A data-driven Agent-based model and framework for Churn prediction in Telecommunication Industry

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Customer churn presents a significant challenge for the telecommunications industry, necessitating effective strategies for prediction and prevention. While prior research has explored diverse methodologies, including Agent-based Modeling (ABM), limitations persist. Existing approaches often rely heavily on theoretical constructs, resulting in oversimplified models and constrained data utilization. This study addresses a critical research gap: the absence of a comprehensive framework integrating empirical data, agent-based modeling, and machine learning techniques for churn prediction in telecommunication markets. By bridging the gap between theoretical abstraction and empirical reality, proposed framework enables more proactive churn management strategies. Additionally, it facilitates the simulation of diverse market scenarios, empowering stakeholders to optimize key metrics such as revenue and market share. Through the implementation of the proposed framework within a specific telecom market scenario involving two competing entities, this study demonstrates its efficacy in achieving desired market share objectives. This research contributes to advancing the understanding and management of customer churn in the telecommunications industry.

Language:
English
Published:
Journal of Modern Research in Decision Making, Volume:9 Issue: 2, 2024
Pages:
164 to 190
https://www.magiran.com/p2812575