An Insurance Provider Slashed Mean Time Taken For Ticket Resolution By 15%, By Boosting Process Efficiency






Exploratory Analysis

Enabled 15% reduction in MTTR (mean time to resolve) by successfully identifying and discontinuing unactionable tickets

Identified caveats in the ticket resolution process                    

An Insurance Provider aimed to upscale implementation of analytical solutions to streamline and automate processes in its Infrastructure Services (IS) function. The existing ticket resolution process handled by the IS Operations team, involved tagging tickets into pre-defined groups based on product domain and allocating tickets to resources based on availability. Key attributes such as the skillset required to resolve a ticket, nature of the problem etc. were not considered, which led to inefficient use of time and resource. The team wanted to reduce the mean time taken for ticket resolution by 50% over the course of the year, but needed assistance in identifying potential areas for process improvement.

TheMathCompany partnered with the insurance provider to process vast records of ticket logs and draw out actionable insights that would help in automating ticket-tagging and streamlining ticket allocation, and thereby help in reducing the mean time taken for ticket resolution.

TheMathCompany identified potential areas for improvement, such as the Infrastructure Services (IS) department functions, which had rich and underutilized data.

The findings helped in:

  • Identifying potential processes that could be streamlined
  • Monitoring tickets to identify inessential tickets
  • Redefining groups based on the problem type, problem severity, skill required for resolution etc.
  • Automating tagging

This included:

  • Exploratory analysis of ticket description data such as resolution, time to resolve, application affected etc., using text analytics.
  • Root Cause Analysis to identify latent problems in the infrastructure and ticket handling processes.
  • Unsupervised techniques to identify latent groups and defining factors.
  • Efficient mapping of resources to tickets based on performance and expertise.

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