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Master's Dissertation
DOI
Document
Author
Full name
Rodolfo Augusto da Silva Arruda
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2019
Supervisor
Committee
Louzada Neto, Francisco (President)
Dias, Teresa Cristina Martins
Ramos, Pedro Luiz
Suzuki, Adriano Kamimura
Title in Portuguese
Modelagem de propensão ao atrito no setor de telecomunicações
Keywords in Portuguese
Aprendizado de máquina
Atrito
Score de propensão
Abstract in Portuguese
A satisfação dos clientes é fundamental para a manutenção do relacionamento com a empresa. Quando eles precisam resolver algum problema, a empresa necessita proporcionar bom atendimento e ter capacidade de resolutividade. No entanto, o atendimento massificado, muitas vezes, impossibilita soluções sensíveis às necessidades dos clientes. A metodologia estatística pode ajudar a empresa na priorização de clientes com perfil a reclamar em um órgão de defesa ao consumidor (ODC), evitando assim uma situação de atrito. Neste projeto, foi realizada a modelagem do comportamento do cliente com relação à propensão ao atrito. Foram testadas as técnicas de Regressão Logística, Random Forest e Algoritmos Genéticos. Os resultados mostraram que os Algoritmos Genéticos são uma boa opção para tornar o modelo mais simples (parcimonioso), sem perda de performance, e que o Random Forest possibilitou ganho de performance, porém torna o modelo mais complexo, tanto do ponto de vista computacional quanto prático no que tange à implantação em sistemas de produção da empresa.
Title in English
Modeling Attrition Propensity in the Telecommunication Sector
Keywords in English
Attrition
Machine learning
Propensity score
Abstract in English
Customer satisfaction is key to maintaining the relationship with the company. When these need to solve some problem, the company needs to provide good service and have resolving capacity. However, the mass service often makes it impossible. The statistical methodology can help the company in the prioritization of clients with profile to complain in ODC, thus avoiding a situation of attrition. In this project was carried out the modeling of the behavior of the client in relation to the propensity to attrition. Logistic Regression, Random Forest and Genetic Algorithms were tested. The results showed that the Genetic Algorithms are a good option to make the model simpler (parsimonious) without loss of performance and that Random Forest allowed performance gain, but it makes the model more complex, both from the point of view computational and practical in relation to the implantation in production systems of the company.
 
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Publishing Date
2019-08-21
 
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