The recent growth of market and technology advancement led to the increse of amount of competitors providing online services to its users. In those circumstances acquiring a new user is multiple times more expensive than keeping the existing ones. That makes user retention one of the key metrics of success for such an online service (e-shops, bank services, insurrance companies etc.). Successful prediction of churn of a specific user provides an opportunity to change his decision by for instance giving him a special offer. This kind of prevention and identification of churn reasons create huge motivation to explore this area. In our work we focus on identification of the set of features to create a user model for further use for the churn prediction. In first stage of our work we plan to build a user model in selected domain and explore the possibilities of automatic feature extraction from the data. As a next step we want to select classifiers and build a structure of a learning ensemble. Finally, we are planning to test our model with a nontrivial dataset from the selected domain.