimental approach to comparing similarity- and guilt-based charitable appeals," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 25-40.
    25. Pasricha, Palvi & Nivedhitha, K.S. & Raghuvanshi, Juhi, 2023. "The perceived CSR-innovative behavior conundrum: Towards unlocking the socio-emotional black box," Journal of Business Research, Elsevier, vol. 161(C).
    26. Septianto, Felix, 2020. "Do past scandals influence the present performance? The moderating role of consumer mindset," Journal of Business Research, Elsevier, vol. 106(C), pages 75-81.
    27. Rapert, Molly Inhofe & Thyroff, Anastasia & Grace, Sarah C., 2021. "The generous consumer: Interpersonal generosity and pro-social dispositions as antecedents to cause-related purchase intentions," Journal of Business Research, Elsevier, vol. 132(C), pages 838-847.

  28. K.W. de Bock & K. Coussement & D. van den Poel, 2010. "Ensemble classification based on generalized additive models," Post-Print halshs-00581711, HAL.

    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Mojirsheibani, Majid & Kong, Jiajie, 2016. "An asymptotically optimal kernel combined classifier," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 91-100.
    13. 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.

  29. G. A. Verhaert & D. Van Den Poel, 2010. "Improving campaign success rate by tailoring donation requests along the donor lifecycle," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/666, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Li, Jing, 2023. "I’m feeling lucky: How randomly drawn suggested donations affect donation choice," Economics Letters, Elsevier, vol. 223(C).
    2. 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.
    3. Eric Van Steenburg & Nwamaka A. Anaza & Ahmed Ashhar & Andres Barrios & Ashley R. Deutsch & Meryl P. Gardner & Preeti Priya & Abhijit Roy & Anu Sivaraman & Kimberly A. Taylor, 2022. "The new world of philanthropy: How changing financial behavior, public policies, and COVID‐19 affect nonprofit fundraising and marketing," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(3), pages 1079-1105, September.
    4. Bataoui, Soffien & Boch, Emmanuelle, 2023. "The role of socially rich photos in generating favorable donation behavior on charity websites," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    5. Moon, Sangkil & Azizi, Kathryn, 2013. "Finding Donors by Relationship Fundraising," Journal of Interactive Marketing, Elsevier, vol. 27(2), pages 112-129.
    6. De Bruyn, Arnaud & Prokopec, Sonja, 2017. "Assimilation-contrast theory in action: Operationalization and managerial impact in a fundraising context," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 367-381.

  30. 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.

    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. 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.
    3. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.
    4. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    5. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. 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.
    7. 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.

  31. 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.

    Cited by:

    1. Haegeman, Karel & Marinelli, Elisabetta & Scapolo, Fabiana & Ricci, Andrea & Sokolov, Alexander, 2013. "Quantitative and qualitative approaches in Future-oriented Technology Analysis (FTA): From combination to integration?," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 386-397.
    2. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    3. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    4. Tamar C Weenen & Bahar Ramezanpour & Esther S Pronker & Harry Commandeur & Eric Claassen, 2013. "Food-Pharma Convergence in Medical Nutrition– Best of Both Worlds?," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
    5. Venugopalan, Subhashini & Rai, Varun, 2015. "Topic based classification and pattern identification in patents," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 236-250.
    6. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    7. Panula-Ontto, Juha & Luukkanen, Jyrki & Kaivo-oja, Jari & O'Mahony, Tadhg & Vehmas, Jarmo & Valkealahti, Seppo & Björkqvist, Tomas & Korpela, Timo & Järventausta, Pertti & Majanne, Yrjö & Kojo, Matti , 2018. "Cross-impact analysis of Finnish electricity system with increased renewables: Long-run energy policy challenges in balancing supply and consumption," Energy Policy, Elsevier, vol. 118(C), pages 504-513.
    8. Jongchan Kim & Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry," Sustainability, MDPI, vol. 8(5), pages 1-14, May.
    9. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    10. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    11. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    12. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    13. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    14. Gauch, Stephan & Blind, Knut, 2015. "Technological convergence and the absorptive capacity of standardisation," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 236-249.
    15. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.
    16. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    17. Panula-Ontto, J. & Piirainen, K.A., 2018. "EXIT: An alternative approach for structural cross-impact modeling and analysis," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 89-100.
    18. Kadaifci, Cigdem & Asan, Umut & Bozdag, Erhan, 2020. "A new 2-additive Choquet integral based approach to qualitative cross-impact analysis considering interaction effects," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    19. 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.
    20. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    21. Panula-Ontto, Juha, 2019. "The AXIOM approach for probabilistic and causal modeling with expert elicited inputs," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 292-308.

  32. K. Coussement & D.F. Benoît & D. van den Poel, 2010. "Improved marketing decision making in a customer churn prediction context using generalized additive models," Post-Print halshs-00581701, HAL.

    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. 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. 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.
    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. 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.
    14. 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).

  33. 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.

    Cited by:

    1. R. Ferrentino & M. T. Cuomo & C. Boniello, 2016. "On the customer lifetime value: a mathematical perspective," Computational Management Science, Springer, vol. 13(4), pages 521-539, October.
    2. Adela-Laura POPA & Dinu Vlad SASU & Teodora Mihaela TARCZA, 2021. "Investigating The Importance Of Customer Lifetime Value In Modern Marketing - A Literature Review," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 30(2), pages 410-416, December.
    3. 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.
    4. Andrew Phiri, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) approach," Working Papers 1702, Department of Economics, Nelson Mandela University, revised Jul 2017.
    5. Marlies Ahlert & Friedrich Breyer & Lars Schwettmann, 2013. "What You Ask is What You Get: Willingness-to-Pay for a QALY in Germany," CESifo Working Paper Series 4239, CESifo.
    6. Volpe, Richard III & Park, Timothy A. & Hennessy, David A. & Jensen, Helen H., 2013. "Somatic Cell Counts in Dairy Marketing: Quantile Regression for Count Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151425, Agricultural and Applied Economics Association.
    7. Charles-Olivier Amédée-Manesme & Michel Baroni & Fabrice Barthélémy & Francois des Rosiers, 2017. "Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach," Urban Studies, Urban Studies Journal Limited, vol. 54(14), pages 3260-3280, November.
    8. Adams, Kweku & Attah-Boakye, Rexford & Yu, Honglan & Johansson, Jeaneth & Njoya, Eric Tchouamou, 2023. "Female board representation and coupled open innovation: Evidence from emerging market multinational enterprises," Technovation, Elsevier, vol. 124(C).
    9. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    10. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    11. Cristina Davino & Vincenzo Esposito Vinzi, 2016. "Quantile composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 491-520, December.
    12. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    13. 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.
    14. Sharan Jagpal & Feihong Xia, 2019. "Coordinating Marketing and Production with Asymmetric Costs: Theory and Estimation," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(1), pages 1-12, June.
    15. Phiri, Andrew, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) model," MPRA Paper 79956, University Library of Munich, Germany.
    16. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    17. Maria Kubacka, 2020. "Review and Analysis of Selected Customer Value Measurement Methods (Przeglad i analiza wybranych metod pomiaru wartosci klienta)," Research Reports, University of Warsaw, Faculty of Management, vol. 1(32), pages 34-46.
    18. Chiang, Lan-Lung (Luke) & Yang, Chin-Sheng, 2018. "Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 177-187.
    19. Seung Hwan (Shawn) Lee, 2019. "An Exploration of Initial Purchase Price Dispersion and Service-Subscription Duration," Sustainability, MDPI, vol. 11(9), pages 1-14, April.
    20. 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.

  34. P. Baecke & D. Van Den Poel, 2009. "Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/596, Ghent University, Faculty of Economics and Business Administration.

    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. 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.
    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. 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.
    5. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.
    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. 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.
    8. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    9. 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.

  35. 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.

    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.

  36. A. Prinzie & D. Van Den Poel, 2009. "Modeling complex longitudinal consumer behavior with Dynamic Bayesian Networks: An Acquisition Pattern Analysis application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/607, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.

  37. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2009. "Mining Ideas from Textual Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/619, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    2. 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.
    3. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    4. 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.
    5. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    6. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.
    7. 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.

  38. 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.

    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.

  39. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, 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. 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.
    3. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    4. 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.
    5. 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.
    6. Guven, Faruk, 2018. "Churn and loyalty behaviour of Turkish digital natives," 29th European Regional ITS Conference, Trento 2018 184943, International Telecommunications Society (ITS).
    7. 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.

  40. 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.

    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.

  41. 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.

    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.

  42. A. Prinzie & D. Van Den Poel, 2007. "Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/469, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Luca Pappalardo & Paolo Cintia, 2018. "Quantifying The Relation Between Performance And Success In Soccer," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-30, May.

  43. B. Larivière & D. Van Den Poel, 2007. "Banking behaviour after the lifecycle event of “moving in together”: An exploratory study of the role of marketing investments," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/433, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. v. Wangenheim, Florian & Wünderlich, Nancy V. & Schumann, Jan H., 2017. "Renew or cancel? Drivers of customer renewal decisions for IT-based service contracts," Journal of Business Research, Elsevier, vol. 79(C), pages 181-188.
    2. Filippo Neri, 2020. "How to Identify Investor's types in real financial markets by means of agent based simulation," Papers 2101.03127, arXiv.org.

  44. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
    2. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    3. Michelsen, Carl Christian & Madlener, Reinhard, 2016. "Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany," Energy Policy, Elsevier, vol. 89(C), pages 95-105.
    4. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
    10. Joseph Guiltinan, 2010. "Consumer durables replacement decision-making: An overview and research agenda," Marketing Letters, Springer, vol. 21(2), pages 163-174, June.
    11. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.
    12. Katerina Shapoval & Thomas Setzer, 2018. "Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(2), pages 151-166, April.
    13. 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.
    14. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.

  45. J. Burez & D. Van Den Poel, 2007. "Separating Financial From Commercial Customer Churn: A Modeling Step Towards Resolving The Conflict Between The Sales And Credit Department," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/476, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.

  46. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. K.W. de Bock & K. Coussement & D. van den Poel, 2010. "Ensemble classification based on generalized additive models," Post-Print halshs-00581711, HAL.
    2. 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.
    3. 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.
    4. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    5. Tomáš Vantuch & Michal Prílepok & Jan Fulneček & Roman Hrbáč & Stanislav Mišák, 2019. "Towards the Text Compression Based Feature Extraction in High Impedance Fault Detection," Energies, MDPI, vol. 12(11), pages 1-13, June.
    6. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    7. 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.
    8. 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.

  47. Maarten Dossche & Freddy Heylen & Dirk Van den Poel, 2006. "The kinked demand curve and price rigidity : evidence from scanner data," Working Paper Research 99, National Bank of Belgium.

    Cited by:

    1. Maarten Dossche & Freddy Heylen & Dirk Van den Poel, 2006. "The kinked demand curve and price rigidity : evidence from scanner data," Working Paper Research 99, National Bank of Belgium.
    2. Sergey Kichko, 2018. "Competition, Land Price, and City Size," HSE Working papers WP BRP 190/EC/2018, National Research University Higher School of Economics.
    3. Vasconcelos, Helder & Brito, Duarte & Ribeiro, Ricardo, 2013. "Quantifying the Coordinated Effects of Partial Horizontal Acquisitions," CEPR Discussion Papers 9536, C.E.P.R. Discussion Papers.
    4. Takushi Kurozumi & Willem Van Zandweghe, 2021. "Macroeconomic Changes with Declining Trend Inflation: Complementarity with the Superstar Firm Hypothesis," Bank of Japan Working Paper Series 21-E-13, Bank of Japan.
    5. Takushi Kurozumi & Willem Van Zandweghe, 2023. "A Theory of Intrinsic Inflation Persistence," Bank of Japan Working Paper Series 23-E-3, Bank of Japan.
    6. Yasuo Hirose & Takushi Kurozumi & Wille Van Zandweghe, 2023. "Inflation Gap Persistence, Indeterminacy, and Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 867-887, December.
    7. Christopher J. Gust & Sylvain Leduc & Nathan Sheets, 2008. "The adjustment of global external balances: does partial exchange rate pass-through to trade prices matter?," Working Paper Series 2008-16, Federal Reserve Bank of San Francisco.
    8. Christian Hellwig & Ariel Burstein, 2007. "Prices and Market Shares in a Menu Cost Model," 2007 Meeting Papers 327, Society for Economic Dynamics.
    9. Andrew T. Levin & J. David López-Salido & Edward Nelson & Tack Yun, 2008. "Macroeconometric equivalence, microeconomic dissonance, and the design of monetary policy," Working Papers 2008-035, Federal Reserve Bank of St. Louis.
    10. Cosmin Ilut & Rosen Valchev & Nicolas Vincent, 2020. "Paralyzed by Fear: Rigid and Discrete Pricing Under Demand Uncertainty," Econometrica, Econometric Society, vol. 88(5), pages 1899-1938, September.
    11. Maiko Koga & Koichi Yoshino & Tomoya Sakata, 2020. "Strategic complementarity and asymmetric price setting among firms," BIS Papers chapters, in: Bank for International Settlements (ed.), Inflation dynamics in Asia and the Pacific, volume 111, pages 85-97, Bank for International Settlements.
    12. Peter J. Klenow & Jonathan L. Willis, 2006. "Real rigidities and nominal price changes," Research Working Paper RWP 06-03, Federal Reserve Bank of Kansas City.
    13. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2020. "Optimal Minimax Rates against Non-smooth Alternatives," KIER Working Papers 1051, Kyoto University, Institute of Economic Research.
    14. Steffen Ahrens & Inske Pirschel & Dennis J. Snower, 2014. "A Theory of Price Adjustment under Loss Aversion," SFB 649 Discussion Papers SFB649DP2014-065, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Luca Guerrieri & Christopher J. Gust & J. David López-Salido, 2008. "International competition and inflation: a New Keynesian perspective," International Finance Discussion Papers 918, Board of Governors of the Federal Reserve System (U.S.).
    16. Philippe Jeanfils, 2008. "Imperfect exchange rate pass-through : the role of distribution services and variable demand elasticity," Working Paper Research 135, National Bank of Belgium.
    17. Maarten Dossche, 2009. "Understanding inflation dynamics : Where do we stand ?," Working Paper Research 165, National Bank of Belgium.
    18. Biondi, Beatrice & Cornelsen, Laura & Mazzocchi, Mario & Smith, Richard, 2020. "Between preferences and references: Asymmetric price elasticities and the simulation of fiscal policies," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 108-128.
    19. Ogawa, Shogo, 2022. "Survey of non-Walrasian disequilibrium economic theory," MPRA Paper 115011, University Library of Munich, Germany.
    20. Benjamin Verhelst & Dirk Van den Poel, 2014. "Deep habits in consumption: a spatial panel analysis using scanner data," Empirical Economics, Springer, vol. 47(3), pages 959-976, November.
    21. Tack Yun & Andrew Levin, 2009. "Reconsidering the Microeconomic Foundations of Price-Setting Behavior," 2009 Meeting Papers 798, Society for Economic Dynamics.
    22. Michael K. Johnston, 2009. "Real and Nominal Frictions within the Firm: How Lumpy Investment Matters for Price Adjustment," Staff Working Papers 09-36, Bank of Canada.
    23. Linde, Jesper & Trabandt, Mathias, 2019. "Resolving the Missing Deflation Puzzle," CEPR Discussion Papers 13690, C.E.P.R. Discussion Papers.
    24. Lein, Sarah Marit & Beck, Günter W., 2015. "Microeconometric evidence on demand-side real rigidity and implications for monetary non-neutrality," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113144, Verein für Socialpolitik / German Economic Association.
    25. Iñaki Aguirre, 2008. "Output and misallocation effects in monopolistic third-degree price discrimination," Economics Bulletin, AccessEcon, vol. 4(11), pages 1-11.
    26. S. Dupraz, 2017. "A Kinked-Demand Theory of Price Rigidity," Working papers 656, Banque de France.
    27. Pazhanisamy, R., 2018. "Re examination of Kinked Demand Oligopoly Market: Theory, Evidence and Policy Implications from Lakshadweep," MPRA Paper 91176, University Library of Munich, Germany, revised 25 Dec 2018.
    28. Lein, Sarah & Beck, Günter, 2020. "Price elasticities and demand-side real rigidities in micro data and in macro models," CEPR Discussion Papers 14303, C.E.P.R. Discussion Papers.
    29. B. Verhelst & D. Van Den Poel, 2012. "Implicit Contracts and Price Stickiness: Evidence from Customer-Level Scanner Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/776, Ghent University, Faculty of Economics and Business Administration.
    30. Dirk Van den Poel & Benjamin Verhelst, 2011. "Price Rigidity in Europe and the US: A Comparative Analysis Using Scanner Data," 2011 Meeting Papers 524, Society for Economic Dynamics.
    31. Maiko Koga & Koichi Yoshino & Tomoya Sakata, 2019. "Strategic Complementarity and Asymmetric Price Setting among Firms," Bank of Japan Working Paper Series 19-E-5, Bank of Japan.
    32. Watson, Anna, 2016. "Trade openness and inflation: The role of real and nominal price rigidities," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 137-169.
    33. Vasconcelos, Helder & Brito, Duarte & Ribeiro, Ricardo, 2013. "Measuring Unilateral Effects in Partial Acquisitions," CEPR Discussion Papers 9354, C.E.P.R. Discussion Papers.
    34. Anna Watson, 2010. "The Impact of Trade Integration and Competition on Real and Nominal Price Rigidities: Insights from a New-Keynesian DSGE Model," DEGIT Conference Papers c015_061, DEGIT, Dynamics, Economic Growth, and International Trade.
    35. Gita Gopinath & Oleg Itskhoki, 2008. "Frequency of Price Adjustment and Pass-through," NBER Working Papers 14200, National Bureau of Economic Research, Inc.
    36. Yaman, Firat & Offiaeli, Kingsley, 2022. "Is the price elasticity of demand asymmetric? Evidence from public transport demand," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 318-335.
    37. Furlanetto Francesco & Seneca Martin, 2009. "Fiscal Shocks and Real Rigidities," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-33, February.
    38. Simon Mongey, 2017. "Market Structure and Monetary Non-neutrality," Staff Report 558, Federal Reserve Bank of Minneapolis.
    39. Geert Langenus, 2006. "Fiscal sustainability indicators and policy design in the face of ageing," Working Paper Research 102, National Bank of Belgium.
    40. Takushi Kurozumi & Willem Van Zandweghe, 2016. "Kinked Demand Curves, the Natural Rate Hypothesis, and Macroeconomic Stability," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 240-257, April.
    41. Luis C. Corchón & Ramón J. Torregrosa, 2022. "Two extensions of consumer surplus," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 557-579, September.
    42. Brito, Duarte & Ribeiro, Ricardo & Vasconcelos, Helder, 2014. "Measuring unilateral effects in partial horizontal acquisitions," International Journal of Industrial Organization, Elsevier, vol. 33(C), pages 22-36.

  48. A. Prinzie & D. Van Den Poel, 2006. "Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/390, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.

  49. G. Verstraeten & D. Van Den Poel, 2006. "Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/360, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.

  50. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.
    2. Guven, Faruk, 2018. "Churn and loyalty behaviour of Turkish digital natives," 29th European Regional ITS Conference, Trento 2018 184943, International Telecommunications Society (ITS).
    3. 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.
    4. Seung Hwan (Shawn) Lee, 2019. "An Exploration of Initial Purchase Price Dispersion and Service-Subscription Duration," Sustainability, MDPI, vol. 11(9), pages 1-14, April.

  51. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    2. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
    3. Cui, Geng & Wong, Man Leung & Wan, Xiang, 2015. "Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 27-37.

  52. W. Buckinx & G. Verstraeten & D. Van Den Poel, 2005. "Predicting Customer Loyalty Using The Internal Transactional Database," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/324, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. W. Bruggeman & P. Everaert & S. R. Anderson & Y. Levant, 2005. "Modeling Logistics Costs using Time-Driven ABC: A Case in a Distribution Company," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/332, Ghent University, Faculty of Economics and Business Administration.
    2. M. Vanhoucke & B. Maenhout, 2005. "Characterisation and Generation of Nurse Scheduling Problem Instances," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/339, Ghent University, Faculty of Economics and Business Administration.
    3. Jan Lepoutre & Nikolay Dentchev & Aimé Heene, 2007. "Dealing With Uncertainties When Governing CSR Policies," Journal of Business Ethics, Springer, vol. 73(4), pages 391-408, July.
    4. Ooghe, H. & Spaenjers, C. & Pieter vandermoere, 2005. "Business failure prediction: simple-intuitive models versus statistical models," Vlerick Leuven Gent Management School Working Paper Series 2005-22, Vlerick Leuven Gent Management School.
    5. Maenhout, B. & Vanhoucke, M., 2006. "New computational results for the nurse scheduling problem: A scatter search algorithm," Vlerick Leuven Gent Management School Working Paper Series 2006-06, Vlerick Leuven Gent Management School.
    6. Y. Samimi & A. Aghaie, 2010. "Monitoring heterogeneous serially correlated usage behavior in subscription-based services," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1761-1777.
    7. J. Albrecht & M. Neyt & T. Verbeke, 2005. "Bureaucratisation and the growth of health care expenditures in Europe," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/335, Ghent University, Faculty of Economics and Business Administration.
    8. L. Pozzi, 2005. "Income Uncertainty and Aggregate Consumption," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/334, Ghent University, Faculty of Economics and Business Administration.
    9. Dossche, Maarten & Everaert, Gerdie, 2005. "Measuring inflation persistence: a structural time series approach," Working Paper Series 495, European Central Bank.
    10. P. Windels & J. Christiaens, 2005. "Management Reform in Flemish Local Authorities: Testing the Institutional Framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/331, Ghent University, Faculty of Economics and Business Administration.
    11. A. Karas & K. Schoors, 2005. "Heracles or Sisyphus? Finding, cleaning and reconstructing a database of Russian banks," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/327, Ghent University, Faculty of Economics and Business Administration.
    12. N. Geeroms & P. Van Kenhove & W. Verbeke, 2005. "Health Advertising to promote Fruit and Vegetable Intake: Application of need-related Health Audience Segmentation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/336, Ghent University, Faculty of Economics and Business Administration.
    13. W. Buckinx & D. Van Den Poel, 2005. "Assessing and exploiting the profit function by modeling the net impact of targeted marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/330, Ghent University, Faculty of Economics and Business Administration.
    14. E. Labro & M. Vanhoucke, 2005. "A simulation analysis of interactions between errors in costing system design," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/333, Ghent University, Faculty of Economics and Business Administration.

  53. B. Larivière & D. Van Den Poel, 2005. "Investigating the post-complaint period by means of survival analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/299, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Mathieu Béal & William Sabadie & Yany Grégoire, 2019. "The effects of relationship length on customer profitability after a service recovery," Marketing Letters, Springer, vol. 30(3), pages 293-305, December.
    2. Jens Hogreve & Nicola Bilstein & Leonhard Mandl, 2017. "Unveiling the recovery time zone of tolerance: when time matters in service recovery," Journal of the Academy of Marketing Science, Springer, vol. 45(6), pages 866-883, November.

  54. W. Buckinx & D. Van Den Poel, 2005. "Assessing and exploiting the profit function by modeling the net impact of targeted marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/330, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. W. Bruggeman & P. Everaert & S. R. Anderson & Y. Levant, 2005. "Modeling Logistics Costs using Time-Driven ABC: A Case in a Distribution Company," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/332, Ghent University, Faculty of Economics and Business Administration.
    2. M. Vanhoucke & B. Maenhout, 2005. "Characterisation and Generation of Nurse Scheduling Problem Instances," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/339, Ghent University, Faculty of Economics and Business Administration.
    3. Jan Lepoutre & Nikolay Dentchev & Aimé Heene, 2007. "Dealing With Uncertainties When Governing CSR Policies," Journal of Business Ethics, Springer, vol. 73(4), pages 391-408, July.
    4. Ooghe, H. & Spaenjers, C. & Pieter vandermoere, 2005. "Business failure prediction: simple-intuitive models versus statistical models," Vlerick Leuven Gent Management School Working Paper Series 2005-22, Vlerick Leuven Gent Management School.
    5. Maenhout, B. & Vanhoucke, M., 2006. "New computational results for the nurse scheduling problem: A scatter search algorithm," Vlerick Leuven Gent Management School Working Paper Series 2006-06, Vlerick Leuven Gent Management School.
    6. J. Albrecht & M. Neyt & T. Verbeke, 2005. "Bureaucratisation and the growth of health care expenditures in Europe," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/335, Ghent University, Faculty of Economics and Business Administration.
    7. L. Pozzi, 2005. "Income Uncertainty and Aggregate Consumption," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/334, Ghent University, Faculty of Economics and Business Administration.
    8. Dossche, Maarten & Everaert, Gerdie, 2005. "Measuring inflation persistence: a structural time series approach," Working Paper Series 495, European Central Bank.
    9. P. Windels & J. Christiaens, 2005. "Management Reform in Flemish Local Authorities: Testing the Institutional Framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/331, Ghent University, Faculty of Economics and Business Administration.
    10. N. Geeroms & P. Van Kenhove & W. Verbeke, 2005. "Health Advertising to promote Fruit and Vegetable Intake: Application of need-related Health Audience Segmentation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/336, Ghent University, Faculty of Economics and Business Administration.
    11. E. Labro & M. Vanhoucke, 2005. "A simulation analysis of interactions between errors in costing system design," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/333, Ghent University, Faculty of Economics and Business Administration.

  55. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.
    2. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.

  56. B. Vindevogel & D. Van Den Poel & G. Wets, 2004. "Why promotion strategies based on market basket analysis do not work," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/262, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Leeflang, Peter S.H. & Parreño Selva, Josefa & Van Dijk, Albert & Wittink, Dick R., 2008. "Decomposing the sales promotion bump accounting for cross-category effects," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 201-214.
    2. B. Vindevogel & D. Van Den Poel & G. Wets, 2004. "Dynamic cross-sales effects of price promotions: Empirical generalizations," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/276, Ghent University, Faculty of Economics and Business Administration.

  57. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Vikram Ojha & JeongHoe Lee, 2021. "Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009," Digital Finance, Springer, vol. 3(3), pages 249-271, December.
    2. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    3. Justin A. Sirignano & Gerry Tsoukalas & Kay Giesecke, 2016. "Large-Scale Loan Portfolio Selection," Operations Research, INFORMS, vol. 64(6), pages 1239-1255, December.
    4. Pisanets Konstantin K., 2013. "Models of Assessment of the Credit Risk of Borrowers with a Time Parameter for the Systems of Application Credit Scoring," Business Inform, RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE), Kharkiv National University of Economics, issue 7, pages 136-140.
    5. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    6. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    7. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    8. R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
    9. Esther Calderon-Monge & Ivan Pastor-Sanz, 2017. "Effects of Contract and Trust on Franchisor Performance," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 11(4), December.
    10. T H Moon & S Y Sohn, 2011. "Survival analysis for technology credit scoring adjusting total perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1159-1168, June.
    11. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020.
    12. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    13. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.

  58. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. 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.
    2. 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.
    3. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    4. 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.
    5. 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.
    6. 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.
    7. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    8. 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.

  59. G. Verstraeten & D. Van Den Poel, 2004. "The Impact of Sample Bias on Consumer Credit Scoring Performance and Profitability," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/232, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Gero Szepannek, 2022. "An Overview on the Landscape of R Packages for Open Source Scorecard Modelling," Risks, MDPI, vol. 10(3), pages 1-33, March.
    2. Karol Przanowski, 2014. "Credit acceptance process strategy case studies - the power of Credit Scoring," Papers 1403.6531, arXiv.org.
    3. Hand, David J. & Crowder, Martin J., 2012. "Overcoming selectivity bias in evaluating new fraud detection systems for revolving credit operations," International Journal of Forecasting, Elsevier, vol. 28(1), pages 216-223.
    4. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
    5. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    6. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
    7. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.

  60. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.

    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. Asamoah, Kwadwo, 2016. "On the credibility of insurance claim frequency: Generalized count models and parametric estimators," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 339-353.
    3. 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.
    4. Ali Dehghan & Theodore Trafalis, 2012. "Examining Churn and Loyalty Using Support Vector Machine," Business and Management Research, Business and Management Research, Sciedu Press, vol. 1(4), pages 153-161, December.
    5. 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.
    6. 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.
    7. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    8. 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.
    9. 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.
    10. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    11. Alvaro Arroyo & Alvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2023. "Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers," Papers 2306.05479, arXiv.org.
    12. B. Larivière & D. Van Den Poel, 2005. "Investigating the post-complaint period by means of survival analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/299, Ghent University, Faculty of Economics and Business Administration.
    13. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    14. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    15. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    16. Atakan Yalcin & Lerzan Aksoy & Timothy L. Keiningham & Bart Larivière & Sunil Mithas & Forrest V. Morgeson III, 2012. "The Satisfaction, Repurchase Intention and Shareholder Value Linkage: A Longitudinal Examination of Fixed and Firm Specific Effects," EcoMod2012 4543, EcoMod.
    17. Liu, Meijun & Hu, Xiao & Wang, Yuandi & Shi, Dongbo, 2018. "Survive or perish: Investigating the life cycle of academic journals from 1950 to 2013 using survival analysis methods," Journal of Informetrics, Elsevier, vol. 12(1), pages 344-364.

  61. T. Van Gestel & B. Baesens & J. A.K. Suykens & D. Van Den Poel & D.-E. Baestaens & Bm. Willekens, 2004. "Bayesian Kernel-Based Classification for Financial Distress Detection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/247, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    2. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    3. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
    4. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
    5. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    6. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
    7. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    8. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
    9. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    10. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
    11. 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.
    12. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    13. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    14. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
    15. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    16. 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.
    17. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    18. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

  62. 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.

    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. Uner, M.Mithat & Guven, Faruk & Cavusgil, S.Tamer, 2020. "Churn and loyalty behavior of Turkish digital natives: Empirical insights and managerial implications," Telecommunications Policy, Elsevier, vol. 44(4).
    6. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    7. 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.
    8. Makoto Abe, 2015. "Deriving Customer Lifetime Value from RFM Measures:Insights into Customer Retention and Acquisition," CIRJE F-Series CIRJE-F-962, CIRJE, Faculty of Economics, University of Tokyo.
    9. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    10. Yeung, Alice H.W. & Lo, Victor H.Y. & Yeung, Andy C.L. & Cheng, T.C. Edwin, 2008. "Specific customer knowledge and operational performance in apparel manufacturing," International Journal of Production Economics, Elsevier, vol. 114(2), pages 520-533, August.
    11. 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.
    12. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    13. Bogomolova, Svetlana, 2016. "Determinants of ex-customer winback in financial services," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 1-6.
    14. Hanen Khanchel & Karim Ben Kahla, 2019. "Job Dissatisfaction and Turnover Crises in Tunisia," Business and Management Research, Business and Management Research, Sciedu Press, vol. 8(3), pages 53-73, September.
    15. 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.
    16. Melis, Kristina & Campo, Katia & Breugelmans, Els & Lamey, Lien, 2015. "The Impact of the Multi-channel Retail Mix on Online Store Choice: Does Online Experience Matter?," Journal of Retailing, Elsevier, vol. 91(2), pages 272-288.
    17. 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.
    18. 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.
    19. 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.
    20. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
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    22. López-Díaz, María Concepción & López-Díaz, Miguel & Martínez-Fernández, Sergio, 2023. "On the optimal binary classifier with an application," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    30. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
    31. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    32. Behrooz Hashemian & Emanuele Massaro & Iva Bojic & Juan Murillo Arias & Stanislav Sobolevsky & Carlo Ratti, 2017. "Socioeconomic characterization of regions through the lens of individual financial transactions," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-20, November.
    33. H-Y Tsao & P-C Lin & L Pitt & C Campbell, 2009. "The impact of loyalty and promotion effects on retention rate," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 646-651, May.
    34. Vicente G. Cancho & Dipak K. Dey & Francisco Louzada, 2016. "Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 572-584, March.
    35. Łapczyński Mariusz, 2014. "Hybrid C&RT-Logit Models In Churn Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 37-52, December.
    36. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
    37. 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.
    38. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    39. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    40. H-Y Tsao & L Pitt & C Campbell, 2010. "Analysing consumer segments to budget for loyalty and promotion programmes and maximize market share," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1523-1529, October.
    41. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2017. "Modeling reward expiry for loyalty programs in a competitive market," International Journal of Production Economics, Elsevier, vol. 193(C), pages 352-364.
    42. Gandomi, A. & Zolfaghari, S., 2013. "Profitability of loyalty reward programs: An analytical investigation," Omega, Elsevier, vol. 41(4), pages 797-807.
    43. Guven, Faruk, 2018. "Churn and loyalty behaviour of Turkish digital natives," 29th European Regional ITS Conference, Trento 2018 184943, International Telecommunications Society (ITS).
    44. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    45. Hsiu-Yuan Tsao & Lucy M. Matthews & Victoria L. Crittenden, 2012. "Balancing Market Share Growth And Customer Profitability: Budget Allocation For Customer Acquisition And Retention," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 3(2).
    46. Gázquez-Abad, Juan Carlos & Canniére, Marie Hélène De & Martínez-López, Francisco J., 2011. "Dynamics of Customer Response to Promotional and Relational Direct Mailings from an Apparel Retailer: The Moderating Role of Relationship Strength," Journal of Retailing, Elsevier, vol. 87(2), pages 166-181.
    47. 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.
    48. Marko Sarstedt & Sebastian Scharf & Alexander Thamm & Michael Wolff, 2010. "Die Prognose von Serviceintervallen mit der Hazard-Raten-Analyse – Ergebnisse einer empirischen Studie im Automobilmarkt," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 20(3), pages 269-283, April.
    49. 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.
    50. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2018. "‘Buy n times, get one free’ loyalty cards: Are they profitable for competing firms? A game theoretic analysis," European Journal of Operational Research, Elsevier, vol. 265(2), pages 621-630.
    51. 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.
    52. 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.
    53. Duan Lianjie, 2023. "Export Cutoff Productivity, Uncertainty and Duration of Waiting for Exporting," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-19, January.
    54. Leonardo José Silveira & Plácido Rogério Pinheiro & Leopoldo Soares de Melo Junior, 2021. "A Novel Model Structured on Predictive Churn Methods in a Banking Organization," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    55. 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.
    56. Tan, Pei Jie & Corsi, Armando & Cohen, Justin & Sharp, Anne & Lockshin, Larry & Caruso, William & Bogomolova, Svetlana, 2018. "Assessing the sales effectiveness of differently located endcaps in a supermarket," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 200-208.
    57. 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.
    58. Shane S. Dikolli & William R. Kinney & Karen L. Sedatole, 2007. "Measuring Customer Relationship Value: The Role of Switching Cost," Contemporary Accounting Research, John Wiley & Sons, vol. 24(1), pages 93-132, March.
    59. Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
    60. Miguel Angel de la Llave Montiel & Fernando López, 2020. "Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1643-1665, December.
    61. Daniel Baier & Ines Daniel & Sarah Frost & Robert Naundorf, 2012. "Image data analysis and classification in marketing," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 253-276, December.
    62. Philipp Brüggemann & Nina Lehmann-Zschunke, 2023. "How to reduce termination on freemium platforms—literature review and empirical analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 707-721, December.
    63. 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.
    64. Edin Osmanbegovic & Anel Dzinic & Mirza Suljic, 2022. "Prediction Of Telecom Services Consumers Churn By Using Machine Learning Algorithms," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 20(2), pages 53-64, November.
    65. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
    66. 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.
    67. Leslie Hannah & Makoto Kasuya, 2015. "Twentieth Century Enterprise Forms: Japan in Comparative Perspective," CIRJE F-Series CIRJE-F-966, CIRJE, Faculty of Economics, University of Tokyo.
    68. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    69. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.

  63. P. Van Kenhove & K. De Wulf & D. Van Den Poelt, 2003. "Does Attitudinal Commitment to Stores Always Lead to Behavioral Loyalty? The Moderating Effect of Age," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/168, Ghent University, Faculty of Economics and Business Administration.

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    1. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    2. Baltas, George & Argouslidis, Paraskevas C. & Skarmeas, Dionysis, 2010. "The Role of Customer Factors in Multiple Store Patronage: A Cost–Benefit Approach," Journal of Retailing, Elsevier, vol. 86(1), pages 37-50.

  64. 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.

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    1. Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    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. Dhananjay Bapat, 2012. "Customer Relationship for Electronic Payment Products," Global Business Review, International Management Institute, vol. 13(1), pages 137-151, February.
    6. A. Prinzie & D. Van Den Poel, 2003. "Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/213, Ghent University, Faculty of Economics and Business Administration.
    7. Alexei A. Gaivoronski & Per Jonny Nesse & Olai Bendik Erdal, 2017. "Internet service provision and content services: paid peering and competition between internet providers," Netnomics, Springer, vol. 18(1), pages 43-79, May.
    8. Gianna Giudicati & Massimo Riccaboni & Anna Romiti, 2013. "Experience, socialization and customer retention: Lessons from the dance floor," Marketing Letters, Springer, vol. 24(4), pages 409-422, December.
    9. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    10. 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.
    11. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    12. Asadi, Majid & Ebrahimi, Nader & Soofi, Ehsan S., 2018. "Optimal hazard models based on partial information," European Journal of Operational Research, Elsevier, vol. 270(2), pages 723-733.
    13. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
    14. Amir Gandomi & Amirhossein Bazargan & Saeed Zolfaghari, 2019. "Designing competitive loyalty programs: a stochastic game-theoretic model to guide the choice of reward structure," Annals of Operations Research, Springer, vol. 280(1), pages 267-298, September.
    15. Andreea Dumitrache & Denisa Maria Melian & Stelian Stancu, 2020. "Churn Prepaid Client Profile in Romanian Postmodernism Telecommunications," Postmodern Openings, Editura Lumen, Department of Economics, vol. 11(2Sup1), pages 93-106, September.
    16. 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.
    17. Lariviere, Bart & Van den Poel, Dirk, 2007. "Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments," European Journal of Operational Research, Elsevier, vol. 183(1), pages 345-369, November.
    18. Boehm, Martin, 2008. "Determining the impact of internet channel use on a customer's lifetime," Journal of Interactive Marketing, Elsevier, vol. 22(3), pages 2-22.
    19. Devigne, David & Manigart, Sophie & Wright, Mike, 2016. "Escalation of commitment in venture capital decision making: Differentiating between domestic and international investors," Journal of Business Venturing, Elsevier, vol. 31(3), pages 253-271.
    20. 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.
    21. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    22. 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.
    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. 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.
    25. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
    26. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
    27. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.
    28. 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.
    29. Łapczyński Mariusz, 2014. "Hybrid C&RT-Logit Models In Churn Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 37-52, December.
    30. Jose Angelo Divino & Edna Souza Lima & Jaime Orrillo, 2013. "Interest rates and default in unsecured loan markets," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1925-1934, December.
    31. B. Larivière & D. Van Den Poel, 2005. "Investigating the post-complaint period by means of survival analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/299, Ghent University, Faculty of Economics and Business Administration.
    32. 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.
    33. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    34. Demirbag, Mehmet & Apaydin, Marina & Tatoglu, Ekrem, 2011. "Survival of Japanese subsidiaries in the Middle East and North Africa," Journal of World Business, Elsevier, vol. 46(4), pages 411-425, October.
    35. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2017. "Modeling reward expiry for loyalty programs in a competitive market," International Journal of Production Economics, Elsevier, vol. 193(C), pages 352-364.
    36. Gandomi, A. & Zolfaghari, S., 2013. "Profitability of loyalty reward programs: An analytical investigation," Omega, Elsevier, vol. 41(4), pages 797-807.
    37. Karthik Sridhar & Ram Bezawada & Minakshi Trivedi, 2012. "Investigating the Drivers of Consumer Cross-Category Learning for New Products Using Multiple Data Sets," Marketing Science, INFORMS, vol. 31(4), pages 668-688, July.
    38. Guven, Faruk, 2018. "Churn and loyalty behaviour of Turkish digital natives," 29th European Regional ITS Conference, Trento 2018 184943, International Telecommunications Society (ITS).
    39. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    40. 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.
    41. 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.
    42. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    43. Matthew Jaremski, 2010. "Free Bank Failures: Risky Bonds versus Undiversified Portfolios," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(8), pages 1565-1587, December.
    44. I. Fustos & R. Abarca-del-Rio & P. Moreno-Yaeger & M. Somos-Valenzuela, 2020. "Rainfall-Induced Landslides forecast using local precipitation and global climate indexes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 115-131, May.
    45. Vijayakumar Bharathi S & Dhanya Pramod & Ramakrishnan Raman, 2022. "An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers," Data, MDPI, vol. 7(5), pages 1-15, May.
    46. 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.
    47. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    48. Christopher, Gandrud, 2011. "Competing risks analysis and deposit insurance governance convergence," MPRA Paper 36087, University Library of Munich, Germany.
    49. 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.
    50. Alisa Bilal Zoric, 2016. "Predicting customer churn in banking industry using neural networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 116-124.
    51. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    52. 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.
    53. Vishal Shukla & Sanjeev Prashar & Bhartrihari Pandiya, 2022. "Is price a significant predictor of the churn behavior during the global pandemic? A predictive modeling on the telecom industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(4), pages 470-483, August.
    54. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).
    55. Shane S. Dikolli & William R. Kinney & Karen L. Sedatole, 2007. "Measuring Customer Relationship Value: The Role of Switching Cost," Contemporary Accounting Research, John Wiley & Sons, vol. 24(1), pages 93-132, March.
    56. Gaivoronski, Alexei A. & Nesse, Per-Jonny & Østerbo, Olav-Norvald & Lønsethagen, Håkon, 2016. "Risk-balanced dimensioning and pricing of End-to-End differentiated services," European Journal of Operational Research, Elsevier, vol. 254(2), pages 644-655.
    57. Ruhanen, Lisa & Whitford, Michelle & McLennan, Char-lee, 2015. "Indigenous tourism in Australia: Time for a reality check," Tourism Management, Elsevier, vol. 48(C), pages 73-83.
    58. Stewart R. Miller & Douglas E. Thomas & Lorraine Eden & Michael Hitt, 2008. "Knee Deep in the Big Muddy: The Survival of Emerging Market Firms in Developed Markets," Management International Review, Springer, vol. 48(6), pages 645-666, December.
    59. Abril, Carmen & Sanchez, Joaquin, 2016. "Will they return? Getting private label consumers to come back: Price, promotion, and new product effects," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 109-116.
    60. 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.
    61. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.

  65. D. Van Den Poel, 2003. "Predicting Mail-Order Repeat Buying: Which Variables Matter?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/191, Ghent University, Faculty of Economics and Business Administration.

    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. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    3. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    4. 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.
    5. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    6. 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.
    7. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
    8. De Cannière, Marie Hélène & De Pelsmacker, Patrick & Geuens, Maggie, 2009. "Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior," Journal of Business Research, Elsevier, vol. 62(1), pages 82-92, January.
    9. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.

  66. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    2. 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.
    3. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    4. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    5. G. A. Verhaert & D. Van Den Poel, 2012. "The Role of Seed Money and Threshold Size in Optimizing Fundraising Campaigns: Past Behavior Matters!," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/815, Ghent University, Faculty of Economics and Business Administration.
    6. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    7. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.

  67. A. Prinzie & D. Van Den Poel, 2003. "Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/213, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Claudia Elena DINUCA, 2011. "Using web mining in e-commerce applications," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 3, pages 65-74, September.
    2. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.

  68. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Perera K.J.T. & Fernando P.I.N. & Ratnayake R.M.C.S. & Udawaththa U.D.I.C., 2021. "Consumer Behavior within the Covid-19 Pandemic A Systematic Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(12), pages 806-812, December.
    2. 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.
    3. Pelin Atahan & Sumit Sarkar, 2011. "Accelerated Learning of User Profiles," Management Science, INFORMS, vol. 57(2), pages 215-239, February.
    4. Anjali Singh & Ajay Kumar, 2021. "Designing the marketspace for millennials: fun, functionality or risk?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 311-327, December.
    5. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    6. Cao, XinYu & Mokhtarian, Patricia L, 2005. "The Intended and Actual Adoption of Online Purchasing: A Brief Review of Recent Literature," Institute of Transportation Studies, Working Paper Series qt095934s0, Institute of Transportation Studies, UC Davis.
    7. Katarzyna Szalonka & Agnieszka Sadowa & Aleksandra Wicka & Ludwik Wicki, 2020. "E-Commerce Purchasing Behaviour and the Level of Consumers‘ Income in Poland and Great Britain," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 552-568.
    8. 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.
    9. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    10. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    11. 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.
    12. 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.
    13. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    14. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    15. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Reimer, Kerstin & Albers, Sönke, 2011. "Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing," EconStor Preprints 50730, ZBW - Leibniz Information Centre for Economics.
    17. 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.
    18. 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.
    19. Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.
    20. Mohamed R. Smaoui, 2017. "A Novel Method to Investigate the Effect of Social Network “Hook” Images on Purchasing Prospects in E-Commerce," Complexity, Hindawi, vol. 2017, pages 1-16, October.
    21. Pallant, Jason I. & Danaher, Peter J. & Sands, Sean J. & Danaher, Tracey S., 2017. "An empirical analysis of factors that influence retail website visit types," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 62-70.
    22. Ioan-Sebastian Brumă & Cristina Cautisanu & Lucian Tanasă & Simona-Roxana Ulman & Meda Gâlea & Alexandra Raluca Jelea, 2024. "Does the payment method matter in online shopping behaviour? Study on the Romanian market of vegetables during the pandemic crisis," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(1), pages 34-47.
    23. Annika Baumann & Johannes Haupt & Fabian Gebert & Stefan Lessmann, 2019. "The Price of Privacy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 413-431, August.
    24. Tang, Wei & Mokhtarian, Patricia L, 2009. "Accounting for Taste Heterogeneity in Purchase Channel Intention Modeling: An Example from Northern California for Book Purchases," Institute of Transportation Studies, Working Paper Series qt9mg5s5g8, Institute of Transportation Studies, UC Davis.
    25. Anderl, Eva & Schumann, Jan Hendrik & Kunz, Werner, 2016. "Helping Firms Reduce Complexity in Multichannel Online Data: A New Taxonomy-Based Approach for Customer Journeys," Journal of Retailing, Elsevier, vol. 92(2), pages 185-203.
    26. Grażyna Suchacka & Grzegorz Chodak, 0. "Using association rules to assess purchase probability in online stores," Information Systems and e-Business Management, Springer, vol. 0, pages 1-30.
    27. Agatz, N.A.H. & Fleischmann, M. & van Nunen, J.A.E.E., 2006. "E-Fulfillment and Multi-Channel Distribution – A Review," ERIM Report Series Research in Management ERS-2006-042-LIS, 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.
    28. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    29. Hélia Gonçalves Pereira & Maria Fátima Salgueiro & Paulo Rita, 2017. "Online determinants of e-customer satisfaction: application to website purchases in tourism," Service Business, Springer;Pan-Pacific Business Association, vol. 11(2), pages 375-403, June.
    30. 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.
    31. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    32. Katerina Shapoval & Thomas Setzer, 2018. "Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(2), pages 151-166, April.
    33. Tsung‐Sheng Chang & Wei‐Hung Hsiao, 2014. "Time Spent on Social Networking Sites: Understanding User Behavior and Social Capital," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(1), pages 102-114, January.
    34. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    35. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
    36. 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.
    37. Xu, Xianhao & Shen, Yaohan & (Amanda) Chen, Wanying & Gong, Yeming & Wang, Hongwei, 2021. "Data-driven decision and analytics of collection and delivery point location problems for online retailers," Omega, Elsevier, vol. 100(C).

  69. D. VAN DEN POEL & Jan J. DE SCHAMPHELAERE & G. WETS, 2003. "Direct and Indirect Effects of Retail Promotions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/202, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. Juan C. Gázquez-Abad & Manuel Sánchez-Pérez, 2009. "Factors influencing olive oil brand choice in Spain: an empirical analysis using scanner data," Agribusiness, John Wiley & Sons, Ltd., vol. 25(1), pages 36-55.
    2. Sandro Shelegia, 2008. "Pricing Interrelated Goods in Oligopoly," Working Papers 014-08, International School of Economics at TSU, Tbilisi, Republic of Georgia.
    3. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    4. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    5. B. Vindevogel & D. Van Den Poel & G. Wets, 2004. "Why promotion strategies based on market basket analysis do not work," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/262, Ghent University, Faculty of Economics and Business Administration.
    6. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    7. B. Vindevogel & D. Van Den Poel & G. Wets, 2004. "Dynamic cross-sales effects of price promotions: Empirical generalizations," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/276, Ghent University, Faculty of Economics and Business Administration.

  70. B. Baesens & G. Verstraeten & D. Van Den Poel & M. Egmont-Petersen & P. Van Kenhove & J. Vanthienen, 2002. "Bayesian Network Classifiers for Identifying the Slope of the Customer - Lifecycle of Long-Life Customers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/154, Ghent University, Faculty of Economics and Business Administration.

    Cited by:

    1. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    2. Md. Rabbi & Syed Mithun Ali & Golam Kabir & Zuhayer Mahtab & Sanjoy Kumar Paul, 2020. "Green Supply Chain Performance Prediction Using a Bayesian Belief Network," Sustainability, MDPI, vol. 12(3), pages 1-19, February.
    3. Lee, Changyong & Song, Bomi & Park, Yongtae, 2015. "An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 285-301.
    4. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
    5. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    6. 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.
    7. Ülengin, Füsun & Önsel, Şule & Aktas, Emel & Kabak, Özgür & Özaydın, Özay, 2014. "A decision support methodology to enhance the competitiveness of the Turkish automotive industry," European Journal of Operational Research, Elsevier, vol. 234(3), pages 789-801.
    8. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    9. Wu, Wei-Wen & Lan, Lawrence W. & Lee, Yu-Ting, 2012. "Exploring the critical pillars and causal relations within the NRI: An innovative approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 230-238.
    10. 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.
    11. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    12. 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.
    13. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    14. Muhammad Naeem Anjum & Bi Xiuchun & Jaffar Abbas & Zhang Shuguang, 2017. "Analyzing predictors of customer satisfaction and assessment of retail banking problems in Pakistan," Cogent Business & Management, Taylor & Francis Journals, vol. 4(1), pages 1338842-133, January.
    15. Loureiro, Sandra Maria Correia & Guerreiro, João & Tussyadiah, Iis, 2021. "Artificial intelligence in business: State of the art and future research agenda," Journal of Business Research, Elsevier, vol. 129(C), pages 911-926.
    16. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
    17. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    18. Jonker, J.-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Arbore, Alessandro & Busacca, Bruno, 2009. "Customer satisfaction and dissatisfaction in retail banking: Exploring the asymmetric impact of attribute performances," Journal of Retailing and Consumer Services, Elsevier, vol. 16(4), pages 271-280.
    20. Budsaratragoon, Pornanong & Jitmaneeroj, Boonlert, 2020. "A critique on the Corruption Perceptions Index: An interdisciplinary approach," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    21. 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.
    22. M. Neyt & J. Albrecht & B. Clarysse & V. Cocquyt, 2003. "The Cost-Effectiveness of Herceptin® in a Standard Cost Model for Breast-Cancer Treatment in a Belgian University Hospital," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/180, Ghent University, Faculty of Economics and Business Administration.

  71. Jonker, J.-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    2. Jonker, J.-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," ERIM Report Series Research in Management ERS-2002-111-LIS, 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.
    3. Jonker, J.-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," Econometric Institute Research Papers ERS-2002-111-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. G. A. Verhaert & D. Van Den Poel, 2010. "Empathy as Added Value in Predicting Donation Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/692, Ghent University, Faculty of Economics and Business Administration.
    5. G. A. Verhaert & D. Van Den Poel, 2012. "The Role of Seed Money and Threshold Size in Optimizing Fundraising Campaigns: Past Behavior Matters!," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/815, Ghent University, Faculty of Economics and Business Administration.
    6. Yeliz Ekinci & Füsun Ulengin & Nimet Uray, 2014. "Using customer lifetime value to plan optimal promotions," The Service Industries Journal, Taylor & Francis Journals, vol. 34(2), pages 103-122, January.

Articles

  1. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.

    Cited by:

    1. Gupta, Mukul & Kumar, Pradeep, 2020. "Recommendation generation using personalized weight of meta-paths in heterogeneous information networks," European Journal of Operational Research, Elsevier, vol. 284(2), pages 660-674.
    2. 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.

  2. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.

    Cited by:

    1. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    2. Anastasiia O. Khlobystova & Maxim V. Abramov & Tatiana V. Tulupyevа & Alexander L. Tulupyev, 2019. "Social Influence on the User in Social Network: Types of Communications in Assessment of the Behavioral Risks connected with the Socio-engineering Attacks," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 3.
    3. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    5. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    6. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.

  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.

    Cited by:

    1. Sangjae Lee & Kun Chang Lee & Joon Yeon Choeh, 2020. "Using Bayesian Network to Predict Online Review Helpfulness," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    2. Ali E. Abbas & Jay Simon & Chris Smith, 2017. "Introduction to the Special Issue on Decision Analysis and Social Media," Decision Analysis, INFORMS, vol. 14(4), pages 227-228, December.
    3. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    4. Vicki M. Bier & Simon French, 2020. "From the Editors: Decision Analysis Focus and Trends," Decision Analysis, INFORMS, vol. 17(1), pages 1-8, March.

  4. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).

    Cited by:

    1. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    2. Dejan Živkov & Boris Kuzman & Jonel Subić, 2020. "What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(5), pages 215-225.
    3. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
    4. Mani Suleiman & Haydar Demirhan & Leanne Boyd & Federico Girosi & Vural Aksakalli, 2022. "Bayesian prediction of emergency department wait time," Health Care Management Science, Springer, vol. 25(2), pages 275-290, June.
    5. Iddrisu, Abdul-Aziz & Alagidede, Imhotep Paul, 2020. "Monetary policy and food inflation in South Africa: A quantile regression analysis," Food Policy, Elsevier, vol. 91(C).
    6. Xiaocang Xu & Linhong Chen, 2019. "Projection of Long-Term Care Costs in China, 2020–2050: Based on the Bayesian Quantile Regression Method," Sustainability, MDPI, vol. 11(13), pages 1-13, June.
    7. Asharani Samal & Phanindra Goyari, 2022. "Does Monetary Policy Stabilise Food Inflation in India? Evidence From Quantile Regression Analysis," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(3), pages 361-372, September.
    8. Yang Yang & Thian Yew Gan & Xuezhi Tan, 2021. "Recent changing characteristics of dry and wet spells in Canada," Climatic Change, Springer, vol. 165(3), pages 1-21, April.

  5. Ballings, Michel & Van den Poel, Dirk & Bogaert, Matthias, 2016. "Social media optimization: Identifying an optimal strategy for increasing network size on Facebook," Omega, Elsevier, vol. 59(PA), pages 15-25.

    Cited by:

    1. Ta-Chung Chu & Miroslav Kysely, 2021. "Ranking objectives of advertisements on Facebook by a fuzzy TOPSIS method," Electronic Commerce Research, Springer, vol. 21(4), pages 881-916, December.
    2. Julian Inchauspe, 2021. "Modelling Facebook and Outlook event attendance decisions: coordination traps and herding," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(4), pages 797-815, October.
    3. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    4. Wang, Xu & Baesens, Bart & Zhu, Zhen, 2019. "On the optimal marketing aggressiveness level of C2C sellers in social media: Evidence from china," Omega, Elsevier, vol. 85(C), pages 83-93.
    5. Ionela-Roxana GLAVAN & Andreea MIRICA & Bogdan Narcis FIRTESCU, 2016. "The Use of Social Media for Communication In Official Statistics at European Level," Romanian Statistical Review, Romanian Statistical Review, vol. 64(4), pages 37-48, December.
    6. 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. Dries F. Benoit & Stefan Van Aelst & Dirk Van den Poel, 2016. "Outlier‐Robust Bayesian Multinomial Choice Modeling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1445-1466, November.

    Cited by:

    1. Changbiao Liu & Yuling Li, 2023. "Estimation of Rank-Ordered Regret Minimization Models," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1611-1630, December.
    2. Nail Kashaev, 2022. "Identification and Estimation of Multinomial Choice Models with Latent Special Covariates," University of Western Ontario, Departmental Research Report Series 20224, University of Western Ontario, Department of Economics.

  7. 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.

    Cited by:

    1. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    2. Shakeel ul Rehman & Rafia Gulzar & Wajeeha Aslam, 2022. "Developing the Integrated Marketing Communication (IMC) through Social Media (SM): The Modern Marketing Communication Approach," SAGE Open, , vol. 12(2), pages 21582440221, May.
    3. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    4. 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.
    5. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. He Jiang, 2022. "A novel robust structural quadratic forecasting model and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1156-1180, September.
    7. Li, Yongli & Luo, Peng & Fan, Zhi-ping & Chen, Kun & Liu, Jiaguo, 2017. "A utility-based link prediction method in social networks," European Journal of Operational Research, Elsevier, vol. 260(2), pages 693-705.
    8. Liu, Zhenyuan & Han, Shuihua & Li, Chao & Gupta, Shivam & Sivarajah, Uthayasankar, 2022. "Leveraging customer engagement to improve the operational efficiency of social commerce start-ups," Journal of Business Research, Elsevier, vol. 140(C), pages 572-582.
    9. 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".
    10. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    11. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
    12. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    13. Nada Abdulla Al-Kubaisi, 2023. "The Black Box of Implementing Strategic Decisions," International Journal of Customer Relationship Marketing and Management (IJCRMM), IGI Global, vol. 14(1), pages 1-21, January.
    14. Xi Chen & Ralf van der Lans & Michael Trusov, 2021. "Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks," Management Science, INFORMS, vol. 67(12), pages 7575-7598, December.
    15. Sanaz Farhangi & Habib Alipour, 2021. "Social Media as a Catalyst for the Enhancement of Destination Image: Evidence from a Mediterranean Destination with Political Conflict," Sustainability, MDPI, vol. 13(13), pages 1-26, June.
    16. 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.
    17. Luzon, Yossi & Pinchover, Rotem & Khmelnitsky, Eugene, 2022. "Dynamic budget allocation for social media advertising campaigns: optimization and learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 223-234.

  8. Benjamin Verhelst & Dirk Van den Poel, 2014. "Deep habits in consumption: a spatial panel analysis using scanner data," Empirical Economics, Springer, vol. 47(3), pages 959-976, November.
    See citations under working paper version above.
  9. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    See citations under working paper version above.
  10. Dries F. Benoit & Dirk Van den Poel, 2012. "Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1174-1188, November.

    Cited by:

    1. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.
    2. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
    3. Florios, Kostas, 2018. "A hyperplanes intersection simulated annealing algorithm for maximum score estimation," Econometrics and Statistics, Elsevier, vol. 8(C), pages 37-55.
    4. Yves S. Schüler, 2014. "Asymmetric Effects of Uncertainty over the Business Cycle: A Quantile Structural Vector Autoregressive Approach," Working Paper Series of the Department of Economics, University of Konstanz 2014-02, Department of Economics, University of Konstanz.
    5. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).
    6. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Working Papers hal-01548710, HAL.
    7. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    8. Oh, Man-Suk & Park, Eun Sug & So, Beong-Soo, 2016. "Bayesian variable selection in binary quantile regression," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 177-181.
    9. Magzhanov, Timur & Sagradyan, Anna, 2023. "Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 89-108.
    10. Victor Muthama Musau & Carlo Gaetan & Paolo Girardi, 2022. "Clustering of bivariate satellite time series: A quantile approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    11. Yuzhi Cai, 2018. "A novel statistical approach to marketing campaigns," Working Papers 2018-21, Swansea University, School of Management.
    12. B. Dima & Ş. M. Dima, 2016. "Income Distribution and Social Tolerance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(1), pages 439-466, August.
    13. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    14. Sriram, Karthik, 2015. "A sandwich likelihood correction for Bayesian quantile regression based on the misspecified asymmetric Laplace density," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 18-26.
    15. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
    16. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    17. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    18. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
    19. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    20. Peter Congdon, 2017. "Quantile regression for overdispersed count data: a hierarchical method," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
    21. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.
    22. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
    23. Yukiko Omata & Hajime Katayama & Toshi. H. Arimura, 2017. "Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(3), pages 581-608, July.
    24. Karthik Sriram & R. V. Ramamoorthi & Pulak Ghosh, 2016. "On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 87-104, February.
    25. Bouoiyour, Jamal & Selmi, Refk & Miftah, Amal, 2015. "“Every cloud has a silver lining”; to what extent does the Arab Spring accelerate the integration among Arab monarchies?," MPRA Paper 70942, University Library of Munich, Germany.

  11. Vera Miguéis & Dirk Poel & Ana Camanho & João Falcão e Cunha, 2012. "Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 337-353, December.
    See citations under working paper version above.
  12. Verhaert, Griet Alice & Van den Poel, Dirk, 2011. "Improving Campaign Success Rate by Tailoring Donation Requests along the Donor Lifecycle," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 51-63.
    See citations under working paper version above.
  13. Verhaert, Griet A. & Van den Poel, Dirk, 2011. "Empathy as added value in predicting donation behavior," Journal of Business Research, Elsevier, vol. 64(12), pages 1288-1295.
    See citations under working paper version above.
  14. Maarten Dossche & Freddy Heylen & Dirk Van den Poel, 2010. "The Kinked Demand Curve and Price Rigidity: Evidence from Scanner Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 112(4), pages 723-752, December.
    See citations under working paper version above.
  15. Philippe Baecke & Dirk Van Den Poel, 2010. "Improving Purchasing Behavior Predictions By Data Augmentation With Situational Variables," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 853-872.
    See citations under working paper version above.
  16. 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.
  17. Lariviere, Bart & Van den Poel, Dirk, 2007. "Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments," European Journal of Operational Research, Elsevier, vol. 183(1), pages 345-369, November.
    See citations under working paper version above.
  18. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.

    Cited by:

    1. Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
    2. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    3. Takanobu Nakahara & Katsutoshi Yada, 2012. "Analyzing consumers’ shopping behavior using RFID data and pattern mining," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 355-365, December.
    4. 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.
    5. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    6. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    7. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    8. Özden Gür Ali & Yalçın Akçay & Serdar Sayman & Emrah Yılmaz & M. Hamdi Özçelik, 2017. "Cross-Selling Investment Products with a Win-Win Perspective in Portfolio Optimization," Operations Research, INFORMS, vol. 65(1), pages 55-74, February.
    9. Samy Mansouri, 2021. "Business cycles influences upon customer cross-buying behavior in the case of financial services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(3), pages 181-201, September.
    10. 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.
    11. 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.
    12. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    13. Özden Gür Ali & Yalçın Akçay & Serdar Sayman & Emrah Y?lmaz & M. Hamdi Özçelik, 2017. "Cross-Selling Investment Products with a Win-Win Perspective in Portfolio Optimization," Operations Research, INFORMS, vol. 65(1), pages 55-74, February.
    14. Creed, Bernard & Ning Shen, Kathy & Ashill, Nick & Wu, Tianshi, 2021. "Retail shopping at airports: Making travellers buy again," Journal of Business Research, Elsevier, vol. 137(C), pages 293-307.
    15. Paas, Leonard J. & Bijmolt, Tammo H.A. & Vermunt, Jeroen K., 2007. "Acquisition patterns of financial products: A longitudinal investigation," Journal of Economic Psychology, Elsevier, vol. 28(2), pages 229-241, April.
    16. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    17. 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.
    18. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
    19. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    20. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    21. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.
    22. Oppewal, Harmen & Paas, Leonard J. & Crouch, Geoffrey I. & Huybers, Twan, 2010. "Segmenting consumers based on how they spend a tax rebate: An analysis of the Australian stimulus payment," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 510-519, August.
    23. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
    24. Leonard Paas & Tammo Bijmolt & Jeroen Vermunt, 2015. "Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 249-262, August.

  19. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    See citations under working paper version above.
  20. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    See citations under working paper version above.
  21. 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.
    See citations under working paper version above.
  22. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    See citations under working paper version above.
  23. B Baesens & T Van Gestel & M Stepanova & D Van den Poel & J Vanthienen, 2005. "Neural network survival analysis for personal loan data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1089-1098, September.
    See citations under working paper version above.
  24. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
    See citations under working paper version above.
  25. 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.
    See citations under working paper version above.
  26. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    See citations under working paper version above.
  27. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.

    Cited by:

    1. Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    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. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    4. 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.
    5. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
    6. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    7. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
    8. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    9. 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.
    10. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    11. 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.
    12. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.
    13. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
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    18. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    19. 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.
    20. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.
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    30. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
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    2. Smith, Donnavieve N. & Sivakumar, K., 2004. "Flow and Internet shopping behavior: A conceptual model and research propositions," Journal of Business Research, Elsevier, vol. 57(10), pages 1199-1208, October.
    3. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
    4. Heiman, Amir & Just, David R. & McWilliams, Bruce P. & Zilberman, David, 2015. "A prospect theory approach to assessing changes in parameters of insurance contracts with an application to money-back guarantees," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 54(C), pages 105-117.
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