A data and analytics strategy is a great place to start addressing some of the roadblocks that can pop up between you and your customers. Meeting your customers’ expectations, being where they are, and providing seamless support is an easy call to make when your data nudges you in the right direction. Your customer data is a goldmine that’s valuable to sales, marketing, and your evolving support strategy. To better understand how data can be used to improve customer service, we took insight from Zendesk’s director of training and quality assurance, Holly Vande Walle, and panelists from a session at the Zendesk Relater conference on optimizing customer analytics.
How big data improves agent experience
Big data only helps customer service employees when it can be tracked, viewed, and acted upon across the organization. Key performance indicators for support, like customer satisfaction (CSAT), first response time (FRT), and total time to resolution (TTR), can be pulled and viewed to improve existing workflows. For support agents, the findings might mean coaching opportunities.
“Where we get the data to do the coaching and development is on the individual scoreboards that will tell us about their total time to resolution and their first response time,” Vande Walle explained. This coaching might mean working toward quicker resolution times as an overall best practice, but when data suggests certain products and features consistently result in tickets with slower TTR, it might mean that advocate teams need further education.
In the field of customer experience, know-how is just as important as politeness. Support is about effective, empathetic problem-solving, which comes with in-depth knowledge of the product.
“We do more of our emphasis on in-depth product training,” Vande Walle said, highlighting that while soft skills are important, sometimes the solution requires deep technical know-how.
Taking the time to focus on training demonstrates how agents are indispensable—which can lead to a better agent experience and morale. Elisa Reggiardo, Chief Brand Officer at Tymeshift, writes that once agents understand the depth of possibility in their role, they can flourish, helping organizations grow. There’s evidence that suggests reinforcing agents through education and coaching—and empowering them with data—means they’ll be more likely to stick around.
Making relevant data visible to agents can help them meet their targets, clear their tickets, and keep key metrics top of mind. This is possible when information from their customer conversations, like KPIs mentioned above, are displayed on intuitive, prescriptive dashboards.
The future of the contact center is a data-driven utopia. Unified customer profiles, support data and history from every conversation, visibility into the entire customer lifecycle—it’s almost too good to be true. But data isn’t all schmaltz, as more and more companies are adopting support strategies and CRM solutions built on the idea that big data fuels growth.
How data can improve customer service
Data can be used to improve the decision-making process within customer service teams. As mentioned above, we use data to improve and iterate on team performance. Vande Walle notes that one way to do this is to use a skills matrix to demonstrate familiarity with product and competency, cross-referenced with metrics like CSAT. This can be used as a learning tool to affect change and implement training where necessary.
At the Relater session on optimizing and improving customer analytics, product expert Andrew Forbes sat down with a panel of CX and data experts to discuss how better data leads to better customer satisfaction. For panelist Brent Pliskow, director of support at Box, it’s important that customer service strategies reflect business strategies—and the best way to ensure this is with data that supports necessary education and training.
Pliskow explained, “We hope that what we’re feeding to our agents and what they’re paying close attention to, driving towards satisfaction and resolution times, is then reflected in our data that we can actually say is meeting our top-level business strategy and serving our top-tier customers effectively.”
Understandably, if goals are not being met by your existing support strategy, it might be time to take stock of the data you have and analyze it for guidance on where to go next
How to effectively manage data for customer service
Improving CX through data and analytics is like peanut butter and jelly—fundamentally inseparable. But whose job is it to effectively manage customer service data? According to Relater panelist Olena Gyrenko, ops specialist at Booking.com, it’s everyone’s job—and the more visibility, the better.
“Everyone is capable of building reports and checking data,” she says. “One head is good, but two heads are better. We have multiple instances and multiple people are using it—everyone can use it.”
To effectively manage data for improving customer service, you can work with a number of out-of-the-box solutions, including intuitive data-capture software with pre-built customizations, interactive dashboards, real-time analytics, and individual scoreboards. For a larger operation, you might need insight from a third party. Partnering with industry experts and consultants is advisable if you’re looking to scale your data and analytics game.
Other panelists suggested creating a dedicated data and analytics team that meets regularly to discuss support data and to make necessary adjustments. This is an essential step in developing a customer service data management strategy. Pliskow highlighted his own company’s metrics task force as an example: “We made sure that as we were defining our KPIs and metrics, we were cataloguing them. We have a dictionary in a Confluence article that says how we measure pending time. For example, does that include business hours or non-business hours and weekends? All of that goes into an article that everyone in the organization can reference so we know we’re aligned when we start to pull reports.”
Software for using data to improve customer service
Figuring out what’s best software-wise requires a bit of homework. The best software to pull customer service data depends on the size of your business, your support team, and products. If your business has a growth-mindset, certain customer service data points will lend themselves better to your growth.
Data is not scary.