PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS

Edin Osmanbegovic (), Anel Dzinic () and Mirza Suljic ()
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Edin Osmanbegovic: University of Tuzla, Faculty of Economics, Bosnia and Herzegovina
Anel Dzinic: CaDa Solucije doo, Bosnia and Herzegovina
Mirza Suljic: University of Tuzla, Bosnia and Herzegovina

Economic Review: Journal of Economics and Business, 2022, vol. 20, issue 2, 53-64

Abstract: Machine learning, or as it is also called automated learning, is a special subfield of scientific information technologies. The name "machine learning" refers to the automated detection of meaningful patterns in large data sets. Machine learning is gaining importance in many different areas of the economy. One of those areas is the prediction and prevention of consumer churn. There are two basic types of consumer churn, complete churn and partial churn. Machine learning is used to determine the most significant characteristics that play a role in the churn/retention of consumers, and with the help of machine learning it is possible to establish the probability of churn for each individual consumer. Some of the most commonly used machine learning algorithms for this issue are Logistic Regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, Decision Tree, and Random Forest.

Keywords: machine learning; customer churn; customer retention (search for similar items in EconPapers)
JEL-codes: L86 (search for similar items in EconPapers)
Date: 2022
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