Machine learning methods applicable in customer lifecycle management

In the current business environment, globalization and digitalization are the main distinguishing features of the world economy. Machine learning, as a field of artificial intelligence, has gained wide popularity as a technology used in the process of extracting knowledge about customers at different stages of their life cycle. The purpose of this research paper is to explore the types of machine learning and their applications in the study of dependencies related to changing the behavior of customers in the telecommunication services market, as well as the justification of an appropriate type of machine learning method applicable to these studies. The focus of the study is aimed at the application of supervised machine learning methods, at different stages of the customer lifecycle, to solve problems that can be categorized as classification. The study shows the advantage of boosting and bagging ensemble classifiers in terms of correct classification of specimens and model accuracy. Recommendations for future research are also defined.