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Title: Approach to Prediction of Mobile Operators Subscribers Churn
Other Titles: Предсказание оттока абонентов от операторов мобильной связи
Authors: Baria, A.
Globa, L.
Moroz, A.
Баря, А. Д.
Глоба, Л. С.
Мороз, А. М.
Keywords: материалы конференций
telecom operator
machine learning
random forest
prediction mathematical methods
Issue Date: 2019
Publisher: БГУИР
Citation: Baria, A. Approach to Prediction of Mobile Operators Subscribers Churn / A. Baria, L. Globa, A. Moroz // Открытые семантические технологии проектирования интеллектуальных систем = Open Semantic Technologies for Intelligent Systems (OSTIS-2019) : материалы международной научно-технической конференции, Минск, 21 - 23 февраля 2019 г. / Белорусский государственный университет информатики и радиоэлектроники; редкол.: В. В. Голенков (гл. ред.) [и др.]. - Минск, 2019. - С. 155 - 160.
Abstract: This article presents an approach to the description of machine learning methods for predicting the outflow of telecom operator subscribers. Describes the parameters characterizing the interaction of the mobile operator with end users. The parameters that have the greatest influence on the client’s decision to refuse the services of a mobile operator have been determined. The originality of the approach lies in the use of such mathematical methods that allow you to determine the main set of parameters, due to which specific subscribers are prone to changing the mobile operator. The proposed approach allows you to organize a system in which it is possible to determine the main parameters characterizing the tendency of customers to outflow and acting on them using various methods to try to increase subscriber loyalty. A comparative analysis of the results obtained using the analyzed logistic regression methods, Bootstrap aggregating and random forest showed that the spread of prediction errors does not exceed 6%. However, the advantage of the random forest method is the ability to determine the set of parameters that make the greatest contribution to making decisions by the subscriber to change the mobile operator. Therefore, for analyzing customer loyalty, a random forest method can be recommend, which showed on the test sample an improvement in the accuracy of the predictions in the sample to 6-7%.
Appears in Collections:OSTIS-2019

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