CRM Analytics can combine Communications Cloud and legacy information to identify churn patterns across subscriber behavior, service history, payment behavior, usage, and other relevant indicators. Einstein Discovery or predictive scoring can then operationalize that analysis through Next Best Action, presenting the agent with a configured retention treatment when the customer is assessed as likely to churn. The key is to represent the requirement in the same layer where Salesforce evaluates that business state. A simple report of completed disconnects is retrospective and does not proactively identify customers at risk. The objective is to detect risk before churn occurs and put the recommended intervention directly into the service or sales interaction. This avoids mixing customer-facing product logic with downstream technical state or using integration code as a substitute for catalog and orchestration design. When the model is aligned correctly, the platform can validate the request, retain the required lifecycle information, and generate the appropriate downstream action without redundant processing. That improves auditability and makes later changes easier because administrators can adjust the relevant metadata rather than reworking multiple custom components.

Study Guide Reference:CRM Analytics; churn prediction; Einstein Discovery; Next Best Action; retention.