KPI SLA XLA indicators

KPIs To Consider, But Don’t get Caught on the Classics

Consistently meeting your incident management key performance indicators (KPI) and matching service level agreements is something you’re likely to contend with on a regular basis — monthly, weekly and sometimes daily basis – but meeting incident management targets is integral to your organization. You’ve got to consider what targets in which you need to aim, and the metrics you should be focusing on.

KPIs are, well, a lot of things to a lot of people. There are the tried and true “classics” and some new entrants to the dichotomy. There are even some other quality KPI practices you might wish to consider. Nevertheless, it’s important to realize that not everything is a key indicator of performance. However, there are metrics that deserve more attention than other metrics. In service management, or in regard to the service desk, some of the most common KPIs used include:

  • The amount of tickets logged per day: As a base-line, how busy is your service desk, and how does it evolve over time?
  • The number of repeat incidents: This indicates a need for a standard solution or escalation to a problem.
  • The average response time: How fast are incidents picked up? Is there a bottle neck?
  • The average solution time: This is especially powerful in conjunction with the above metric.
  • The number of tickets breached: How many tickets breach your SLAs?
  • The first-time fixes: Conversely, how many tickets are handled quickly? This, along with solution time, could indicate efficiency.

KPIs to consider

Some of the classic KPIs to consider include the percentage of calls abandoned, calls answered and the average speed to answer calls. These can help measure customer satisfaction and can reveal sub-optimal shift patterns or agent scheduling; externally, long wait times means frustrated customers keen to take their business elsewhere.

First-contact resolution rate is vital for customers who rightly resent being put on hold or passed through different departments. A low first-contact resolution can indicate poor internal processes or inadequate staff training. Finally, customer satisfaction scores provide direct feedback from the people who count, the customers. Take it seriously and don’t resort to email feedback surveys as your only data-gathering mechanism.

Other classic KPIs include operational efficiency. Of these are “agent occupancy” that describes how long the agent spends answering or dealing directly with calls. Low occupancy can indicate overstaffing and high operational costs. There’s also “handling time,” the measure of time an agent spends handling individual calls. Long times can reveal inadequately skilled agents or broken processes and tools. Additionally, there’s “call transfer rate” – indicating if calls are being routed to the right agent the first time or whether too many are being redirected and wasting agents’ time – and “cost per call” — that takes into account all fixed and variable costs expended in running the contact center operation and provides an overall gauge of efficiency when compared with similar operations.

There are other KPIs that can be considered “classic,” but we’ve established enough here for the point of our conversation. And as with anything, there is more to the story. For example, service management leaders keep in mind that these are “performance”indicators. If you find that something is pivotal to tracking the incident management performance of your service desk you should track it, but make sure your reports are clear and actionable. But you should not track everything.

Tracking the user experience

When considering the user experience, you’ll understand how important these experiences can be for fleshing out the black and white numbers of SLA stats. All SLAs might be met, and most incidents processed in good time, but are you keeping your customers happy? At TOPdesk, for example, we have five-star rating system that means users are able to gain consistent and useful feedback on their work and services, but don’t get too caught up in the numbers. They can be misleading.

Here’s a somewhat common example backing this comment. Password management usually requires the calling of the IT department. The password reset process for one organization we worked with took up more than 10 minutes with an organization for every employee. Thus, the SLA of 10-minute password resents were continually met, but their eXperience Level Agreement (XLAs) were not. Users were, understandably, upset with the lack of efficiency.

Finally, a good thing to do is to monitor your KPI average, but drill into the outliers occasionally especially when it comes to user experience.

Adding Customer Experience KPIs

Some additional KPIs to track that indicate how the customer finds your service include certain customer satisfaction rating (CSAT) (for example, four stars out of five); tracking lost productivity (downtime) and try to make sure you minimize it; ensuring clarity about products and services the department can supply; and a quality Net Promoter Score (30 percent to 40 percent).

Deep dig occasionally

Viewing KPIs top-down won’t give you the information to change and improve individual aspects of your team. Delve into team reports to analyze the difference in the time taken to process incidents across your first and second line teams could be shortened by automation. Consistently check to see if the priority of incidents reflected the difficulty of solving them. This approach often is best lost in larger teams but should always be a key aspect of your analysis.

Examine different time lines

Varying your KPI deadlines across contrasting time-frames will allow you to analyze both long-term and short-term trends so no worrying incidents are lost in the data. Checking across combined timeframes allows you the ability to present snapshots you can learn and develop from, particularly outliers in your data. Through this approach you can become more proactive as you prepare for anticipated dips or increase in tickets noted from previous data.

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