The application of churn prevention represents an important step for mobile communication
companies aiming at increasing customer loyalty. In a machine learning perspective,
Customer Value Management departments require automated methods and processes to
create marketing campaigns able to identify the most appropriate churn prevention approach.
Moving towards a big data-driven environment, a deeper understanding of data
provided by churn processes and client operations is needed. In this context, a procedure
aiming at reducing the number of churners by planning a customized marketing campaign
is deployed through a data-driven approach. Decision Tree methodology is applied to drow
up a list of clients with churn propensity: in this way, customer analysis is detailed, as well
as the development of a marketing campaign, integrating the individual churn model with
viral churn perspective. The first step of the proposed procedure requires the evaluation of
churn probability for each customer, based on the influence of his social links. Then, the
customer profiling is performed considering (a) individual variables, (b) variables describing
customer-company interactions, (c) external variables. The main contribution of this work
is the development of a versatile procedure for viral churn prevention, applying Decision
Tree techniques in the telecommunication sector, and integrating a direct campaign from
the Customer Value Management marketing department to each customer with significant
churn risk. A case study of a mobile communication company is also presented to explain
the proposed procedure, as well as to analyze its real performance and results.