Impact Solution Challenge 1 • Call volume dictates staffing levels • Existing volume forecasting was falling short • Inaccurate staffing proved to be very expensive - Overstaff: Direct cost - Understaff: Indirect cost, bad customer service • Looking to improve advanced analytics function overall to gain a competitive edge • Needed project management guidance and assistance applying best practices • $665K projected annual savings per call center • Achieved average accuracy of 93% for 90-day forecast • Automation frees up resources for other projects • Formalized a repeatable process for: - Moving ML models to production - Maintaining models • Identified key steps to making ML projects successful - Templates and best practices to be applied to other use cases • Two forecast models usi

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