Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companiesβ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models
