Skip to main content

How To Drive and Measure User Experience - Part 1

Ron van Haasteren
TOPdesk

Service desks teams use internally focused performance-based metrics more than many might think. These metrics are essential and remain relevant, but they do not provide any insight into the user experience. To gain actual insight into user satisfaction, you need to change your metrics. The question becomes: How do I efficiently change my metrics? Then, how do you best go about it?

Living in the Age of Customer Experience

The customer experience is vital to the outcomes of your service team. The word "experience" is critical. The quality of the user experiences is paramount.

When we look at our internal customers — our employees — their expectations are continually changing. For them, they want to stay in the flow, remain productive, and make meaningful progress in their work.

Customer experience is the sum of the employees' perceptions of working in an organization, "perception" being most important. To understand the experience, service desk members must ask their users to define their experiences. Part of this journey is managing the emotional parts of the customer journey. However, even if you meet expectations, but somehow, the emotional experience goes south. Then, while the issue may have gotten resolved, this doesn't mean the user is happy. Perceptions are not the same as results. So, even if the service desk meets all pre-defined success metrics, this doesn't mean user satisfaction is excellent.

Taking the pulse of the user is vital to organizational success.

What is the User's Experience?

The service desk delivers support to users, but they must measure the services provided and which are the most important to them. When measuring the user experience, you may find that your services need improvement.

For example, one organization I recently worked with let their customers ask them questions whenever they needed assistance. Thus, users found that the service desk remained open for users, who soon understood that their concerns were always valid; this only occurred because the service desk asked users how to support them best.

There are likely dozens of things that your department can address, but the team can't handle everything at once. Start with what's most important to the user so they can experience the best benefit for your effort. You can achieve this in several ways. For example, consider focus groups. These are what you think they are: teams sitting down with a group of users to ask them about the services provided. You are asking about specific goals and measuring outcomes.

Even though these groups can be a good starting point if you have nothing in place and can be easy to implement, they can require a fair amount of trust otherwise these groups can turn them into ranting sessions. Get through the negativity to regain confidence before diving into what you want out of these focus groups.

Periodic Measurements and Continuous Measurements

Periodic measurement is examining your services regularly, through a survey, for example. Alternatively, continuous measurement is the use of a brief survey to ask for feedback from customers about the services they just received after every interaction. Periodic measurement only provides a general overview of aspects that apply to multiple services, such as how friendly the department is and how well the communication is. These assessments are a great place to start because they help provide a picture in terms of user experience.

Because periodic measurements can be pretty general, how you phrase your survey questions to users matters. "How do you rate our services?" will not suffice. You must dive into various aspects or themes of the service so that you can gauge authentic user experience.

There are usually five main themes that the customer thinks of when experiencing a service ...

Read How To Drive and Measure User Experience - Part 2, covering the five main themes and more.

Ron van Haasteren is the Global Culture Strategist at TOPdesk

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How To Drive and Measure User Experience - Part 1

Ron van Haasteren
TOPdesk

Service desks teams use internally focused performance-based metrics more than many might think. These metrics are essential and remain relevant, but they do not provide any insight into the user experience. To gain actual insight into user satisfaction, you need to change your metrics. The question becomes: How do I efficiently change my metrics? Then, how do you best go about it?

Living in the Age of Customer Experience

The customer experience is vital to the outcomes of your service team. The word "experience" is critical. The quality of the user experiences is paramount.

When we look at our internal customers — our employees — their expectations are continually changing. For them, they want to stay in the flow, remain productive, and make meaningful progress in their work.

Customer experience is the sum of the employees' perceptions of working in an organization, "perception" being most important. To understand the experience, service desk members must ask their users to define their experiences. Part of this journey is managing the emotional parts of the customer journey. However, even if you meet expectations, but somehow, the emotional experience goes south. Then, while the issue may have gotten resolved, this doesn't mean the user is happy. Perceptions are not the same as results. So, even if the service desk meets all pre-defined success metrics, this doesn't mean user satisfaction is excellent.

Taking the pulse of the user is vital to organizational success.

What is the User's Experience?

The service desk delivers support to users, but they must measure the services provided and which are the most important to them. When measuring the user experience, you may find that your services need improvement.

For example, one organization I recently worked with let their customers ask them questions whenever they needed assistance. Thus, users found that the service desk remained open for users, who soon understood that their concerns were always valid; this only occurred because the service desk asked users how to support them best.

There are likely dozens of things that your department can address, but the team can't handle everything at once. Start with what's most important to the user so they can experience the best benefit for your effort. You can achieve this in several ways. For example, consider focus groups. These are what you think they are: teams sitting down with a group of users to ask them about the services provided. You are asking about specific goals and measuring outcomes.

Even though these groups can be a good starting point if you have nothing in place and can be easy to implement, they can require a fair amount of trust otherwise these groups can turn them into ranting sessions. Get through the negativity to regain confidence before diving into what you want out of these focus groups.

Periodic Measurements and Continuous Measurements

Periodic measurement is examining your services regularly, through a survey, for example. Alternatively, continuous measurement is the use of a brief survey to ask for feedback from customers about the services they just received after every interaction. Periodic measurement only provides a general overview of aspects that apply to multiple services, such as how friendly the department is and how well the communication is. These assessments are a great place to start because they help provide a picture in terms of user experience.

Because periodic measurements can be pretty general, how you phrase your survey questions to users matters. "How do you rate our services?" will not suffice. You must dive into various aspects or themes of the service so that you can gauge authentic user experience.

There are usually five main themes that the customer thinks of when experiencing a service ...

Read How To Drive and Measure User Experience - Part 2, covering the five main themes and more.

Ron van Haasteren is the Global Culture Strategist at TOPdesk

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...