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   In: Annals of Operations Research.
   RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-025-06577-w.

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2. Investigating the impact of undersampling and bagging: an empirical investigation for customer attrition modeling. (2025). Caigny, Arno ; Coussement, Kristof ; Meire, Matthijs ; Hoornaert, Steven.
   In: Annals of Operations Research.
   RePEc:spr:annopr:v:346:y:2025:i:3:d:10.1007_s10479-025-06516-9.

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3. Life event-based marketing using AI. (2025). Meire, Matthijs ; Hoornaert, Steven ; Coussement, Kristof ; de Caigny, Arno.
   In: Journal of Business Research.
   RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001729.

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4. Profit-driven pre-processing in B2B customer churn modeling using fairness techniques. (2025). Bogaert, Matthias ; Janssens, Bram ; Rahman, Shimanto.
   In: Journal of Business Research.
   RePEc:eee:jbrese:v:189:y:2025:i:c:s0148296324006635.

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   In: International Journal of System Assurance Engineering and Management.
   RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01759-2.

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   In: Annals of Operations Research.
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7. Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction. (2024). Liu, Zhenkun ; Niu, Xinsong ; Jiang, Ping ; Zhang, Lifang ; de Bock, Koen W ; Wang, Jianzhou.
   In: Technological Forecasting and Social Change.
   RePEc:eee:tefoso:v:198:y:2024:i:c:s0040162523006303.

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9. Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach. (2024). Guo, Yihan ; Feng, YI ; Marra, Marianna ; Ignatius, Joshua ; Wang, Dujuan ; Cheng, T. C. E., ; Yin, Yunqiang.
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10. Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. (2024). de Bock, Koen W ; Verbeke, Wouter ; Delen, Dursun ; Choi, Tsan-Ming ; Martens, David ; Lessmann, Stefan ; Vairetti, Carla ; Maldonado, Sebastian ; Baesens, Bart ; Sowiski, Roman ; de Caigny, Arno ; Boute, Robert N ; Weber, Richard ; Kraus, Mathias ; Oskarsdottir, Maria ; Coussement, Kristof.
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    In: Post-Print.
    RePEc:hal:journl:hal-04274684.

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12. Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda. (2023). Boute, Robert N ; Weber, Richard ; Kraus, Mathias ; Oskarsdottir, Maria ; Coussement, Kristof ; de Bock, Koen W ; Verbeke, Wouter ; Delen, Dursun ; Choi, Tsan-Ming ; Martens, David ; Lessmann, Stefan ; Vairetti, Carla ; Maldonado, Sebastian ; Baesens, Bart ; Slowiski, Roman ; de Caigny, Arno.
    In: Post-Print.
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13. Deep Churn Prediction Method for Telecommunication Industry. (2023). Gaber, Tarek ; Saha, Lewlisa ; Tripathy, Hrudaya Kumar ; El-Gohary, Hatem ; El-Kenawy, El-Sayed M.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2023:i:5:p:4543-:d:1086814.

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14. Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors. (2023). Zhu, Hui.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2023:i:18:p:13592-:d:1237713.

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15. Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art. (2023). Bogaert, Matthias ; Delaere, Lex.
    In: Mathematics.
    RePEc:gam:jmathe:v:11:y:2023:i:5:p:1137-:d:1079547.

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16. A novel deep learning model based on convolutional neural networks for employee churn prediction. (2022). Ozcan, Tuncay ; Ozmen, Ebru Pekel.
    In: Journal of Forecasting.
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17. A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection. (2022). Abedin, Mohammad Zoynul ; Rahman, Saifur M ; Sana, Joydeb Kumar.
    In: PLOS ONE.
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18. Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques. (2022). Patil, Vivek H ; Raju, Sudhakar ; Valluri, Chandrasekhar.
    In: Journal of Marketing Analytics.
    RePEc:pal:jmarka:v:10:y:2022:i:3:d:10.1057_s41270-021-00135-6.

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19. Bias in Algorithms: On the trade-off between accuracy and fairness. (2021). Janssen, Patrick ; Sadowski, Bert M.
    In: 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world.
    RePEc:zbw:itsb21:238032.

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20. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. (2021). Nayak, Soumyaranjan ; Barsocchi, Paolo ; Saha, Lewlisa ; Tripathy, Hrudaya Kumar ; Bhoi, Akash Kumar.
    In: Sustainability.
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21. Overcapacity Risk of China’s Coal Power Industry: A Comprehensive Assessment and Driving Factors. (2021). Wang, Yadong ; Xue, Xun.
    In: Sustainability.
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22. An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach. (2021). Almuqren, Latifah ; Alrayes, Fatma S ; Cristea, Alexandra I.
    In: Future Internet.
    RePEc:gam:jftint:v:13:y:2021:i:7:p:175-:d:588849.

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23. Customer comeback: Empirical insights into the drivers and value of returning customers. (2021). Meire, Matthijs.
    In: Journal of Business Research.
    RePEc:eee:jbrese:v:127:y:2021:i:c:p:193-205.

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24. How training on multiple time slices improves performance in churn prediction. (2021). Thonemann, Ulrich W ; Gattermann-Itschert, Theresa.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:295:y:2021:i:2:p:664-674.

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25. Incorporating textual information in customer churn prediction models based on a convolutional neural network. (2020). Lessmann, Stefan ; Coussement, Kristof ; de Bock, Koen W ; de Caigny, Arno.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:36:y:2020:i:4:p:1563-1578.

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26. Incorporating textual information in customer churn prediction models based on a convolutional neural network. (2019). de Caigny, Arno ; Lessmann, Stefan ; Coussement, Kristof ; de Bock, Koen W.
    In: Post-Print.
    RePEc:hal:journl:hal-02275958.

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27. A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting. (2019). Chen, Shuixia ; Wang, Jian-Qiang ; Zhang, Hong-Yu.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:146:y:2019:i:c:p:41-54.

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28. Customer churn prediction in telecommunication industry using data certainty. (2019). Anwar, Sajid ; Loo, Jonathan ; Al-Obeidat, Feras ; Shah, Babar ; Adnan, Awais ; Amin, Adnan.
    In: Journal of Business Research.
    RePEc:eee:jbrese:v:94:y:2019:i:c:p:290-301.

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29. Targeting customers for profit: An ensemble learning framework to support marketing decision making. (2018). de Bock, Koen W ; Haupt, Johannes ; Lessmann, Stefan ; Coussement, Kristof.
    In: IRTG 1792 Discussion Papers.
    RePEc:zbw:irtgdp:2018012.

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30. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. (2018). Coussement, Kristof ; de Bock, Koen W ; de Caigny, Arno.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:269:y:2018:i:2:p:760-772.

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