Predictive Modeling of Customer Churn in Telecommunication Companies in USA

David Anderson ()

Journal of Statistics and Actuarial Research, 2024, vol. 8, issue 2

Abstract: Purpose: The aim of the study was to analyze the predictive modeling of customer churn in telecommunication companies in USA. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Predictive modeling of customer churn in US telecom firms leverages extensive data on demographics, usage patterns, contracts, and interactions to predict churn using methods like logistic regression and machine learning. Key predictors include contract length, charges, tenure, service quality metrics, and customer sentiment. Achieving 70%-90% accuracy, these models guide targeted retention strategies and personalized interventions to mitigate churn. Unique Contribution to Theory, Practice and Policy: Customer lifetime value (CLV) theory, machine learning and predictive analytics theory & theory of customer relationship management (CRM) may be used to anchor future studies on analyze predictive modeling of customer churn in telecommunication companies in USA. Implement advanced data analytics and predictive modeling techniques to enhance risk assessment accuracy. Advocate for adaptive regulatory frameworks that balance consumer protection with industry innovation. Policymakers should consider regulatory reforms that promote transparency in premium calculations, standardize risk assessment methodologies across regions, and foster competitive market dynamics.

Date: 2024
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