Natural Language Processing Strives to Meet Star Trek Standard

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Customer Confusion

Another hindrance is customer perception of natural language technology. "Callers are still looking to talk to a live agent," Tetschner says. "Automation of any kind is a turnoff for a lot of people, and that is universal."

Also, when met with an open-ended prompt, many consumers still get confused. "There are plenty of consumers who are not familiar with these systems and need some guidance," notes Bruce Pollock, vice president of strategic growth and planning at West Interactive, a provider of contact center solutions.

At the other extreme, some callers faced with natural language prompts tend to say too much, which also challenges the system. "If the nature of the call is more urgent, people tend to go on at great length about why they're calling and explaining their problem in greater detail than is needed," Pollock states.

For these reasons, some companies that have field-tested natural language IVRs have backed them up with directed-dialogue interfaces just in case the natural language engine fails or the caller has other problems. An example of this is the following: "I'm sorry. I did not understand you. You can say, 'check my balance,' 'make a payment,' or 'update my account information.'"

While those types of backups are useful to customers, they have done little to advance the cause of natural language processing and have left companies wondering why they should invest in natural language if they still must rely on directed dialogue.

NLP Is Not for Everyone

Another factor that has stood in the way of more widespread adoption of natural language is that not all IVRs are suited for it. "Natural language] is not necessary if you're going to present customers with a small list of menu options," Julio Murillo, a speech technologist at West Interactive, says. "You might not need a natural language interface. You probably just need to reorganize the prompts and shorten the menu options."

In some cases, a graphical interface might be more effective. "If a caller wants to find a movie, it doesn't make sense to have a system read off information about fifty different movies that are playing at the time," Kaplan contends. "That can be presented visually."

Where natural language is most effective is with companies that have long lists of products and large numbers of reasons for customers to call them.

That was the case at TalkTalk Group, a United Kingdom provider of Internet, TV, and phone services. Last year, prior to implementing a natural language call-steering IVR solution from Nuance, the company reviewed more than 30,000 calls and identified more than 300 call types.

So far, its call recognition accuracy has been 94 percent, resulting in 16 percent fewer transfers and a 26-second reduction in the amount of time spent in the IVR. Self-service use has increased by 28 percent and customer satisfaction has increased by 0.6 percent. TalkTalk expects to reduce costs by roughly $5 million per year as a result of the implementation.

With those kinds of numbers in its favor, industry insiders expect adoption of natural language to pick up. "I suspect that as the technology becomes democratized, cheaper to implement, and more effective, it will be more prevalent in contact centers," says Jonathan Gale, CEO of NewVoiceMedia, a provider of cloud-based contact center technology.

"More rapid deployment is on the horizon," Kaplan adds. "In the next year or so, you're going to see a lot more of these [deployments]. It's definitely coming."

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