Monitoring and Decision Making on the Basis of the Churn Predictive Model

Student: Denisov Andrey

Supervisor: Zinaida Avdeeva

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2016

The rapid growth of the market in different sectors of modern business spheres is leading to bigger customer bases for service and product providers. The increasing number of competitors, innovative business models and better services/products are increasing the cost of customer acquisition and churn. In these circumstances, companies have realized the value of the retention of existing customers. So as providers of services are forced to deal with client`s outflow more and more efforts are put for predicting and prevention of churn. This paper aims to present popular data mining techniques for the churn prediction, compare several data mining tools, which can be used for solving churn problems, and describe how churn can be managed with the help of data mining, segmentation and campaign management tools.

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