Unidentified “It’s Getting Hot in Here” Metric

In the last chapter, we talked about how the ticket category tsunami wave can be seen as a data blind spot in service management.

Have you heard of the frog that sat in a pot of water while the water temperature slowly increased? The story goes that even though the water kept getting hotter and hotter, the frog never jumped out, and the water eventually boiled, killing the passive frog.

Well, the good news about this story is that it’s not entirely true: Frogs do typically recognize when water is getting hotter and eventually jump out. But the cautionary tale is still valuable; sometimes we don’t sense trends because they’re just not moving fast enough.

For service-desk managers in particular, the lesson of this story is simple: Monitoring standard metrics like the number of tickets received each month is not enough. You have to be able to fine-tune that measurement and look into the entire realm of change in your environment, and you have to be able to spot trends by having visibility into the right metrics.

Being able to dynamically filter data by multiple parameters and perform a comparative analysis of current values versus past values over time is essential to staying out of hot water as a service-desk manager.

We’re not talking about doing hard-core statistics here; simply being able to quickly glance at tickets by month for the past 12 months versus only the current month can provide you insight on two critical things:

Has a trend been increasing over the past few months, and does the metric this month support that trend or oppose it? By having access to this information, your own curiosity will lead you toward finding out the whys or why nots – and while doing so, allow you to discover many more insights related to your service desk data.
Ask yourself these questions to see if this is a blind spot in your organization:

• Do you know which analysts on your team are closing tickets more swiftly or slowly than normal?
• Is there a specific business unit or location that has more rapidly increasing ticket growth versus the others? If so, do you know why?
• Is the number of ticket transfers from tier 1 to tier 2 increasing or decreasing?
• Do you know the busiest days, weeks, hours and months for your help desk? What factors drive the increase during those periods: specific ticket types, specific customers or something else?

Having the answers to these questions can not only allow you to better plan for and handle future increases in ticket loads, but can also give you time to act on trends, altering processes accordingly in order to better manage what’s causing increases. Simply looking at a 30-day rear view of ticket totals by month is reactive, not proactive.

Case Study

The service-desk team for a national retailer was responsible for providing complete support to all retail stores, including ordering supplies and fixing facility issues at each location. Each holiday season the number of tickets would dramatically increase, and since that was a critical period for the business, additional help-desk staff would be added temporarily to help handle the additional workload (at an additional expense).

Being proactive, the service-desk manager decided to analyze the company’s data to see exactly what was causing the increase in tickets during that period: Was it the overall increase in store activity or something else?

After reviewing the data, the service-desk manager discovered that, although the total number of tickets increased during that time frame, the number of supply-order tickets increased disproportionately, while the number of IT-oriented tickets actually decreased. Upon examining the supply-order tickets more thoroughly, it was quickly discovered that the amount of reorders for essentials like bathroom supplies spiked during this period.

Furthermore, upon examination of the ticket-closure rates by analyst, it was discovered that, even though more money was being spent on additional staff during the holiday season, the overall result was slower resolution rates and lower customer-satisfaction rates. The reason? There were only two people on the team who handled supply issues!

Armed with this information, the service desk manager was able to proactively increase supplies at stores for the holiday season, allocate the appropriate human resources in the right help-desk specialty area, and reduce the overall number of resources added to the help desk during that time — making customers happier and saving the company money.

Now that you’ve learned all of the three data blind spots, let’s talk about how we can eliminate those for our company.