A Framework for the Adaptive Churn Management in Telecommunications

Mohammad Syibli; Rahmat Gernowo; Bayu Surarso; Aldi Setiawan; Nur Andi Setiabudi

doi:10.48084/etasr.18530

Authors

Mohammad Syibli Diponegoro University, Semarang, Indonesia | Wholesale Services Division, Telkom Indonesia, Jakarta, Indonesia
Rahmat Gernowo Diponegoro University, Semarang, Indonesia
Bayu Surarso Diponegoro University, Semarang, Indonesia
Aldi Setiawan Digital Product Division, Telkom Indonesia, Jakarta, Indonesia
Nur Andi Setiabudi Digital Product Division, Telkom Indonesia, Jakarta, Indonesia

Volume: 16 | Issue: 3 | Pages: 35848-35861 | June 2026 | https://doi.org/10.48084/etasr.18530

Received: 4 March 2026 | Revised: 30 March 2026 and 12 April 2026 | Accepted: 17 April 2026 | Online: 6 June 2026
Corresponding author: Mohammad Syibli

Abstract

This paper proposes an adaptive churn management paradigm that incorporates feedback-driven retraining, channel suggestion, churn prediction, and churn factor detection into a closed-loop architecture. The framework was assessed by comparing adaptive and non-adaptive setups across six monthly observation periods using actual operational data from a fixed broadband provider in Indonesia. The adaptive approach significantly improved imbalance-sensitive and operational metrics, increasing PR-AUC from 9.32% to 15.02%, recall from 39.44% to 50.70%, and F1-score from 9.57% to 11.01%, even though both configurations achieved comparable discrimination performance in terms of AUC-ROC (77.67% adaptive versus 77.05% non-adaptive). Adaptive learning improved decision-support alignment in the recommendation module, increasing Mean Reciprocal Rank (MRR) from 75.08% to 81.69% and Hit Rate@1 from 51.36% to 64.45%. With churn rate reductions of up to 0.56% during observation periods, these analytical advancements resulted in more reliable business outcomes. The findings demonstrated that churn behavior exhibits temporal drift and that, in dynamic customer contexts, predictive robustness, explanatory relevance, and operational efficacy depend on adaptive system design.

Keywords:

adaptive churn management, customer churn, fixed broadband, machine learning, telecommunications industry

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