Support Centre Volatility Analytics
September 6, 2020
A global manufacturing company
Problem Statement
Analyze and enhance performance of the IT Ticket Operations’ support center by:
- Determining reasons for volatility in Key Performance Indicators (KPIs)
- Tracking of IT Server’s Ticket and Trigger notification data
- Predicting drop in performance to take preventive action for ticket/trigger details
- Acquiring ability to notify alarming trends of factors affecting KPIs to operation leads
Analytics Led Approach
- Identified variation in KPI by defining control limits obtained after treating outliers in data
- Used Linear Regression models to predict KPI values using significant factors and services impacted affecting KPI performance
- Created warning limits for significant factors using regression equation
- Developed excel dashboard to monitor daily KPI performance with warning signals for breaches
Business Impact
- Reduction in effort spend on KPI performance monitoring and analytics by 40%
- Developed models for KPI prediction with accuracy of around 87%
- Consolidated view of KPI data with relevant factors in excel dashboard
- Inputs to operations team for KPI performance improvement
Critical Success Factors
- KPI prediction with high accuracy of 87%
- Critical inputs to Operations Team for KPI performance improvement
- Data flow and Incidence chart for typical IT Operations’ support center processes across the board for the client
