In the typical contact center, every customer interaction can be counted, routed, tracked, measured, and scored against dozens of internal and external metrics. Automatic call distributors capture and store each and every caller's phone number and location, the date and time he called, the agent who received his call, how long the call lasted, and other data. Every word that is spoken or written by the customer or the agent can be recorded and tagged. Every action the agent takes, every screen and application she accesses, and every response she gives can be logged and stored. And as if that's not enough, a new breed of applications can even detect the emotions the caller and agent exhibit during the interaction. (See "New Emotions Analytics App Launches" for an example.)
Because of this, there generally isn't another corporate entity that manages more big data than the contact center. To complicate matters, the volume, velocity, and variety of this data will continue to grow, requiring agents to handle a wider breadth of issues coming in over more channels. "The channels in which people are communicating [with companies] are growing all the time, and so is the data being generated from it," says Donna Fluss, founder and president of DMG Consulting.
Naturally, this brings a slew of questions. How do contact centers capture, track, and manage all of this data? Should they bother to capture all of it? Is it possible, or even necessary? If it's not necessary, what data should be captured and tracked, and how should this be done?
This is where analytics comes in. "Analytics is the answer to the contact center's big data challenge," Fluss says. She notes that analytics can help companies improve agent productivity, reduce operating costs, enhance the customer experience, reduce customer attrition, uncover new sales opportunities, increase regulatory compliance and script adherence, and much more. Analytics can even turn all that data into profits, if companies know how to access and use it correctly.
The problem, though, was that until recently, very few companies were able to access or use the data. Thankfully, that's no longer the case.
Overcoming Cost Concerns
Contact center analytics has been around for decades, but the high cost of most solutions put it out of reach for many businesses.
"You originally needed so much technology that most companies couldn't afford it," says Zubin Dowlaty, head of innovation and development at Mu Sigma, a Chicago-based provider of big data and analytics solutions.
Fluss agrees."Up until about two years ago, it was extremely difficult to get the investment," she says.
As a result, there has been constant pressure on the market to deliver solutions more cost effectively, and the cloud is enabling vendors to do just that. In December, inContact, a provider of cloud-based contact center software, added speech analytics from Verint Systems to its workforce optimization suite. Providing contact center leaders with cost-effective access to sophisticated intelligence tools was the prime motivator, Paul Jarman, CEO of inContact, said at the time.
Leveling the Playing Field
"Delivering speech analytics capabilities through a true cloud solution is really a game changer for the market, because it makes these sophisticated tools more accessible and affordable to contact centers of all sizes," Jarman said in a statement. "Customers no longer need to purchase expensive hardware or hire specialized staff to manage their speech analytics engine, because it's all there in the cloud. It's a new level of operational insight at a fraction of the cost."
That inContact partnered with Verint for this offering is also significant, as it demonstrates another growing trend among contact center analytics providers: Because there are so many parts, full solution sets typically do not come from just one vendor.
These newer multivendor solution sets have required simpler integrations, and vendors are starting to deliver there as well. Dowlaty calls this the "API economy," where customers are given an a la carte menu that allows them to build with multiple applications and integrate them all through shared application programming interfaces.
"This is bringing down the costs, and gives [users] a more robust, best-of-breed set of solutions," he says. "Solutions are available much quicker, integrations are quicker, and you can rapidly grab technology from another vendor and plop it into what you've already got."
Modern contact center analytics, Dowlaty adds, "are low-cost, low-touch solutions. You can scale them out easily."
Matthew Storm, director of innovation and solutions at NICE Systems, says the simpler integrations have also enabled companies to start small and add on without a lot of extra work. "You do not need to replace what you have every time," he states. "You can pull new data and marry it with the interactions that are already being pulled from today."
Anna Convery, executive vice president of strategy at OpenSpan, a provider of agent desktop analytics and desktop automation software, sees this as an important new capability, especially as many small and midsized businesses leverage analytics. She maintains that businesses should test the waters and not simply plunge into the deep end of the analytics pool.
"You need [an analytics platform] that can grow and evolve with you," she says. "You need an analytics platform that will be able to aggregate data from other sources as the business grows and expands."
Convery suggests that businesses seriously consider what they want to find out before selecting any analytics products. "And don't think about technology solutions only," she says. "Think about what you are trying to do as a business and what questions you are trying to answer. Then look at what will be the best source of that data."
From real-time to predictive
Modern contact center analytics solutions have also benefited from the emergence of real-time capabilities, which analysts say has added a new level of interest in analytics, giving companies the power to influence the outcomes of customer interactions as they are happening.
Real-time analytics, Fluss says, started to surface in late 2012 but didn't really become viable until 2013.
Using real-time capabilities, contact center managers can see what is happening during a live customer interaction and join the conversation or alter the script to influence the outcome.
The value of that can't be understated, but Fluss and others expect contact center analytics vendors to work harder to incorporate more predictive capabilities into their solutions. "Predictive analytics is the future in the contact center," Fluss says.