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

Mirjana Pejić Bach, Jasmina Pivar and Božidar Jaković
Additional contact information
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

JRFM, 2021, vol. 14, issue 11, 1-25

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.

Keywords: churn; telecommunications; clustering; k-means; market segmentation; prediction; decision trees; CHAID (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1911-8074/14/11/544/pdf (application/pdf)
https://www.mdpi.com/1911-8074/14/11/544/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:544-:d:676538

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().