Título Churn and Net Promoter Score forecasting for business decision-making through a new stepwise regression methodology
Autores Velez, D. , Ayuso, A. , PERALES GONZÁLEZ, CARLOS, Tinguaro Rodriguez, J.
Publicación externa No
Medio Knowl Based Syst
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 8.038
Impacto SJR 1.587
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082480132&doi=10.1016%2fj.knosys.2020.105762&partnerID=40&md5=c37e849fb057f6c8df7f470564ff59a7
Fecha de publicacion 21/05/2020
ISI 000527301700010
Scopus Id 2-s2.0-85082480132
DOI 10.1016/j.knosys.2020.105762
Abstract Companies typically have to make relevant decisions regarding their\n clients\' fidelity and retention on the basis of analytical models\n developed to predict both their churn probability and Net Promoter Score\n (NPS). Although the predictive capability of these models is important,\n interpretability is a crucial factor to look for as well, because the\n decisions to be made from their results have to be properly justified.\n In this paper, a novel methodology to develop analytical models\n balancing predictive performance and interpretability is proposed, with\n the aim of enabling a better decision-making. It proceeds by fitting\n logistic regression models through a modified stepwise variable\n selection procedure, which automatically selects input variables while\n keeping their business logic, previously validated by an expert. In\n synergy with this procedure, a new method for transforming independent\n variables in order to better deal with ordinal targets and avoiding some\n logistic regression issues with outliers and missing data is also\n proposed. The combination of these two proposals with some competitive\n machine-learning methods earned the leading position in the NPS\n forecasting task of an international university talent challenge posed\n by a well-known global bank. The application of the proposed methodology\n and the results it obtained at this challenge are described as a\n case-study. (C) 2020 Elsevier B.V. All rights reserved.
Palabras clave Churn prediction; Net Promoter Score; Stepwise regression; WOE variables
Miembros de la Universidad Loyola

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