At times, it takes longer to log a Service Desk ticket than it does to resolve a customer issue. Why do we spend so much time and effort logging tickets? What value do we get from storing years-worth of data? How can we effectively use data analysis and data visualization techniques to gain insight, streamline our processes and prevent future issues? Get answers to these as well as several other questions below.
1. We log Service Desk tickets to maintain visibility on recurring issues and frequent service requests. Analyzing collected data allows IT to address customer issues and complete requests proactively, which improves service delivery and increases customer satisfaction.
2. Accurate ticket categorization and including the right amount of detail are essential for effective data mining. Examples of ticket classification best practices:
a.) Incident vs. service request.
b.) Affected item / product / service.
c.) Category of incident (not responding, slow, error message, etc.) or request (how to, access, install).
d.) Priority (low, medium, high).
e.) Event description, resolution and closure – Free-text fields can be a critical piece for data analysis. Tell a story and include the proper keywords, but remain relevant and on topic.
3. Data analysis best practices:
a.) Categorize the events into buckets using one or more of the above classifications.
b.) Analyze the top categories to find the 20% of your tickets that cause 80% of the problem.
c.) Move to free-text fields for deeper-dives.
d.) Use data visualization tools and apps to design charts that tell a story of your data analysis results.
e.) Write an executive summary of the results, including your assessment and recommendations, then present your findings to the business.
4. Benefits of data analysis include answering questions frequently-asked by your executives, such as:
a.) What are my top 5 issues?
b.) What are my top 5 requests?
c.) Who are my top 10 “frequent fliers”? This includes requestors, customers with issues and both combined.
d.) What are my top 5 applications/products/services that generate the most incidents/requests?
e.) Have we seen this specific issue in the past? If yes, how often? And how do we normally resolve it? This typically results in deploying a patch that resolves a recurring problem, rather than handing the same complaint over and over.
f.) What is the average customer satisfaction score for each period? How are we trending?
g.) What are my average number of tickets per user per month? How does that compare to the industry standard (two tickets/user/month)? Lower-than-average tickets per user per month could indicate that you provide excellent self-service tools, or that your users avoid calling the help desk. Higher-than-average tickets per user per month could indicate system/product improvement opportunities, or having a world-class help desk that provides excellent customer service.
h.) How many tickets are we resolving at 1st level support? How many are resolvable at 1st level support? What is our true 1st level resolution rate and how does it compare to others?
i.) What is our average number of hours to resolve top priority tickets? What is acceptable and how does that compare to other firms? This can be measured for each team/department separately, as well as all combined from the customer’s perspective.
5. It is essential to capture incidents and requests generated by all contact methods, such as phone calls, emails, chat and walk-ins.
a.) Phone calls are the easiest to capture, but emails, chat and walk-ins may be easier for end users.
b.) Changing the “event source” is not an easy task for Service Desk Analysts to remember. Think of other ways the system can automatically indicate the event source. Tracking these will help you determine your most common event sources and adjust staffing to ensure adequate coverage and high customer satisfaction.
c.) Walk-ins: First, help the customer. Second, advise the attorney/staff of ways to contact the help desk. Third, record the interaction (and log it as a walk-in if possible).
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