Measuring IT Service Satisfaction When Your Reputation Is on the Line
Delivering Proactive IT Services: Part I
Many IT organizations feel out of control of their reputation. This is especially true during the ongoing global crisis we all currently face. During such times, questions naturally arise about whether dissatisfaction is being driven by factors that are within IT’s actual control. In such times of crisis, IT organizations desperately need information that can help them not only preserve their own reputation, but also that of the enterprises they serve.
To begin improving IT service satisfaction, organizations must have access to quality data that accurately measures true customer sentiment. Survey data cannot be relied upon wholly. IT leaders need to supplement surveys and add context to them — maybe even replace them entirely — using data-based IT analytics methods.
With IT business analytics, organizations can extract data from corporate social chatter, IT service tickets, performance monitoring reports, and other systems of record. They can then use this information to reveal a combined, 360° view of IT service sentiment. Analysis and visualization of this data allow for the creation of actionable KPIs, such as an aggregate sentiment score.
Leveraging analytics in this manner opens the door for measures like user-group-centered views, drill downs, slices, trends, and predictive AI projections. IT leaders can identify low-hanging fruit for service delivery improvements. Or, they can predict how certain changes could impact sentiment scores in the long run. Analytics, therefore, paves the way for a transition from a reactive to a proactive approach, allowing IT to improve their reputation and that of their enterprise by making measurable progress on the areas that affect satisfaction the most.
Why IT Customer Satisfaction Surveys Are Never Enough on Their Own
Obtaining visibility into IT service satisfaction can be challenging, especially if IT leaders only have access to limited, imperfect data. They may not even know where to start with improving known CSAT metrics. High service ticket volume and high operating costs can make most of their work feel like they’re just barely keeping up. Such an environment can force IT leaders to respond reactively, often only once a problem becomes highly visible.
The worst part may be that taking the true temperature of IT service customer satisfaction can feel impossible. Surveys — the most common method of measuring and expressing customer sentiment — often have significant shortcomings. Whether they use NPS, CSAT, or CES surveys, they all frequently share the same limitations. Low response rates, respondent bias, and a highly constricted view make it difficult to contextualize survey data within the actual activities IT engages in on a daily basis. Some IT service customers also simply refuse to take surveys seriously, answering all 5/5 perfect scores or 0/5 scores in an attempt just to complete all of the fields.
Risk of non-response typically increases with a higher volume of survey requests. One Numerify customer even sent out a survey with every single IT ticket, generating over 100,000 surveys in a given month. This led to an ultra-low response rate of just 0.2%.
Performing an accurate, revealing analysis on such a small data pool can be impossible. With sources of bias and non-response, there’s no way to know if the responses are representative. This leads to skewing, high margins of error, and a limited ability to compare your sample population with any sort of background level.
Making matters more concerning, surveys tend to disproportionately rate the direct IT service providers, who resolve problems but may have had nothing to do with managing the source of the issue on a daily basis. In other words, some surveys expressing dissatisfaction target service desk employees when the real source of frustration is the root cause of ongoing problems. For example, customers dealing with a switch to a new SaaS tool may rate specific IT service providers poorly when, in reality, the users are simply upset about the software not working as it should.
All of these flaws collectively cause surveys to obscure what should be an accurate view. Some surveys — perhaps even most surveys — are capable of providing useful results, but inherent flaws make them a weak source of decision-making data. This makes it highly difficult to glean insights for proactive changes that could improve satisfaction — and, thereby, sentiment towards IT and enterprise technology as a whole.
The solution? IT leaders need more data.
Modeling Customer Experience Using IT Analytics
How do you directly measure the customer experience for those receiving IT services? You can look to metrics for activities that form the basis of customers’ relationship with IT.
To investigate, IT leaders can:
- Identify common tech-related problem areas and top IT service request items
- Analyze metrics that reflect processes already in place, such as incidents, work orders, and HR requests
- Quantify common areas where service customers have problems, such as a specific productivity suite
- Tackle the big problems first, doing the most good with the least amount of effort
The ultimate goal of such an effort is to enable service desk agents to have a service-centered view of each specific customer’s interaction with IT. Where did this customer see success, and where did they encounter pain? Enabling such a perspective allows IT agents to be proactive in providing positive customer experiences.
On an administrative level, IT leaders can identify opportunities to rationalize the feedback they’ve been receiving while streamlining their support approach. All of this serves to identify areas where IT can provide a better service experience, across the board.
In order to obtain the perspective needed to drive proactive action, IT can source customer sentiment data from across multiple systems of record to reveal a combined view. IT leaders can consider this view as a bellwether — an impression of customer sentiment at a glance for a particular moment in time. Revealing an aggregate customer sentiment score when someone calls the service desk, for example, allows the agent to pull up the customer’s likely pain points and their history of IT interactions. They can then address the customer within this personalized context.
Such a bellwether has greater implications, too, thanks to the informative views it can provide upon analysis. IT leaders can decipher an individual’s service experience across a specific period, such as the last 3 months, 6 months, or year. Specific views can be obtained for user groups, such as people in a particular department, or those who tend to rely upon a specific enterprise product more often than others.
Using machine learning and AI, this data can offer exponentially more value. Trend analyses and predictive engines enable organizations to consider the ramifications of specific change rollouts, or they can allow the organization to prepare for challenges that may lie ahead.
Using a Data-Based Approach Opens Opportunities for Proactive IT Service Delivery Improvements
The true benefits of measuring IT service delivery satisfaction come when dashboards are put in the hands of those who provide direct customer service. They can model employee experience at an individual or group level by blending data across multiple sources, and they can capture actionable views through aggregate KPIs.
IT service leaders can also identify opportunities to gather even-more robust data, such as using machine learning and natural language processing to fill in incomplete fields that are key to revealing true IT service satisfaction.
Collectively, obtaining an accurate view through analytics allows IT leaders and enterprises as a whole to target the true causes of service dissatisfaction, lowering costs and improving productivity in a time when we rely on our technology more than ever across greater distances than ever before.
The whole process starts with obtaining IT data from relevant systems of record. We will reveal the best sources of this data in part II of this series on proactive IT service delivery.
For more information on improving IT service sentiment, watch our recent webinar: “How IT can proactively anticipate and deliver services that drive employee productivity“
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