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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

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