Service Desk Metrics vs. KPIs and Analytics – What is the Difference?


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I often get the question, what is the difference between a Key Performance Indicator (KPI) and a metric? Or, when in conversation I hear people using the terms interchangeably. Therefore, because these are very common terms used in the service support industry I decided to explain the difference between KPIs, metrics and analytics.

In today’s business environment we rely heavily on data to make informed decisions that impact the operations of every aspect of our business. In doing so, you will use a lot of highly technical terms like service desk metrics, data analytics, and Key Performance Indicators or KPIs. In many cases, some people seem to use these terms interchangeably. The truth is that each of these data-driven terms means something entirely different.

There is no denying the importance of quantifiable numbers for determining performance levels. While making the mistake of referring to analytics as metrics or vice versa is easily overlooked, learning the differences in meaning is important in developing, implementing, and maintaining a successful service desk management strategy.

What is a Key Performance Indicator (KPI)?
A Key Performance Indicator is a measurable, predefined goal or objective which organizations continuously monitor and evaluate to determine a targeted level of success, profitability, or efficiency. The KPI must be measurable, but this does not mean that the KPI is a constantly changing numerical value. While the official, written context of a KPI usually contains some sorts of numbers, they are essentially only there to define specific threshold limits for success or failure. The numeric values of a KPI’s definition only change if the original goal or objective changes.

What are service desk metrics?
If a KPI is a quantifiable, measurable goal or objective, then think of a service desk metric as the KPI’s constantly changing numeric value that identifies or “measures” the KPI’s related level of success at any moment in time. For example, let’s say that a company establishes a daily goal (KPI) to resolve 30 Service Desk Requests per hour. At noon on Monday, the service desk is averaging 120 calls per hour and out of those 120 calls, 34 of them are Requests that are being resolved. The numeric values of 120 and 34 are examples of service desk metrics – constantly changing numeric values that determine an organization’s current level of success or failure in achieving a predefined goal.

What are service desk analytics?
Analytics involve the evaluating (analyzing or analytics) of multiple, historical, numeric values (metrics) as a means of answering a specific business-related question. At its core, data analytics is the science of deciphering data from the past to predict outcomes in the future. For example, analytics can help companies determine or predict which new products or services will be most popular with its current customer base. Analytics can also be helpful in predicting future problems, such as the impending decrease in productivity of an individual service desk agent due to too many currently open Service Requests.

Putting it all Together
If data analytics are numeric values used to predict the future, then service desk metrics are numeric values that represent performance levels of the past. Meanwhile, the constantly changing numeric value of a KPI represents only the level of success or failure at the present moment in time.

Here’s another way to think about it: a well-defined KPI acts as a compass, identifying the best possible path towards achieving a desired destination. The service desk metrics are individual milestones that the company passes along the way, and the data analytics predict how long it will take to reach the destination.

Every KPI is a metric, but not every metric is a KPI. Remember, metrics are just numbers, and these numbers have no meaning unless they can be compared to an indicator which identifies which numbers are good and which numbers are bad. Only then can these numbers be analyzed over time to predict or determine how profitable, how effective, or how efficient the organization can be.

This article originally appeared on the RDT blog and was reprinted with permission.


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