Ticket Severity Forecasting by Mphasis is an advanced machine learning solution designed to predict the severity of IT helpdesk tickets, enabling organizations to prioritize and address critical issues promptly. By analyzing historical ticket data, the system forecasts potential escalations, allowing IT teams to allocate resources efficiently and enhance overall service quality. Key Features and Functionality: - Machine Learning-Based Prediction: Utilizes sophisticated algorithms to assess factors such as ticket impact, urgency, and priority, providing accurate severity forecasts. - Natural Language Processing (NLP: Processes free-text ticket descriptions to extract meaningful features, improving the differentiation between ticket types and their appropriate resolution paths. - Customizable Input Fields: Supports user-defined input fields to accommodate the variability in ticketing information across different organizations, ensuring flexibility and adaptability. - Model Selection and Ensemble Learning: Employs a pool of models to analyze data, selecting the most generalizable model for ticket classification tasks, thereby enhancing prediction accuracy. Primary Value and Problem Solved: By accurately forecasting ticket severity, this solution addresses common challenges in IT service management, such as misallocation of resources and delayed responses to critical issues. It enables organizations to: - Improve First Call Resolution (FCR: By correctly assigning tickets to the appropriate teams from the outset, reducing the need for escalations. - Reduce Mean Time to Resolve (MTTR: Through proactive identification and prioritization of high-severity tickets, leading to faster resolution times. - Enhance Resource Planning: By predicting incident volumes and distributions, facilitating efficient capacity planning and resource utilization. Overall, Ticket Severity Forecasting empowers IT departments to deliver more responsive and efficient support services, ultimately enhancing user satisfaction and operational effectiveness.