Revitalizing telecoms customer loyalty in advanced analysis of churn prediction with machine learning

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Kudzai Marutsi; Monica Gondo

Revitalizing telecoms customer loyalty in advanced analysis of churn prediction with machine learning

Authors: Kudzai Marutsi, Monica Gondo

Unique Paper ID: 166779
Volume: 11
Issue: 2
PageNo: 1997-2001

Keywords: No Keywords Found

Abstract:
The researcher is objective to develop an appropriate model which helps in revitalizing telecoms industry customer loyalty in advanced analysis of churn prediction with machine learning. The researcher notes the high rate of churn within the telecoms industry in Zimbabwe due to factors such as pricing, service quality, network connectivity, customer support effectiveness and competitive offerings by other service providers in the market. Data was collected from the telecoms industry to find the major causes of churn prediction and devise appropriate practices to reduce churn and retain and attract customers. The researcher engaged relevant people from relevant departments with the main thrust of obtaining the needed research data. To effectively reduce churn within the telecoms industry the researcher concluded that, it is imperative for service providers to enhance customer service quality, offer competitive pricing and promotions, improve network reliability and coverage. Innovation and introduction of new services regularly is crucial to maintain customer loyalty. It is also crucial to use predictive analytics to identify and retain at-risk customers. Three models; Logistic Regression, Decision Tree and Random Forest were tested by the researcher to determine the effective model that the telecoms industry in Zimbabwe can use to reduce customer churn. Among the tested models, the Random Forest model demonstrated highest accuracy and was recommended to reduce churn rate in the telecoms industry.

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ISSN: 2349-6002
Volume: 11
Issue: 2
PageNo: 1997-2001

Revitalizing telecoms customer loyalty in advanced analysis of churn prediction with machine learning

Available:https://ijirt.org/article?manuscript=166779

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Journal no 47859

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