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Telecom company tries to find out new ways for developing new personal-oriented services based on data analysis. Changes in telecom users behavior are the objects of investigation. If a user changes its service usage, the company should pay attention to the fact and minimize the negative outcomes (e.g. churn analysis). We propose a method, which allows identifying changes in user behavior based on analysis of Call Detail Records data. The main idea of the method is using clustering techniques to determine the clusters with typical user behavior. Since the clusters exist and all users are labeled in terms of cluster belonging, the new data about user behavior compare with a typical profile. Open data was used in use cases. The results show the outperform of the proposed method in comparison with benchmark model. Keywords—data analysis; CDR; telecommunication services; time series forecasting; MeanShift; KMeans; clustering

Clustering helps to determine the changes in telecom subscribers' behavior