IDEAS home Printed from https://ideas.repec.org/a/tuz/journl/v20y2022i2p53-64.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://ef.untz.ba/wp-content/uploads/2023/05/5_ER_DOI_pp.53_64.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    4. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    5. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    6. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    7. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    8. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
    9. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    10. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    11. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    12. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    13. Yeung, Alice H.W. & Lo, Victor H.Y. & Yeung, Andy C.L. & Cheng, T.C. Edwin, 2008. "Specific customer knowledge and operational performance in apparel manufacturing," International Journal of Production Economics, Elsevier, vol. 114(2), pages 520-533, August.
    14. Bogomolova, Svetlana, 2016. "Determinants of ex-customer winback in financial services," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 1-6.
    15. E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
    16. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    17. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    18. Behrooz Hashemian & Emanuele Massaro & Iva Bojic & Juan Murillo Arias & Stanislav Sobolevsky & Carlo Ratti, 2017. "Socioeconomic characterization of regions through the lens of individual financial transactions," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-20, November.
    19. H-Y Tsao & L Pitt & C Campbell, 2010. "Analysing consumer segments to budget for loyalty and promotion programmes and maximize market share," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1523-1529, October.
    20. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2017. "Modeling reward expiry for loyalty programs in a competitive market," International Journal of Production Economics, Elsevier, vol. 193(C), pages 352-364.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tuz:journl:v:20:y:2022:i:2:p:53-64. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Senad Celikovic (email available below). General contact details of provider: https://edirc.repec.org/data/efutzba.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.