Customer churn is a common issue for businesses, with over 95% of customers leaving without providing feedback. The churn rate, which measures the percentage of customers ceasing to use a service, is crucial for subscriber-based models. To predict future churn, businesses can use machine learning models built with Spark Classification algorithms. This process involves selecting data sources, determining churn rules, merging data, and training the model, which can be challenging and time-consuming. However, tools like Datagran streamline this process by providing a centralized workspace to integrate various data sources, allowing businesses to train, test, and predict churn efficiently. Datagran's pipeline tool facilitates the creation of machine learning workflows that automate the prediction of churn, enabling businesses to act proactively by sending results to apps like Slack for stakeholder analysis. This approach not only helps in retaining customers but also provides a competitive edge by allowing businesses to address churn before it occurs.