When we study artificial intelligence, we can see AI as innovative only if it creates some science-fiction situation that couldn’t have existed previously. For example, callers interacting solely with bots, interactions between speakers of different languages being interpreted back and forth in real time. And such assiduousness, if and when they come, have the potential to transform the way communication takes place.
However AI is well on its way to revolutionizing the process that contact centers follow, and it has nothing undeviatingly to do with caller-to-enterprise communications, and everything to make with a word that became the extraordinary reason globally, data.
AI ensures the ability to analyze unprecedented amounts of data produced by contact centers, and these new abilities could fundamentally transform how contact centers work all without the customer realizing. The only noticeable change for customers would be that they are getting much good service.
An e-book titled “Achieve Optimal Contact Center Efficiency & Agent Performance With Speech Analytics,” written by CallMiner, whose software is on the foremost edge of AI for the contact center has presented such an idea for experts to discuss. The e-book illustrates how AI’s ability to process applications of magnitude more data radically changes what contact center leaders can explain their systems and agents.
The e-book quotes Axcess Financial, which automated it’s quality assurance (QA) processes, as one enterprise sample. The result was that QA went from being a method that supervisors handled one call recording at a time, monitoring to a sampling of calls, to one where every call could be parsed. “In most contact centers, only between 1-3 percent of all conversations are audited,” the e-book notes. “Speech analytics automates the QA method and enables companies to monitor, decipher, categorize, tag and score 100 percent of the conversations between consumers and agents.”
What that means is that the company gets a fundamentally distinct view of the information it’s accumulating in the QA process.
“We didn’t have the distinctness we wanted because we were obtaining less than 1 percent of each agent’s monthly communications,” stated Kyle Carter, Director of Compliance for Axcess Financial. “We felt our representative scorecards were correct, but we really couldn’t recognize trends because we were obtaining so few contacts. Anything we observed was considered anecdotal.”
To explain the same, a human supervisor can be excellent at analyzing what they hear in a call, but they will never have sufficient time to investigate enough calls to understand if what they are understanding describes a significant trend.
Another section of this e-book that can be discussed is the understanding of the concept: silence. One can understand the idea that silence isn’t something one wants on a contact center call; nothing significant can be happening in those few seconds. At a minimum, agent time is signified as wasted.
One company estimated that a 10 percent reduction in call silence would equalize to a 1 percent reduction in call volume, writes the e-book. Such an amendment would effectively improve call handling efficiency by 1 percent without supplementing any incremental labor costs. A related analysis at another firm with 800 agents ascertained that reducing silence by just 5 percent on its calls would produce USD 331,250 in yearly savings.
And it’s not just about supporting the agent understand the customer and thus work further efficiently. One of the reasons for silence on a contact center call may be because the agent is hanging on for one of the enterprise’s systems, such as billing, to answer to an inquiry. This delay is anticipated to be a network problem, so if the enterprise maintains the AI-driven analysis to compare which systems are idle and when IT can troubleshoot the underlying network and implement faster response time from the application. That not only cuts time off the agent’s call, but it also reduces awkward moments as the agent stalls for the time.