A Novel Model Structured on Predictive Churn Methods in a Banking Organization

Leonardo José Silveira, Plácido Rogério Pinheiro and Leopoldo Soares de Melo Junior
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Leonardo José Silveira: Professional Master’s in Business Administration, University of Fortaleza, Fortaleza 61599, CE, Brazil
Plácido Rogério Pinheiro: Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza 61599, CE, Brazil
Leopoldo Soares de Melo Junior: Banco do Nordeste do Brasil S/A, Fortaleza 60715, CE, Brazil

JRFM, 2021, vol. 14, issue 10, 1-24

Abstract: A constant in the business world is the frequent movement of customers joining or abandoning companies’ services and products. The customer is one of the company’s most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most efficient way to maintain the customer base, since the replacement of dropouts by new customers costs, on average, 40% more. Aiming to mitigate the churn (customer evasion) phenomenon, this study compared predictive models to discover the most efficient method to identify customers who tend to drop out in the context of a banking organization. A literature review of related works on the subject found the neural network, decision tree, random forest and logistic regression models were the most cited, and thus the models were chosen for this work. Quantitative analyses were carried out on a sample of 200,000 credit operations, with 497 explanatory variables. The statistical treatment of the data and the developments of predictive models of churn were performed using the Orange data mining software. The most expressive results were achieved using the random forest model, with an accuracy of 82%.

Keywords: churn; machine learning; predictive model; neural networks; decision tree; random forest; logistic regression (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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