This Guidance demonstrates how to improve the accuracy of the Customer Lifetime Value (CLV) metric by combining the data from both historical and proprietary databases, unifying operational and real-time data, and delivering that data through a powerful business intelligence service. Using the scenario of a financial institution, this Guidance demonstrates how to leverage data from various sources, such as transaction systems, enterprise resource planning (ERP), customer clickstream data, as well as data from customer relationship management (CRM) software. A machine learning model can then be trained on those results and predict a CLV. The results are displayed in interactive dashboards to visualize customer profiles, revenue, and lifetime value, empowering users to unlock actionable insights.