Development of a Customer Churn Prediction and Retention Analytics Platform

Data Science — 2025, Undergraduate

This study developed a customer churn prediction and retention analytics platform that scores subscribers by churn risk, explains contributing factors, and tracks retention campaigns. The platform presents segment-level risk dashboards. Adopting a client-server architecture and a survey-based usability evaluation, the system was built using Node.js, Express, and MongoDB. The findings showed that the platform improved proactive retention and reduced avoidable churn, while achieving a high System Usability Scale score. The study recommends deployment in subscription-based businesses.

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