Predicting direct marketing response in banking: comparison of class imbalance methods
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DOI: 10.1007/s11628-016-0332-3
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- Bigler, T. & Kammermann, M. & Baumann, P., 2023. "A matheuristic for a customer assignment problem in direct marketing," European Journal of Operational Research, Elsevier, vol. 304(2), pages 689-708.
- Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
- Jeff Allen & Santiago Carbo Valverde & Sujit Chakravorti & Francisco Rodriguez-Fernandez & Oya Pinar Ardic, 2022. "Assessing Incentives to Increase Digital Payment Acceptance and Usage," World Bank Publications - Books, The World Bank Group, number 37487, April.
- 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.
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- Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
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Keywords
Direct marketing; Response modelling; Customer targeting; Random forests; Class imbalance;All these keywords.
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