UM MODELO DE RISCO DE CANCELAMENTO DE CLIENTES DE TELEFONIA FIXA - A APLICAÇÃO DA REGRESSÃO LOGÍSTICA PARA RETENÇÃO DE CLIENTES / A MODEL TO MEASURE CUSTOMERS CANCELLATION RISK IN TELECOMMUNICATIONS - THE APPLICATION OF LOGISTIC REGRESSION FOR CUSTOMER RETENTION

AUTOR(ES)
DATA DE PUBLICAÇÃO

2009

RESUMO

The current organizational environment is marked by a high competitiveness, high turbulence and rapid and discontinuous changes in companies¿ macro environment. Scenario requires focus on customers and strategies geared towards maintaining a fruitful relationship for both parties (customer and company), with long-term vision. This dynamics of this market is the main focus of this research, which focuses on customer retention as a competitive strategy to increase value for companies. The objective then is to develop a tool that assists in defining the profile of customers most likely to break the relationship (churn), allowing the company forward to this event, becoming more pro-active, and thus being more efficient in their business processes. In this regard was a comprehensive literature review on consumer behavior, relationship marketing, customer retention, market segmentation and churn in telecommunications. After the theoretical basis and guidance offered by works consulted, the practice was performed by a statistical tool based on data available from a telecommunications company, using binary logistic regression model for the propensity of cancellation. The result achieved an overall success rate of 79,2%, composed of 13 variables that may facilitate identification of the profile of customers most likely to cancellation, further indicating that only 8% of the database have more than 80% chance to be canceled. These results demonstrate the possibility and importance of the identification of customers likely to churn, as well as those that the company must attract and retain to be able to develop efficient and profitable practice.

ASSUNTO(S)

regressao logistica segmentacao marketing of relationship customer retention retencao de clientes segmentation logistic regression marketing de relacionamento

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