in the global telecommunication industry. Hence, a dominant approach for subscriber
management and retention is churn control, since it is cheaper to retain an existing
subscriber than acquiring a new one. Predictive modeling employs the use of data mining
techniques to identify patterns and provide a result that a group of subscribers are likely to
churn in the near future. However, the effectiveness of subscriber retention strategy in an
organization can be further boosted if the reason for churn and the timing of churn can also
be predicted.
In this paper, we propose a data mining process framework that can be used to predict
churn, determine when a subscriber is likely to churn, provides the reason why a subscriber
may churn, and recommend appropriate intervention strategy for customer retention using
a combination of statistical and machine learning techniques. This experiment is carried
out using data from a major telecom operator in Nigeria
