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NLP in Your Contact Center: What You Need to Know
As I mentioned in my previous post, "AI in Your Contact Center: Understanding the Basics," the use of natural language processing (NLP) to conduct a conversation with a customer is far and away the most impactful use of artificial intelligence in a contact center. NLP underpins the conversational AI used with Web chatbots, voice assistants for enhanced IVR functionality, and voice computing interfaces for virtual assistants like Alexa, and as such is a critical technology to understand for the modern contact center.
NLP comprises two component parts: natural language understanding (NLU) and natural language generation (NLG). NLU involves mapping of natural language input into useful representations for processing and analyzing, while NLG turns representations into natural language.
In some respect these technologies are old. Computer scientists Robert Mercer and Peter Brown filed their first NLP-related patents in 1972. But with the availability of affordable, high-powered computing platforms, these technologies can now support real-time responses fast enough to achieve comfortable human interaction and be usable in customer engagement. In practical terms, this means the ability to deliver conversational responses in less than 300 milliseconds.
If conversational interfaces are your goal, then you should use NLU and NLG together. However, you can still find utility in using these components separately. Asking an IVR to transfer funds from one account to another only requires NLU, for example. The audio confirmation is what requires NLG.
The following are descriptions of some capabilities available with NLU platforms.
Discern Intent: The intent of a customer can be discerned by the processing of a complete utterance. However, grammatically complete sentences aren't necessarily common in the customer engagement environment, so you'd need to use contextual and metadata elements to manage the conversation to a successful conclusion. When trying to retrieve a stock quote, for example, a customer may ask, "Tell me what Alphabet is trading at right now." The machine captures this information, applies contextual understanding, and responds, "Alphabet is trading at $1055.82." It's discerned the customer's intent to retrieve a stock quote.
Disambiguation: Customer engagements are less prone to uncertainty than some other forms of conversational AI, and so tend not to require disambiguation as often. This is because interactions focus on a group of customer attributes, products, or services and not the universe. This doesn't mean a customer will never say something ambiguous or contradictory, like "Ship my order to my home address, no, on second thought, please send it to my work address." This leaves the machine to disambiguate the confused messaging. In these types of cases, you would want your AI assistant to recite a confirmation: "Just to confirm, you want your order shipped to your work address?" This confirmation step will provide the machine with an affirmation of correctness it can use to prevent further misunderstanding and aid in the machine learning process.
Terminology Extraction: Every industry has some unique terminology. The tech industry is full of it, an example is "port" in telephony. If a customer wants to move a phone number from one cellphone to another, he might say something like, "Please port number 410-555-1212 from T-Mo to the following SIM card 123-4545-4545-9865." With the proper understanding, the machine will extract the word "port" and the SIM value from this sentence so it can execute a robotic process that will move the number to the new SIM card. Similarly, the machine will know that "T-Mo" refers to the carrier T-Mobile, and will initiate contact so the number gets released to the new carrier.
Translation: Trying to use translation in the customer engagement environment typically isn't a good idea. Rather, the best option is to select an NLU/NLG solution that handles languages natively. Translating a customer request from German to English, then processing a response in English and translating it back to German, for example, is a formula for aggravation.
Parsing: A popular example of NLP parsing is in the interpretation of the sentence: "I saw a girl with a telescope." Did I use a telescope to see the girl, or was the girl I saw holding a telescope? Customer engagements rarely require parsing sentences to determine meaning, so coming up with an example is difficult. As such, a parsing algorithm is more of a like-to-have than a must-have NLP feature for customer engagement platforms.