“I spent years hearing customers telling us exactly what we need to know, but that was overlooked because we didn’t have a process to capture it.”
Sound familiar? For people who’ve worked in Support, the frustration of knowing that customer conversations contain valuable insights, but not being able to communicate that value to other stakeholders is a common feeling.
Fortunately, Jason Viglione, a CX leader who was most recently Senior Director of Global Technical Services, including Technical Customer Support, at Olapic, has advice on how to use customer support data to influence customer experience strategies in your organization.
In this Signal VoC Member Spotlight, Jason shows us how to move beyond answering one ticket at a time. He breaks down how customer support data can be used across the business, and what he thinks Customer Support teams are missing in their processes today.
The Advantages of Using Support Data in Voice-of-Customer
In his presentation, Jason described several advantages of support data over traditional VoC collection methods, including:
High-volume: If you only rely on survey data, you’ll only be hearing from a very small percentage of your customers. Using support data gives you a much bigger pool of information to pull from, across your customer base.
Real-time vs cyclical: As any customer support agent knows, the queue never sleeps. Customers will continue providing feedback 24/7. When you harness this data, it provides you with an always-on, real-time view of what customers want.
Unfiltered and unprompted: Support data is collected at the moment when the customer runs into an issue. This can lead to incredibly candid expressions of frustration.
Captures interactions with a variety of stakeholders: Customer Support interacts with a wide swath of personas (e.g. buyers, end-users, analytics consumers). The feedback from these users can often go unnoticed because they don’t receive the invitation to participate in surveys.
Finally, support data complements existing CX lessons and data. Because it can fill in our gaps in understanding, support data can actually make your existing VoC program more powerful.
Understanding the Ticket Lifecycle
To capture support data, organizations need to be well-acquainted with the ticket lifecycle. At each point, there is an opportunity to collect data and improve the customer experience.
First, there’s a failure that causes customer stress.
The ticket comes in, the customer expresses their frustration with the issue. They might use descriptive or angry language.
Hopefully, the support agent then reduces the customer’s stress. Jason explains that the best support brings in other information to decide the best way to de-escalate. Timing is important. You don’t want to reply fast enough that you’re only using canned responses, but you also can’t afford to wait too long to provide a sufficiently detailed response.
Jason explained it this way: Imagine someone walks up to your desk and asks if you can answer a question for them. You reply “sure!” and then immediately get up and leave the room for an hour. They’d really wonder where you’ve gone!
Customers will feel this way too if Customer Support leaves them hanging without a substantive reply.
Constant communication over a ticket can continue to calm the customer. Sometimes simply letting customers know that you don’t have an answer yet is an update. Jason says that the back and forth “will give us a temperature of how customers are feeling about the issue, but also about the support they’re receiving.” This is valuable information as the ticket continues to evolve.
The final resolution of the ticket is not always clean cut - we might need to deliver bad news. Jason suggests following up with feedback surveys to understand if support did a good job on this step.
Note that the journey is not linear. This ticket has an end, but the next ticket will come in right after. And how we solve the first ticket will determine how the customer interacts with us in the future.
Taking it Beyond One Ticket at a Time
While each ticket provides its own set of data that can help your agents provide great support, there is more value to be found when looking at groups of tickets together. There are two ways to use customer support data to build better relationships: looking at the issues on a per-customer basis and identifying issues by type.
Issues per customer
When you zoom out and look at the types of tickets you’re getting from one customer, you can see three different things; their adoption, their implementation, and where they are in their journey.
For example, you would anticipate set-up questions early in the customer’s journey. But if you’re seeing these types of questions further into the journey, something is likely wrong. Perhaps there is a bug in the product that needs further investigation. This can help you understand how customers are really using the product.
Issues by type
Secondly, instead of grouping tickets by customer, you can group tickets by the type of issue. ie. How many customers are having log-in issues? Or issues with the dashboards?
When you find these common stumbling blocks, you can start to dive into whether there are misaligned expectations or if there is missing documentation.
In Jason’s previous career as a teacher, he would think a lot about these kinds of trends. “If everyone got the same question wrong on an exam, and in the same way, I need to look at what I did to make them think that was the answer.” Once you’ve discovered these trends, there’s no excuse for more of these tickets to come in, because we know about them and it’s on us to fix it.
Feedback for Product
Once we’ve mapped tickets into the reasons for customer frustrations, we can group them against a feature and then use this data to inform the product roadmap.
Gathering the data into this format pulls the valuable information out of customer conversations and provides it to a product team or CMO in a format they can use. As Jason says “the synthesis [of ticket data] is absolutely crucial to getting that value.”
Feedback for Sales
“As a proactive customer support leader,” says Jason, “If I knew what was going to go wrong, I can get in front of that and prevent it from being an issue. Now, we take that back even further to the start of the sales process to make sure we’re doing as much as we can upfront so that customers are successful.”
Using support data to influence the sales process gives teams the opportunity to chase the right-sized deals and choose customers smartly. Not only that, but it helps sales properly structure deals for the customer’s success.
Feedback for Customer Success
“When Account Management sat under Revenue and Support sat under Client Services we made natural enemies of each other,” Jason remembers. “But when we came together under a common umbrella, we became natural bedfellows because we were focused on the entire journey.” There are four ways that Customer Success can benefit from incorporating customer support data into their toolset.
Checking in on the long tail: Use support data to keep a close eye on “low touch customers” that don’t always have as much access to customer success managers. Are they truly self-service? When can we give them extra love? Do we need to automate other parts of the journey for them?
Bring ticket trends back to success teams: Spikes in ticket trends may align with the ups and downs of the customer journey. As long as it drops off at the point you’re expecting it to, then you have a good understanding of what your customers need. Ticket trends can also be used to identify internal user turnover.
Dive into user knowledge trends: As you see questions come in, you can find the most common places users stumble. Bring that into education and onboarding projects.
Provide pre-QBR briefings: Don’t let Customer Success go into a QBR meeting and get surprised by an unhappy customer. Use ticket data to target smart adoption of product features and create deeper value for the customer.
We’d be remiss if we wrapped up a discussion on ticketing data without touching on customer satisfaction (CSAT). There are many pros and cons to using this metric in your VoC program.
|Provides clear insight into how customer sentiment changes over time.||Not all customers respond to CSAT surveys.|
|Can reflect the usage, frustration, and knowledge patterns across multiple stakeholders.||CSAT doesn’t always reflect the opinions of the decision-makers, so can provide surprises at renewal time.|
|Collected upon resolution, so can provide feedback on the outcome.||Customers often “shoot the messenger” and give negative responses even though they received good service.|
Jason provides a different perspective when evaluating ticket customer satisfaction. He suggests reading between the results to gather information. For example:
- High CSAT score, but complaints about the fix can tell you a lot of information about what needs to be resolved.
- High CSAT score, but many open tickets close to renewal dates can be a stronger risk alert than a low CSAT score.
Join us for the next Signal VoC Member Spotlight!
Jason wrapped up by answering a number of questions from the community. Check out the recording below to see more!