Análise dos indicadores de qualidade versus taxa de abandono utilizando método de regressão múltipla para serviço de banda larga

• Ano de defesa: 2008
• Autor(a) principal: Fernandes Neto, André Pedro
• Orientador(a): Costa, José Alfredo Ferreira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção
• Departamento: Estratégia; Qualidade; Gestão Ambiental; Gestão da Produção e Operações
• País: BR
• Palavras-chave em Português:  Medição de Desempenho; Qualidade de Serviço; Descoberta de Conhecimento em Bases de Dados; Taxa de Abandono (Churn); Análise de Regressão Multivariada
• Palavras-chave em Inglês: Performance measurement; Quality of Services; Knowledge Discovery in Databases; Churn; Models of Multivariate Regression; Decision Support Systems
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA DO PRODUTO
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/14878

Telecommunication is one of the most dynamic and strategic areas in the world. Many technological innovations has modified the way information is exchanged. Information and knowledge are now shared in networks. Broadband Internet is the new way of sharing contents and information. This dissertation deals with performance indicators related to maintenance services of telecommunications networks and uses models of multivariate regression to estimate churn, which is the loss of customers to other companies. In a competitive environment, telecommunications companies have devised strategies to minimize the loss of customers. Loosing customers presents a higher cost than obtaining new ones. Corporations have plenty of data stored in a diversity of databases. Usually the data are not explored properly. This work uses the Knowledge Discovery in Databases (KDD) to establish rules and new models to explain how churn, as a dependent variable, are related to a diversity of service indicators, such as time to deploy the service (in hours), time to repair (in hours), and so on. Extraction of meaningful knowledge is, in many cases, a challenge. Models were tested and statistically analyzed. The work also shows results that allows the analysis and identification of which quality services indicators influence the churn. Actions are also proposed to solve, at least in part, this problem