Data is powerful, perhaps more than many of us realize. It contains nearly infinite applications. Yet while its limitless possibilities are seductive, they also provide ample opportunities to get lost in the weeds. One of the most useful applications of data in the support world is the measurement of your team’s performance. With the help of historical data, you can learn a thing or two about how your team performed in the past. But how can you take it a step further? Turn your data into action and use it to build a strategy for the future.
Think through the ultimate goals that you are trying to achieve. While it may be tempting to chase a quick average handle time or a CSAT benchmark, you might find more use in pursuing customer outcomes as your primary goal. As CA technologies notes, “measurements don’t always indicate the outcome of the work, and whether it’s truly impacting the business.” Are your customers happy? Are you providing the service that you advertised? These outcomes, while abstract, can elicit more empathy from your support team than a simple number. Use your metrics as a secondary focus. If your agents are instructed to simply make your customers happy, they’ll be less likely to game the numbers in their favor.
Monitor Your Team in Real-Time
One of the most difficult aspects of measuring team performance is the fact that we are continually looking towards the past. Average handle time, first response time, and similar metrics only show you what’s happened. A real-time view of your support team can be a powerful tool.
Use something like a Team Pulse dashboard to understand how your agents are performing in the present. A dashboard like this can show you how many conversations are currently being handled, how many conversations have been recently completed, and which types of queues are currently in use. What’s even more insightful is the ability to understand which agents are at full capacity and which agents have bandwidth to take on additional tasks. If you notice that one of your agents is perpetually at or over capacity, that may be a signal that they need help.
If you are working in the customer support space, chances are high that you already have a good handle on the basics. You know that fast average response times are desirable and long average handle times should be avoided. Instead of rehashing common knowledge, let’s dig deeper into a sample dataset. For this exercise, I’ll use the 2018 customer survey data provided by San Francisco International Airport.
I’ve cleaned the data to focus on a handful of variables: day of the week, gate, boarding area, STRATA (AM, MID, PM), peak vs. off-peak, and satisfaction score. Satisfaction scores are ranked from 1 – 5. Let’s say I’ve been tasked by SFO to understand why certain passengers may have ranked their experience lower than others. Are there trends to discover?
First, I want to see if I can predict which variables are most likely to affect the CSAT scores.
There’s a couple of interesting things to note here. “STRATA” is the most highly correlated with satisfaction scores. In other words, whether a passenger flies at morning, midday, or evening can influence whether they are satisfied with their experience. This correlation may be a hint that I need to analyze the teams that service the airport during those chunks of time. As a disclaimer here, this particular model captures only a sliver of data. It still provides a good sandbox.
Knowing that time of day may be a factor in customer satisfaction, I dig deeper.
We can see here that the overwhelming majority of respondents are happy with their experience (ranking SFO with a 4 or 5). However, we do see that respondents who fly at STRATA 3 (ie, on flights departing after 5pm) are more likely to report lower satisfaction scores than other times of day.
Finally, I want to understand how satisfaction scores are reported by boarding area.
Another interesting observation emerges. Passengers who flew through boarding area A were more likely to report lower satisfaction scores. It’s worth noting that this boarding area also had the highest number of respondents.
Given what I’ve uncovered through the data, now might be the time where I want to approach the team to understand what’s happening from their perspective. Maybe there aren’t enough staff for the number of passengers moving through boarding area A after 5pm. Maybe there’s construction. Either way, I would start by speaking with the team to understand, rather than using the data as a weapon.
While this example may seem hyper-specific, consider the fact that SFO could be your support team, STRATA could be their shift schedules, and the boarding area could be something like the type of customer request.
Adapt and Evolve
Consider how new technology can affect your support team’s KPIs. Be on the lookout for “red herrings” in your data. Let’s say you’ve invested in a chat deflection tool as part of an ongoing initiative to drive efficiency through artificial intelligence. Part of this investment means that many of the common support requests typically fielded by your agents are now handled by AI. Initially, you celebrate the rise in deflected inquiries, but you become concerned about dropping CSAT scores. You determine the cause after careful investigation: your chat deflection tool is handling simple requests while your agents are working on more complex customer issues. These complex customer issues don’t always have a straightforward answer and satisfaction scores are suffering as a result.
It may be tempting to pull the plug on your deflection tool to save your satisfaction scores and the team’s morale. But instead of retreating, dig deeper. Consider the fact that you may need to start documenting a new type of data like a complexity score. Find a way to measure the complexity of your customer requests and use that data to paint a more accurate picture of your team’s success.
Want to learn more about how the right customer service software can help your team perform to the best of their abilities? Download our Buyer’s Guide here.