Understanding the reasons behind churn can help businesses develop
effective retention strategies, improve customer satisfaction, and
sustain a competitive edge in a highly saturated market like the
telecommunication sector. This systematic literature review uncovers the
implementation of machine learning algorithms to predict customer churn
rates in the telecommunications industry. The review identifies key
predictive variables and methodologies that enhance churn prediction
accuracy by examining a wide range of studies. The findings highlight
the significant role of data integration, particularly the inclusion of
real-time and external data sources, in improving model performance.
Data quality and privacy issues are also discussed, emphasizing the need
for ongoing methodological improvements. The study concludes with
recommendations for prospective studies, including the adoption of
machine learning approaches like deep learning to refine predictive
capabilities further and support robust customer retention strategies in
the telecommunications sector.