AI-powered Field Service Framework to Enhance Efficiency of Technicians and Reduce MTTR by 30%

April 26, 2021

Today, most Digital Service Providers (DSPs) struggle to improve the efficiency of the field technicians, which leads to high MTTR (Mean Time to Resolve) and low First Time Fix Rate (FTFR). Delay in providing fixes can lead to significant customer churn and revenue loss affecting profit margin for DSPs.

Traditionally, 70% of the field technicians are dispatched to the field without any insights about the right issue, fault location, nature of the problem & solution recommendations. This results in a delayed lead time to resolve the issue, partial fixes, and repeat dispatches. 30% of repeat dispatch occurs due to improper fault isolation, and 47% of the dispatches require rework/repeat dispatch when inappropriate actions are taken by field technicians.

Field technician efficiency, if not improved, has a cascading impact:

  • Increase in the number of repeat dispatches
  • More time to resolve customer issues
  • Higher cost to the DSPs
  • Higher customer churn rate due to low FTFR (First Time Fix Rate)

According to Gartner’s report on “Critical Capabilities for Field Service Management,” 70% of customer interactions will involve an emerging technology such as machine learning applications, chatbots, or mobile messaging, by 2022.

To create a great customer experience, DSPs need to build and implement an AI-powered field service framework that helps the technician with the right fault location and guided actions as stated below:

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Download this insight to know more about:

  • How DSPs can build key components of the AI-powered field service framework such as fault location classifier, recommendation engine, and technician dashboard
  • How this framework can help DSPs reduce MTTR by 30%, improve FTFR by 25%-35%, save OpEx by 30%, thereby improving NPS

Authors:

  • Aiswarya D
  • Aarthi T
  • Sudharshan PS
  • Abhay Goyal

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