Speech Analytics Keeps Green Mountain Coffee Grinding
the contents of each call into useful data. The company reported that in September 2013, three contact centers received 23,530 calls combined, totaling 1,569 hours of conversation, "[and] while we can't possibly listen to all of these calls manually, with analytics we can analyze 100 percent of them," Isham says.
"We've been able to discover training opportunities for our agents against current business processes, discover methods certain agents are using on certain type of calls and turn them into best practices for everyone," he adds. "We've [discovered] systems issues that normally would never make it to the right people, and we continue to discover new uses for our now structured data every day."
Green Mountain Coffee Roasters has also deployed speech analytics in its reception area to analyze its corporate calls to better understand the issues receptionists were dealing with. The company is using those findings to improve its auto attendants, "whether it's options we are providing or not providing or the verbiage that we are using to describe an option," Isham says. By using analytics for the 2,865 calls that reached a receptionist in just one month, the company was able to achieve an estimated 5 percent reduction in callers who had to speak with a receptionist, about 143 callers less than before the company applied analytics.
Additionally, in June 2013, Green Mountain Coffee Roasters started a project around Calabrio Desktop Analytics to help with payment card industry compliance regulations, and discovered speech analytics provided insight into just one dimension of the customer interaction.
"We had what the agents and customers were saying, but we were missing out on the other important dimension of what the agents were doing," Isham says. "It was an opportunity to understand and create best practices with what the agents are doing, which ultimately leads to quality of experience of the customers' interaction with us."
With desktop analytics, Green Mountain Coffee Roasters was able to spot additional opportunities where agents were practicing routines and processes that, if deployed across the organization, could greatly increase efficiency. Desktop analytics reported that agents who had a "sticky note" application open on their computers during calls were more efficient.
"When we drilled into how they were using the application, it showed those agents were taking notes on the application instead of using paper and pen," Isham says. "Because they were able to quickly copy and paste their notes into the system, they were more efficient. We were then able to suggest this note taking method in training."
Additionally, Calabrio's desktop analytics showed that agents who viewed a Web-based message board tool between calls spent about six hours per week looking at that application. Agents who only viewed the application before breaks or other activities away from their desks spent significantly less time on the application. "We use this example in training, urging agents that if they can reduce time spent looking at the message board by one minute, that's one [more] minute they can spend talking with customers," Isham says.
The company's immediate plans include expanding the desktop analytics application into its internal help desk to better understand interactions occurring there.
"[We are] discovering trends that we can handle before [they become] larger issues," Isham says. "What solutions could we turn into self-service applications? What trainings should [we] be providing?
"Analytics has to drive action, not just insight," he continues. "Having a powerful but simple-to-use product such as Calabrio Speech Analytics, we are able to easily process and organize our unstructured data. That data becomes knowledge and, combined with action, we are able to enhance the quality of experience that we provide.”"
App at a Glance
Using Calabrio One Speech Analytics, Green Mountain Coffee Roasters was able to:
process 2,865 calls through its auto attendant in one month; and
achieve an estimated 5 percent reduction in callers who asked to speak with the receptionist, handling 143 callers less than it had before applying analytics.
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