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Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

Author

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  • Mirjana Pejić Bach

    (Faculty of Economics & Business, University of Zagreb, 10000 Zagreb, Croatia)

  • Jasmina Pivar

    (Faculty of Economics & Business, University of Zagreb, 10000 Zagreb, Croatia)

  • Božidar Jaković

    (Faculty of Economics & Business, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for different market segments using cluster analysis and decision trees. In the first stage, a case study churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing, monetary value, and churn. In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio. In the third stage, the chi-squared automatic interaction detector (CHAID) decision tree algorithm is used to develop classification models to identify churn determinants at the clusters with the highest churn level. The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable structure. The proposed approach is continuous since the results of market segmentation and rules for churn prediction can be fed back to the customer database to improve the efficacy of churn management.

Suggested Citation

  • Mirjana Pejić Bach & Jasmina Pivar & Božidar Jaković, 2021. "Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees," JRFM, MDPI, vol. 14(11), pages 1-25, November.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:544-:d:676538
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    References listed on IDEAS

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    6. Alae Chouiekh & El Hassane Ibn El Haj, 2020. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(1), pages 1-16, January.
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    Cited by:

    1. Mydyti Hyrmet & Kadriu Arbana & Pejic Bach Mirjana, 2023. "Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development," Organizacija, Sciendo, vol. 56(2), pages 138-154, May.
    2. Ken Nishimatsu & Akiya Inoue, 2023. "User Intent-Based Segmentation Analysis for Internet Access Services," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 14(1), pages 1-21, January.
    3. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    4. Frank Oechsle, 2023. "Increasing the robustness of uplift modeling using additional splits and diversified leaf select," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 738-746, December.
    5. Londhe Sanket Tanaji & Palwe Sushila, 2022. "Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach," Business Systems Research, Sciendo, vol. 13(1), pages 35-45, June.

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