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Practical Methods for Predicting Customer Retention

Author

Listed:
  • Cherkashin, Alexander
  • Sakhadzhi, Vladislav
  • Guliev, Ruslan
  • Bolshunova, Elena

Abstract

This study examines methods for analyzing and forecasting the retention of active subscribers in the telecommunications industry using various criteria for subscriber activity. The results demonstrate that the retention dynamics of an active subscriber base can be effectively modeled using a decreasing power function. This allows for medium-term forecasting based on initial subscriber activity data. However, it is important to note the potential limitations in the effectiveness of the proposed approach for long-term forecasting, associated with changes in subscriber churn dynamics over time.

Suggested Citation

  • Cherkashin, Alexander & Sakhadzhi, Vladislav & Guliev, Ruslan & Bolshunova, Elena, 2024. "Practical Methods for Predicting Customer Retention," MPRA Paper 122400, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:122400
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    File URL: https://mpra.ub.uni-muenchen.de/122752/8/MPRA_paper_122752.pdf
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    References listed on IDEAS

    as
    1. Kumar, V., 2008. "Customer Lifetime Value — The Path to Profitability," Foundations and Trends(R) in Marketing, now publishers, vol. 2(1), pages 1-96, August.
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    3. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    4. Hemlata Jain & Ajay Khunteta & Sumit Srivastava, 2021. "Telecom churn prediction and used techniques, datasets and performance measures: a review," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(4), pages 613-630, April.
    5. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    6. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    subscriber base; customer retention; customer churn; power law; power function; telecommunications; LTV; retention curve; survivorship curve;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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