The new KPIs for a Service Desk based on Artificial Intelligence

KPI Service Desk

KPIs for Service Desk are key strategic indicators, for understanding and measuring the release performance of your services.

The Service Desk is the first entity through which the user communicates with the company, for its requests or problems; therefore, it is a precise indicator of the efficiency and effectiveness of IT work, towards company organization.

In this last period, Service Desk systems have started to evolve towards the guided use of Artificial Intelligence: these new technologies, related to the automation of IT processes, are showing a new way to provide services, faster and with greater continuity. In fact, 79% of enterprises plan to adopt an AI-based Service Desk, by the year 2021.

Considering this new trend, it is essential to identify which new KPIs (Key Performance Indicators) are relevant for modern Artificial Intelligence and Chatbot-based Service Desks.

KPIs for Service Desk: traditional delivery and measurability models

Using the new technologies, applied to Service Desk, is it possible to continue using existing KPIs, to achieve proper measurability of service performance?

Under traditional service delivery models, the primary channels for opening a ticket to the Service Desk are telephone and email, but these methods also involve issues, such as delays, associated with phone waiting, incomplete information delivery, or often bouncing information among different teams, for the resolution.

Based on this traditional model, the recognized KPIs for Service Desk are:

  • • Customer Satisfaction Score
  • • Agent usage
  • • First contact resolution rate
  • • First level resolution rate
  • • Agent satisfaction
  • • Aggregated Service Desk performance
  • • MTTR (Mean Time To Repair)

 

KPIs for Service Desk: emerging delivery and measurability models

The emerging models of service delivery, that modern Service Desks are focusing on, involve both Artificial Intelligence technologies, and new contact methods, which rely on collaborative tools, such as Microsoft Teams.

Therefore, modern Service Desks are focusing on new tools, which facilitate self-service problem resolution, through technologies, integrations and channels, that are much more direct and always available to users.

Self-service tools, that help improving the rate of "automatic resolution" are: the portal, with the Service Catalog, chatbots, to facilitate and guide the user, in opening or self-solving an issue and process automation of requests routing. Users no longer have to use channels such as phone and email, as they find these new tools much faster and easier.

The innovation, introduced by modern service desks, also affect performance measurability conditions.

The KPIs for a Service Desk, based on Artificial Intelligence, automation and collaboration are:

EVERGREEN KPIs:

  • • Customer Satisfaction Score
  • • Mean Time To Repair (MTTR)
  • • Agent Job Satisfaction

 

UPDATED KPIs:

  • • "Cost per ticket": replacing "Cost per contact", as a modern service desk "automatically solves" most repetitive problems and, only in the case of complex problems, the resource intervention is required. Therefore, measuring "Cost per Contact" does not provide a complete picture of agent efficiency.
  • • "Number of tickets per agent": replaces "Agent usage", as a modern service desk, with applied Artificial Intelligence, needs to measure the productivity of the service team, in terms of resolution outcome. This KPI is measured by dividing the number of tickets, solved by the agent, per unit of time.

NEW KPIs:

  • • Accuracy of the chatbot with Artificial Intelligence: it is important to measure the accuracy of the system, by understanding what is the percentage of correct responses from the system, versus how many times the chatbot was unable to understand the request.
  • • Escalation from virtual to human: involving a human agent in the resolution, if the system was not automatically able to solve the problem. Measuring this parameter identifies the percentage of how long the platform automatically took, to perform this step.
  • • Service Desk automation quotient: it would be advisable to also measure newly created automations, identifying how much they speed up resolution process.

HelpdeskAdvanced: a modern Service Desk, based on Artificial Intelligence

Pat's solution, HelpdeskAdvanced, is a Service Desk application whose core application is based on process automation.

Like any evolved system, HelpdeskAdvanced is characterized by being multi-channel, facilitating self-service for users and integrating artificial intelligence technologies, through its chatbot.

Main features:

  • • Process automation: the processes govern the Service Desk, facilitating the work of Support, of Users, of the whole business organization.
  • • Intelligence Interaction: the Bot, thanks to Artificial Intelligence and machine learning technology, is always available, understands requests, categorizes them and provides assistance, in real time.
  • • Self-Service User Experience: offers its users the opportunity to independently search, consult and solve their requests and problems, improving their experience.
  • • Real-time dashboards and reporting: quickly create and share dashboards and reports, on the most important KPIs of Service Desk, on your performance, and on service dynamics.
  • • Omni-channel: anywhere and anytime, via mobile, portal, chatbot, email, the Service Desk is at the service of new business dynamism.