We’ve all been there. Something’s gone wrong, whether it be with your bank or a service provider, so you pick up your phone to call customer service. A voice recording welcomes you and asks you to explain why you’re calling. You hesitate, knowing that the voice on the other end is computer-generated, not human.
How can you explain your complex problem in a way that a robot can understand? Will you just get stuck in an infinite loop of call redirects and repeating yourself over and over again?
Not if the contact center is properly leveraging natural language processing.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP), is a branch of artificial intelligence that helps computers understand and process natural human language. Combining both linguistics and computer science, NLP is essential to any contact center looking to improve its self-service offerings and voice workflows.
However, human language is complex. Though there are basic rules to how language works, like parts of speech and sentence structure, in reality, people speak in complicated ways. Words can have multiple meanings, people use idioms and informal language, and sentiment, tone, and context can completely change the message someone is trying to convey.
In a human-to-human interaction, people can pick up on these nuances and complexities naturally. In a human-to-computer interaction, however, meaning and intent can easily be lost in translation. Natural language processing serves as an intermediary here, generating algorithms to dissect these intricacies and truly understand what a human is trying to say.
How To Use Natural Language Processing In Your Contact Center
Leveraged correctly, natural language processing is incredibly useful for contact center automation. As the first line of defense for an enterprise organization, contact centers and their agents are inundated with a variety of customer inquiries, from simple requests to complex conflict resolution. The more a call center can wield automation to lighten the workload on their agents, the better the experience they can offer their customers.
Contact centers can use natural language processing to their advantage in these three key areas:
1. Create Dynamic Call Workflows with Intelligent IVR
Interactive Voice Response (IVR) is the computer-operated phone system that directs incoming calls through a predetermined call workflow. Elementary versions of IVR have existed since the 1970s, but recent advancements in voice recognition technology have truly transformed IVRs to be much more intelligent than your standard call routing system.
With natural language processing at the helm of the customer journey, contact centers can design robust and dynamic voice workflows to intelligently understand why a customer is calling and direct their call accordingly. Rather than speaking in broken phrases or repeatedly declaring “representative”, customers can naturally describe their issue and NLP will tease out the caller’s intent. This conversational approach to call routing results in a better customer experience and boosts operational efficiency.
2. Automate Issue Resolution with Conversational Self-Service
Self-service resolution is in growing demand in the customer service industry, from both the customer and the company side of the table. In fact, 54% of companies believe that by 2025 at least 40% of customer issues will be handled entirely in self-service environments. With natural language processing, this prediction would not be exclusively limited to digital interactions but can be extended to voice as well.
With a voice-enabled self-service solution powered by NLP, call centers can offer customers swift, efficient, and human-like customer service all while freeing up their human agents to handle more complex, high-touch issues. When combined with other contact center AI and machine learning, such as speech analytics and integrations with service platforms, voice-enabled self-service quickly becomes a no-brainer for enterprises looking to deliver a seamless customer experience and reduce operational costs.
3. Demystify Voice Interactions with Comprehensive Speech Analytics
Perhaps one of the most critical places where natural language processing shines is speech analytics. Compared to digital channels, voice has a unique layer of mystery, where unless a manager is hanging on to every word in real-time, it’s near impossible to know what happens on a phone call.
Recordings and transcriptions fall short as well, with only 1-2% of recorded voice calls being analyzed. There is simply not enough time in the day to examine every call – for a human anyway. Voice bots, on the other hand, have all the time in the world to analyze conversations and relay pertinent information to the human powers that be.
With robust speech analytics powered by NLP, artificial intelligence goes far beyond scanning transcriptions for keywords or phrases. It understands the caller’s sentiment and tone, picking up on not just what the customer and agent are saying, but how they are saying it. Thanks to NLP, contact centers can truly gain full visibility into the “dark data” that would typically be hidden in voice interactions.
The Conversation Starts Here With Natural Language Processing
Natural language processing is a win for everyone involved in the contact center relationship. When used correctly, NLP alleviates both the customer and the call agent from mundane and repetitive tasks. And while some may worry that voice bots pose a threat to the contact center agent role, AI is actually positioning live agents as more valuable, not less.
In their June 2020 Market Study, Contact Center Weekly states that human agents remain an essential and irreplaceable part of the contact center. Rather than seeing voice bots and live agents in competition with each other, contact centers should recognize opportunities for the two to collaborate to deliver the best possible customer experience. “The ideal CX solution seamlessly blends that automated experience with access to live agent assistance in a single system to provide the effortlessness customers expect,” the report says.
In the simplest terms, let the bots do the legwork so your agents can do the important work.
