Customer churn prediction based on eXtreme gradient boosting classifier

Nowadays, Machine Learning (ML) is a hot topic in many different fields. Marketing is one of the best sectors in which ML is giving more advantages. In this field, customer retention models (churn models) aim to identify early churn signals and recognize customers with an increased likelihood to leave voluntarily. Churn problems fit in the classification framework, and several ML approaches have been tested. In this work, we apply an innovative classification approach, eXtreme Gradient Boosting (XGBoost). XGBoost demostrated to be a powerful technique for churn modelling purpose applied to the retail sector.