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Prediction Of Telecom Services Consumers Churn By Using Machine Learning Algorithms

Author

Listed:
  • 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)

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.

Suggested Citation

  • Edin Osmanbegovic & Anel Dzinic & Mirza Suljic, 2022. "Prediction Of Telecom Services Consumers Churn By Using Machine Learning Algorithms," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 20(2), pages 53-64, November.
    • Handle: RePEc:tuz:journl:v:20:y:2022:i:2:p:53-64
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    machine learning; customer churn; customer retention;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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