In the world of contact center software, speech analytics is still very much a Johnny-come-lately. While call recording is practically ubiquitous, speech analytics has often been seen as a nice add-on that might uncover something interesting from time to time. Its true value has often been lost in a jumble of marketing buzzwords, confusing technologies with not-so-subtle differences, and grand expectations that were nearly impossible to meet early on.
"Contact centers have not been quick to adopt [analytics]," observes Donna Fluss, founder and president of DMG Consulting. "We have the technology, but it's only slowly being rolled out."
The latest research from both DMG Consulting and ContactBabel puts the number of contact centers currently using interaction analytics at just slightly less than 20 percent. The good news for the industry is that of the more than 80 percent of contact centers that do not have analytics in place, 25 percent have plans for implementation in the not-too-distant future, according to ContactBabel’s research.
"Analytics in general are starting to gain traction across a wide swath of the contact center market," says Larry Skowronek, senior vice president of product at Nexidia, a provider of the technology. "Call recording is essential to the contact center, and speech analytics is moving in that direction."
Analyst firm MicroMarkets earlier this year estimated the North American speech analytics market at $233.2 million in 2014, but the firm expects it to reach $614.1 million in 2019, at a compounded annual growth rate of 21.4 percent.
Among other reasons, MicroMarkets credits the rising number of U.S. contact center seats for the growth.
The U.S. contact center industry added 20,499 jobs between April 1 and June 30, following the previous quarter's gain of 7,965 new jobs, according to jobs4america. Through 2014, the industry saw the creation of 50,000 new jobs in the United States.
Other drivers for the growth in contact center analytics include new implementation areas, rising demand for cloud analytics and risk management solutions, and the need for real-time and predictive capabilities. A growing emphasis on customer service is also at the heart of the adoption curve.
"The older generations put up with poor customer service," Fluss says. "Younger consumers will not stand for it. They expect and demand a better level of service" that demands immediacy.
With those elements in place, analytics is finally starting, after more than a decade, to take its rightful place at the contact center table, and vendors of contact center analytics have met this growing interest with investments in research and development. And one of the biggest investment areas is real-time (or as close to real-time as possible) analytics.
That's the case with Verint Systems, where Alain Stephan, global vice president of customer analytics, notes that "speed to insight is something that [the company] is constantly working on, and not just with our speech analytics, but with text and desktop analytics too."
Verint has already gone through some pilot programs of its real-time analytics solution and is now moving those customers into deployment, according to Stephan. Many other vendors are at the same stage.
In fact, Nexidia's Skowronek expects real-time speech analytics to become mainstream within the next five years, though it could happen a lot sooner. "The kind of interest that we’re seeing from customers and prospects tells me that it could really pick up steam in the next year or two," he says.
This year, Fluss adds, is shaping up to be "the pivotal year" for real-time analytics in the contact center. "Companies are now starting to see the need to reduce customer effort, and that can best be done with real-time analytics."
"For some businesses, real-time [analytics] is an important and growing part of the armory they have to improve their efficiency and effectiveness," Steve Morrell, founder and principal analyst at ContactBabel, wrote in his company's Inner Circle Guide to Customer Contact Analytics.
HOW IT WORKS
Real-time analytics is essentially a merging of several technologies. It starts with speech analytics capable of scanning for predefined keywords and phrases; instances of talk-over; changes in pitch, tone, or talking speed; and even sentiments or context, while the call is happening. Then, some sort of artificial intelligence or decisioning engine determines what activity to take once a predefined marker has been identified. The application might, for example, alert an agent, supervisor, or retention team member; offer guidance to agents; escalate calls to second-tier support agents; or even trigger back-office processes. Then there needs to be some sort of reporting to close the loop.
To be most effective, real-time speech analytics should be able to not just look at the current call but also consider data from previous calls to spot overall patterns and trends, allowing the business to change course as needs dictate, Stephan explains.
"It's about bringing context into the conversation to deliver the next-best-action recommendation," he says. "Real-time analytics goes beyond just an analysis of the interaction, beyond the individual customer or call to the wider business. It starts with the interaction, but you need to link it to something much broader. Then you can be more nimble as you make decisions."
"Real-time analytics allows the business to take systematic action while the call is still in progress," Skowronek adds. That could mean preventing churn or ensuring that agents are complying with government regulations and company standards.
Under the current federal Fair Debt Collection Practices Act, for example, collection agencies making outbound calls to debtors now have to advise them that they're calling about an outstanding debt and that any information obtained could be used for that purpose. This warning is referred to as the "mini Miranda" because it's similar to the Miranda rights that law enforcement must issue to suspects prior to starting an interrogation. Real-time speech analytics can be used for quality assurance purposes to ensure that agents are issuing the appropriate advisories.
"There's a lot of interest in [real-time analytics] from a risk and compliance perspective," Stephan says.