An Intelligent Sales CRM Framework Integrating Follow-Up Priority Prediction, Sales Forecasting, and Role-Based Dashboard Analytics

Customer Relationship Management systems are widely used to manage leads, clients, meetings, and sales activities; however, many traditional CRM platforms remain largely transactional and lack intelligent decision support. This research presents an Intelligent Sales CRM Framework Integrating Follow-up Priority Prediction, Sales Forecasting, and Role Based Dashboard Analytics. The proposed system combines a React based user interface, a Node.js and Express backend, MongoDB data storage, and Python based machine learning models to support operational and strategic sales management. The framework focuses on five practical business areas: dashboard analytics, follow-up management, meetings, follow-up priority modeling, sales forecasting, and daily closing reports. A follow-up priority prediction model helps sales teams identify which customer interactions require immediate attention, while a sales forecasting model estimates future performance using historical CRM data. Role based dashboards provide administrators, managers, and sales users with different analytical views for monitoring targets, activities, and outcomes. A notification subsystem further improves responsiveness through reminders and event-based alerts. The overall framework improves task prioritization, operational visibility, and data driven decision-making in sales organizations. The study demonstrates how integrating machine learning with CRM workflows can enhance productivity, reduce missed follow-ups, and support more accurate planning.