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Language versus Intelligence
Natural Language Understanding and artificial intelligence are often terms that are used interchangeably when describing virtual assistants, but they are actually two different things.
What is Natural Language Understanding?
Natural Language Processing is a part of artificial intelligence that helps computer machines understand, interpret and manipulate human language.
Natural Language Understanding (NLU) forms the basis of contact center self-service solutions, and when used successfully, it delivers flexibility, efficiency, and clear customer satisfaction. In its own way, it is comparable to the way in which artificial intelligence (AI) is impacting the contact center. In fact, NLU and AI are often used interchangeably when describing virtual assistants in this space, but the fact is that these two solutions are not the same.
NLU is, at its core, all about the ability of a machine to understand and interpret human language the way it is written or spoken. The ultimate goal here is to make the machine as intelligent as a human when it comes to understanding language. NLU is therefore focused on enabling the machine to understand normal human communication – referred to as natural language – as opposed to being able to communicate via computer-speak or machine language.
NLU is the core technology that sits behind modern interactive voice response (IVR) and virtual assistant solutions and is designed to enable the rapid and correct routing of callers and generally to enhance the customer experience throughout their self-service engagement. Because NLU enables the virtual assistant to understand people as they talk in their own words, it means it is no longer constrained by a fixed set of responses. In this way, it is able to effectively mimic a live agent interaction.
NLU is effectively a subset of AI technology, designed to enable the software to be able to understand natural language as it is spoken. Artificial intelligence is crucial here because the virtual assistant needs to be able to comprehend the intent of a question, as opposed to merely the words being said. Furthermore, it has to be able to understand the context of the conversation too, if it is to conduct an interaction that flows, rather than one that consists of individual, standalone questions and answers.
Because AI enables a natural language search, it is easy for the virtual assistant to find answers and learn on the fly, meaning it can better understand a human’s words and recognize a wider variety of responses, even if it has never heard them before. This means that users can speak with the assistant in the same way they would a human agent and they will receive the same type of answers that a human would have provided. NLU, therefore, enables enterprises to deploy virtual assistants to take care of the initial customer touchpoints, while freeing up agents to take on more complex and challenging issues.
Conversational AI & Natural-Language Processing:
As the first line of assistance, virtual assistants are able to capture and captivate customers, by providing them with the answers they need or guiding them to the right places where they can find such answers. And they are also intelligent enough to understand when they don’t have the answer, meaning they can then escalate the call to an agent-assisted channel, such as email or click-to-call.
NLU, therefore, holds the potential to have a massive impact on first call resolution (FCR), as it is able to direct customers to the right place, the first time around. With NLU, your callers can say anything they like and the virtual assistant should be clever enough to understand it. This means FCR is increased, along with your customers’ levels of satisfaction in the contact process – something that should lead to greater long term customer loyalty.
While NLU is a subset of AI, it is certainly not something that should be used interchangeably with the latter term, as AI in a broader sense is able to do much more than merely understand and contextualize natural language.
AI is actually a powerful tool that can aid and augment the entire customer service process within the contact center. AI technology is not only useful in assisting call center managers to route calls more effectively, but it is also able to provide agents with the data and tools they need to create positive interactions with customers. It can even be used to monitor customer satisfaction levels across a variety of channels – including voice, SMS, social media, and chat-based on voice analytics and the type of language used by the caller. In the end, this should result in a more productive and efficient contact center and a greater level of overall customer satisfaction.
AI is ideally suited to interpreting big data, which means it can be useful in identifying customer browsing patterns, purchase history, recent access to customer devices, and most visited webpages. Once it has collated all of this detailed information, the company can even use AI to offer its customers personalized recommendations and proactive service, based on the data patterns it has pulled together.
Without AI, businesses wanting to provide such a service to clients would require one or more dedicated analysts. Even so, you would expect the analysts to take days or even weeks to identify relevant patterns in consumer behavior. AI, on the other hand, can identify such patterns rapidly enough to enable you to deliver the service in near-real-time. Moreover, AI is able to utilize a range of analytics that the company may have, such as self-learning algorithms, as an example, to consistently improve its own performance.
Whereas NLU is clearly only focused on language, AI in fact powers a range of contact center technologies that help to drive seamless customer experiences. From IVR solutions that connect customers quickly and seamlessly with the most qualified agent, to prioritizing callbacks and ensuring the customer is called back when their position arrives at the front of a queue and on to predictive dialers that help to fuel sales through smart and effective lead management, AI offers a much broader scope of advantages to the contact center.
Finally, when one considers the impact that big data and the Internet of Things are likely to have on the future of the contact center, AI is clearly only going to play an even more crucial role in shaping the way brands and customers engage with one another in the future. Both NLU and AI are going to be vital to this future, but if they are to have the impact on the modern contact center that organizations hope for, it is important that people understand the differences, as well as the similarities, between the two.
[About the author]
Dylon Mills is the Director of Marketing Content Strategy & Development at Uniphore. As such, Dylon’s main responsibilities are to strategize, create and deliver content for Uniphore’s product portfolio that align with the global Go-To-Market strategy, corporate positioning, and marketing campaigns. Dylon’s prior work experience includes Product Management at one of the top Fortune 500 Technology companies, Symantec Corporation. Outside of work, Dylon enjoys problem-solving and any project that includes building/tinkering with tools. Dylon holds a BS Consumer Economics from the University of Georgia.