CUSTOMER RETENTION ANALYTIC DASHBOARD

Abstract

In today's highly competitive business environment, customer retention is a critical driver of long-term profitability and sustainable growth. Identifying at-risk customers before they disengage allows businesses to implement proactive, targeted retention strategies rather than relying on reactive measures. This project presents the development of a Real-Time Churn Prediction Tool and an accompanying interactive dashboard designed to empower customer success teams with actionable insights. Leveraging machine learning techniques, the underlying predictive model analyzes historical customer data—including demographic profiles, engagement metrics, and service usage patterns—to calculate the probability of churn for individual users. The predictive insights are surfaced through a dynamic, Streamlitbased web application that provides real-time visualization of key performance indicators (KPIs) and underlying risk factors. The dashboard features an intuitive user interface that allows stakeholders to seamlessly filter data, monitor overall churn metrics, and drill down into specific high-risk customer segments. By synthesizing predictive analytics with an accessible visual interface, this system facilitates data-driven decision-making. Ultimately, the tool enables organizations to optimize resource allocation, enhance