PREDICTING CUSTOMER CHURN IN A DISTRIBUTION COMPANY OF NATURAL GAS USING DATA MINING

Abstract

Customer churn is a relevant problem faced by service companies and that can generate significant economic losses. Identifying the elements that lead a customer to stop consuming a service is a complex task. However, through their behavior, it is possible to estimate a churn probability associated with each one of them. This research applies data mining to predict customer churn in a natural gas distribution company, using two machine learning techniques: neural networks and support vector machine. The results show that by applying these techniques it is possible to identify customers with the highest probability of churn to take retention actions timely and focused, minimizing the costs associated with the error in the identification of these customers.

Keywords: Customer churn, Data mining, Machine learning, Natural gas distribution.

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