<![CDATA[Predictive analytics is an important approach for supporting data-driven managerial decision-making, particularly in addressing customer churn, which can adversely affect company revenue. This study aims to develop a predictive customer churn model and generate insights that can be used to formulate customer retention strategies. The method employs the Random Forest algorithm implemented through RapidMiner for predictive modeling, while Tableau is used for data visualization and interactive exploration of churn patterns. The dataset used is the Telco Customer Churn dataset, which contains information on customer services, charges, and characteristics.The developed model achieved an accuracy of 79.21%, demonstrating a relatively good ability to identify customers who do not churn, although its ability to detect customers who are likely to churn remains limited. Further analysis indicates that the main factors associated with customer churn are contract type, service quality, monthly charges, and customer tenure. Visualization using Tableau reinforces these findings by showing that customers with month-to-month contracts, higher monthly charges, and shorter tenure have a higher risk of churn.The contribution of this study lies in integrating predictive modeling with interactive visualization to produce more comprehensive and accessible insights. This approach can help companies shift from reactive to proactive strategies for customer retention.]]>
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