Customer support teams do a lot: They create help documentation, provide feedback to product teams, and, yes, answer customer questions. Though all of those different activities absolutely have an impact beyond customer support, that impact isn’t always obvious to others.
That said, there is a way you can more effectively communicate that impact to others: through customer support data. Customer support data is the universal language that allows you to share the influence your team has with others who aren't in the inbox every day.
In this article we’ll cover different types of data you can collect, common places to collect it, a couple of metrics to consider, and ways to present your customer support data to have the most impact.
The two types of customer support data
When looking at data, there are two primary types: quantitative and qualitative. You’ll sometimes also see them referred to as structured (quantitative) and unstructured (qualitative) data.
Quantitative data — which is what we’re primarily focusing on in this article — is anything that can be put into numbers, or quantified. Qualitative data is any data that can’t be expressed as a number and is generally concerned with sentiment.
For example, asking someone to rate something on a scale of 1-10 gives you quantitative data. You could chart the responses on a graph. Asking someone why they gave the answer they did gives you qualitative data.
To put it in support terms, a CSAT score is quantitative data. The additional comments some respondents leave on your CSAT survey is qualitative data.
Why you should collect customer support data
We’ve all seen plenty of people and organizations describe themselves as “data-driven.” Though that sounds nice, there’s hardly ever an explanation as to why they’re so obsessed with data.
The truth is, the answer will vary from team to team. However, in the case of customer support teams, there tend to be a few main reasons:
Visibility — Data is a very digestible item to present to a larger group to help them better understand what your team is doing and how they’re contributing. It’s also a very shareable item to include in things like company meetings or quarterly reports.
Performance — Data shows how you’re currently performing and gives insight into areas where you’re excelling and where you can improve. That baseline measurement is also a tool you can use to set future performance goals and a standard to measure against to gauge success.
Buy-in — Data is the primary language of senior leadership. Having strong data points could help strengthen your case to get budget approval for additional team members and tools.
Morale — Your team wants to know how they’re doing. With support data you can show how they’re improving and contributing to the overall success of the business. Knowing that information can help engage and motivate employees.
Common sources of customer support data
For some, the idea of collecting customer support data is intimidating. Though that’s completely understandable, the truth of the matter is that finding customer support data isn’t usually that complicated. In fact, many of the best sources of customer support data are the tools you use to support customers every day.
Most help desk software includes data about volume of conversations, busiest times of day, and busiest channels, as well as performance data like first reply time and average handle time. With some tools you can dive even deeper with reporting features like tags to understand the most common reasons people contact support.
Some tools, like Help Scout, even have pre-built dashboards, making accessing your data even easier. You’re also able to create customized dashboards if you want to focus in on certain areas, teams, or agents. Dashboards can also be a good way to share data with others, as it’s easier to digest than raw data.
With knowledge base software you can get insights like how many people visit your help center and which articles are most popular. Depending on your tool, you may also be able to see most-searched topics to understand if there are any gaps in your content.
Data that’s automatically collected for you through support tools is great because it’s not very resource intensive. That said, it usually only tells part of the story. In order to make that information richer, there are a few other sources to consider.
Though it’s not incredibly common for support folks to conduct customer interviews, it doesn’t mean they don’t have a ton of valuable information. Product managers and marketers generally lead these conversations.
Depending on what you’re trying to learn, it may be worth reaching out to those teams and seeing if you can sit in on one of those conversations or view a recording if it’s available.
Review sites like G2 and Capterra are great sources of qualitative and quantitative data. You can see how people are ranking your product on a variety of different measures.
It is a relatively labor-intensive process to collect and sort the data from these sites, so it’s probably not something you’ll want to do regularly, but it can be good for certain projects.
It's relatively common for a help desk solution to include a survey option to send automatically when a support interaction is closed. Customer satisfaction (CSAT) and net promoter score (NPS) surveys are two popular options.
With any survey, it’s important to write crystal-clear questions to make the data as useful as possible. Many support agents have been burned by someone giving a poor rating because they didn’t like the answer to their issue when they were supposed to be rating the quality of service, not the outcome of the problem.
Business Intelligence (BI) tools
BI tools (Google Analytics, KissMetrics, Tableau, etc.) are often controlled by other teams, but they are extremely rich sources of data and are useful for information outside of day-to-day performance metrics your support tools provide.
That said, they can be incredibly overwhelming if you’re not familiar with them. For most, the best thing to do is understand what data you want and then work with your data team to create a custom dashboard within the tool for you (if possible).
Making the most of your support data
Though finding and collecting data is important, the most important bit is putting that information to use. You want to make sure all your efforts materialize into some sort of real-world impact. In order to accomplish that, we have a few suggestions to follow.
Pick the right metrics
You can think of metrics like ingredients to a dish. You want to find a group of metrics that play well together and create a composed whole. You also need to be wary of metrics that are like truffle oil: They sound good, but in reality, they may be better left out.
For example, ticket deflection: It’s a seemingly useful metric that ties to ticket volume. Higher rates of ticket deflection, in theory, correlate to lower overall support costs. It’s easily trackable and has clear-cut tactics to positively affect it.
However, some of those tactics — like making contact information more difficult to find — are actually things that might hurt you in the long run. It’s what many would call a “vanity metric.”
It is tempting to shoot for quick wins, and some company cultures incentivize that, but long-term success comes from building better customer service habits over time. Seeking out metrics that are focused on the customer experience are probably what will serve you best.
A few examples are:
First Contact Resolution — Did they get an answer to their problem the first time? Customers who receive a first-contact resolution are nearly twice as likely to buy again from a brand and four times more likely to spread positive word of mouth.
CSAT (Customer Satisfaction Score) — How satisfied were they with the answer and the support they received?
NPS (Net Promoter Score) — How likely are they to refer a friend?
If you’re interested in seeing a breakdown of even more useful customer support metrics, check out our article on 12 Key Customer Service Metrics.
Tie performance back to larger company goals
When you think about core business goals, there are two questions to answer:
Does it increase revenue? (i.e., growth metrics)
Does it decrease costs? (i.e., retention metrics)
That may seem a bit reductive, but when you get down to brass tacks, that’s really all there is. Tying customer support data back to larger company goals is generally the best way to make people outside of support — especially leadership — care about the information you’re presenting.
For example, if you say, "This feature is broken, and we have tons of complaints about it" when trying to push for a change from your product team, it might not land. The focus there is on how it’s impacting support, not the company as a whole.
Instead, present it like, "Twelve customers are churning every quarter because this feature is broken, which accounts for $4,130 in lost revenue per year" or "Every person on our team would have an extra half-hour per week to contribute to knowledge base documentation if we invest in these three improvements to our internal billing tool."
In the second two examples, you’re showing the impact at a company-wide level. By creating a clearer picture of that impact, you make it easier for people to understand your argument and, hopefully, agree with your point of view.
Know your audience
When you're curating metrics to present to an individual or team, focus on what the specific audience needs to know. The scale and detail you include (and how you offer it) may vary depending on the audience, even if you address the same question.
For instance, while an individual agent might need to know exactly how a support team performs week-over-week, your CEO doesn't necessarily need to know about a slight drop in productivity, especially if it bounces back the following week.
Similarly, your marketing team may want to know how many support tickets are coming through from blog posts or your marketing site, but your sales team wouldn't benefit from that information.
Rather than creating a roundup of data that you send to every team, try to curate your data based on who will be reading it. This flexible, curated approach ensures the story behind the data doesn't get lost in the numbers — because what's the point of a report that doesn't communicate value?
Use multiple data points to tell a complete story
As much as you might like it to, one metric can't tell you everything. If you want to answer a guiding question, you need to assess multiple metrics to paint a clearer picture.
In the case of "How is our support team performing?" you could look at both the response time (quantity metric) and the happiness rating (quality metric).
Together, the two metrics tell you how quickly a team responds to customers and how effectively they make customers happy. It may even allow you to infer that customers are more satisfied within a specific response time range.
For example, when the response time goes above four hours, you may see a severe and consistent drop in the happiness rating. Together, the two metrics tell you a story: Responding within four hours is essential to customer satisfaction across the board.
Along with utilizing multiple metrics, you could also combine both quantitative and qualitative data.
For example, you could show a stat about first reply time, then add some color to that with a great comment your support team got on a CSAT or NPS survey. You could also use quotes from customer interviews or online reviews, too.
People tend to understand information better when presented as a narrative. If you spend too much time simply harping on the numbers, you might lose your audience.
Customer support data is an effective tool to show people outside the queue exactly how you’re contributing to the company as a whole.
Though it’s only one piece of the puzzle, it’s an important one, and the better you are at collecting, curating, and presenting it, the better you’ll be able to show exactly how valuable customer support is.