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Customer Churn Analysis Based on Predictive Analytics Methods

Student: Elizaveta Bykova

Supervisor: Alexander Porshnev

Faculty: Faculty of Management

Educational Programme: Management (Bachelor)

Year of Graduation: 2020

Over the past decades the flow of information that companies generate has been increasing every year, forming big data. That is why data analysis is one of the most discussed topics in the area of analysis and management technologies not only among IT and enterprises, but also among psychologists and economists. The chosen topic is important for the strategic development of the company. This study discusses methods of analyzing customer churn using predictive analytical tools on a sample of big data sets to identify a pool of client churn factors and increase business value. The study will be conducted on the basis the analysis of practical cases on the study customer churn and the construction of a predictive model. This study will help to expand understanding of the use of predictive analytics methodologies for strategic management. During the in-depth analysis of the research results, valid factors that affect customer care will be identified and some recommendations to minimize their impact in the company will be proposed. The results can be used in the practice of making strategic decisions based on predictive analytics.

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