XM Knowledge Base
Author: Adam Bunker
Subject Matter Expert: Shannon Thacker
What is Natural Language Processing?
In computer science, Natural Language Processing (NLP) is the ability of artificial intelligence (AI) products and services to add context and derive meaning from human speech or written text, using statistical methods and machine learning algorithms.
While more basic speech-to-text software can transcribe the things we say into the written word, things start and stop there without the addition of computational linguistics and NLP. Natural Language Processing goes one step further by being able to parse tricky terminology and phrasing, and extract more abstract qualities – like sentiment – from the message.
The way we speak and write is fascinatingly complex, but our brains are great at understanding the meaning and intent behind someone’s words – even if things are spelled wrong, come amid a flurry of ‘um‘s and ‘ah‘s, or are delivered in a roundabout way.
Natural Language Processing software can mimic the steps our brains naturally take to discern meaning and context. That might mean analyzing the content of a contact center call and offering real-time prompts, or it might mean scouring social media for valuable customer insight that less intelligent tools may miss.
Free eBook: Understanding AI: Your real-world CX playbook
How does Natural Language Processing work?
Whenever Natural Language Processing attempts to find meaning in text or audio, there are a number of statistical methods, machine learning processes, and language detection tasks happening at once. Here are some of the common ones:
Speech-to-text
This is where human speech is converted into text. While Natural Language Processing isn’t always required in this step, it helps with unraveling the disorganized way in which we sometimes speak. Note: NLP also works with text-first messages, not just speech.
Tagging and categorizing
As part of speech tagging, machine learning detects natural language to sort words into nouns, verbs, etc. This is useful for words that can have several different meanings depending on their use in a sentence. This semantic analysis, sometimes called word sense disambiguation, is used to determine the meaning of a sentence.
Name and entity recognition
These NLP tasks break out things like people’s names, place names, or brands. A process called ‘coreference resolution’ is then used to tag instances where two words refer to the same thing, like ‘Tom/He’ or ‘Car/Volvo’ – or to understand metaphors.
Sentiment analysis
The program will then use Natural Language Understanding and deep learning models to attach emotions and overall positive/negative sentiment to what’s being said.
AI Jargon buster
Artificial Intelligence (AI)
While AI’s scope is incredibly wide-reaching, the term describes computerized systems that can perform seemingly human functions. ‘AI’ normally suggests a tool with a perceived understanding of context and reasoning beyond purely mathematical calculation – even if its outcomes are usually based on pattern recognition at their core.
Machine Learning (ML)
Simply put, ‘machine learning’ describes a brand of artificial intelligence that uses algorithms to self-improve over time. An AI program with machine learning capabilities can use the data it generates to fine-tune and improve that data collection and analysis in the future.
Generative AI
‘Gen-AI’ represents a cutting-edge subset of artificial intelligence (AI) that focuses on creating content or data that appears to be generated by humans, even though it’s produced by computer algorithms.
Artificial neural networks
Computation models inspired by the human brain, consisting of interconnected nodes that process information.
Deep learning
A subset of machine learning where neural networks with many layers enable automatic learning from data.
Supervised and unsupervised learning
The former is an ML approach where models are trained on pre-labeled data. The latter is an approach for identifying patterns in unstructured data (without pre-existing labels).
Generative pre-trained transformers (GPT)
This is the name given to an AI model trained on large amounts of data, able to generate human-like text, images, and even audio. ChatGPT is probably the best known example here.
Large Language Model (LLM)
LLMs are similar to GPTs but are specifically designed for natural language tasks.
Computational Linguistics
Natural Language Processing is a subset of a broader type of computer science: the analysis of language. As an umbrella term, this is what we call computational linguistics.
Language is inherently complicated. Every language has its own set of rules, but those rules shift and bend all the time – especially in spoken language, where sentences don’t often follow a usual grammatical structure.
Computational linguistics is the science of understanding language in general, while Natural Language Processing goes a step further by getting to grips with all those nuances inherent to the way people really talk.
Natural Language Generation
Natural Language Generation, otherwise known as NLG, utilizes Natural Language Processing to produce written or spoken language from structured and unstructured data. The most common methods of NLG are extractive and abstractive.
– Extractive NLG
An extractive approach takes a large body of text, pulls out sentences that are most representative of key points, and links them together to generate a summary of the larger text.
– Abstractive NLG
An abstractive approach creates novel text by identifying key concepts and then generating new sentences or phrases that attempt to capture the key points of a larger body of text.
Why is Natural Language Processing important?
About 95% of customer data is found in the form of unstructured text – in emails, survey write-in answers, X posts, online reviews, comments in forums, and more.
Reading through all of this text is next to impossible. Assuming that the average person can process 50 items of unstructured data an hour, it would take nearly seven years for one person to read through one million items. If all those data points represented a huge volume of customer queries, social media posts about emerging issues, or other kinds of customer feedback, you’d never be able to keep up.
So how do you understand and learn from all of this feedback?
Natural Language Processing automates the reading of text using sophisticated speech recognition and human language algorithms. NLP engines are fast, consistent, and programmable, and can identify words and grammar to find meaning in large amounts of text.
Understanding the context behind human language
Most importantly: human language is complicated. And that means that computers need to work harder than we do to ensure that machine translation, speech recognition, and text data make sense.
As an example, can you spot the difference in sentiment between these two sentences:
“The service was outstanding.”
“I have an outstanding balance.”
You probably know, instinctively, that the first one is positive and the second one is a potential issue, even though they both contain the word outstanding at their core. This ability is called word sense disambiguation.
But without Natural Language Processing, a software program wouldn’t see the difference; it would miss the meaning in the messaging here, aggravating customers and potentially losing business in the process. So there’s huge importance in being able to understand and react to human language.
What are the benefits of natural language processing?
Implementing software that can take advantage of machine learning methods can have huge benefits for businesses looking to streamline their customer support systems. Here are a few ways Natural Language Processing (NLP) can lighten the load:
Process automation
Natural Language Processing can take an influx of data from a huge range of channels and organize it into actionable insight in a fraction of the time it would take a human. Qualtrics, for instance, can transcribe up to 1,000 audio hours of speech in just 1 hour.
Moreover, integrated software like this can handle the time-consuming task of tracking customer sentiment across every touchpoint and provide insight in an instant. In call centers, NLP allows automation of time-consuming tasks like post-call reporting and compliance management screening, freeing up agents to do what they do best.
Better self-service options
Chatbots are a great way to allow customers to self-serve where possible, but if the bot in question can’t follow the conversation, you’ll only end up with angry customers.
Natural Language Processing can make bots infinitely more capable, allowing them to speak with human-level understanding in any language, respond appropriately to positive or negative sentiment, and even derive meaning from emojis.
All that makes self-service a more compelling option for customers who’d prefer to get their issues resolved without having to speak to anyone on the phone – which, in turn, frees human agents up for more pressing customer issues.
Curated customer service
Customer interactions aren’t always about a single topic. Thankfully, Natural Language Processing can identify all topics and subtopics within a single interaction, with ‘root cause’ analysis that drives actionability. The same goes for different customer channels.
A fully-integrated experience management tool with Natural Language Processing can scour everything from emails and phone calls to reviews on third-party websites, and learn where customers are finding friction – both on an individual basis and at scale – by understanding language.
Better call center management
For call center managers, a tool like Qualtrics® Frontline Care can listen to customer service calls, analyze what’s being said on both sides, and automatically score an agent’s performance after every call.
If they’re sticking to the script and customers are happy with their experience, you can use that information to celebrate wins. If not, the software will recommend actions to help your agents develop their skills.
Business benefits
When you’re automating customer service-related tasks through natural language processing, you’re collecting increasingly extensive human language datasets all the time, which makes it easier to analyze trends and perform historical analysis.
The speed of cross-channel text and call analysis also means you can act quicker than ever to close experience gaps. Real-time data can help fine-tune many aspects of the business, whether it’s frontline staff in need of support, making sure managers are using inclusive language, or scanning for sentiment on a new ad campaign.
Natural language processing (NLP) use case examples
Anywhere you deploy natural language processing algorithms, you’re improving the scale, accuracy and efficiency at which you can handle customer-related issues and inquiries. That’s because you’ll be understanding human language at the volume and speed capabilities inherent to AI.
Here are a few common NLP tasks where the technology really shines:
Chatbots
Natural Language Understanding takes chatbots from unintelligent, pre-written tools with baked-in responses to tools that can authentically respond to customer queries with a level of real intelligence. With NLP onboard, chatbots are able to use sentiment analysis to understand and extract difficult concepts like emotion and intent from messages, and respond in kind.
The real benefit here is that your chatbot will pick up on customer frustration and empathize – instead of parroting responses that seem tonally at odds with the conversation.
Live call analysis
When human agents are dealing with tricky customer calls, any extra help they can get is invaluable. AI tools imbued with Natural Language Processing can detect customer frustrations, pair that information with customer history data, and offer real-time prompts that help the agent demonstrate empathy and understanding.
Simple statements like “I know this must be frustrating after the last time” are hugely effective, but agents can sometimes be too dedicated to script compliance to offer them up. Natural language tools, then, can act as an empathetic sense-checker – providing a way to mitigate customer frustration.
Post-call writeups
Monotonous, time-consuming contact center tasks are prime candidates for becoming NLP tasks. If an AI tool has sentiment analysis and an understanding of human language, it can interpret everything that happened on a call and turn that into an accurate post-call write up.
The key benefit here isn’t just that AI can handle a typically unglamorous job. It’s that in doing so, it frees up human agents to tackle more pressing issues and do what they do best: human communication. In that sense, NLP can be a powerful enabler for prioritization within the contact center.
Third-party listening
Customers don’t just speak to you on your owned channels. They’re also communicating their opinions and issues to and about you on social media channels and third-party review websites – like Google Reviews, for example.
Social listening tools powered by Natural Language Processing have the ability to scour these external channels and touchpoints, collate customer feedback and – crucially – understand what’s being said. What makes this especially useful is that AI can do all that 24/7, across every touchpoint. That means you’ll always have an up-to-the-minute read on customer sentiment, which means you’ll be able to spot issues and experience gaps right as they arise.
The best customer experience management tools use NLP to spot trends from customer discourse – like an emerging issue with a website, app or product – flag the problem, and help teams course correct before things escalate into a reputational risk. In that way, AI tools powered by natural language processing can turn the contact center into the business’ nerve center for real-time product insight.
Machine translation
Customer queries, reviews and complaints are likely to be coming your way in dozens of languages. Natural language processing doesn’t discriminate; the best AI-powered contact center software can treat every interaction the same, regardless of language. Machine translation sees all languages as the same kind of data, and is capable of understanding sentiment, emotion and effort on a global scale.
AI and NLP vs human agents?
People often think that improvements in artificial intelligence sound the death knell for humans in the workplace, but when it comes to the customer experience and the contact center, that’s really not the case. Instead, AI’s role in these situations is to help human beings do their best work, understand customers on a more personal level, and intercept issues before they have a chance to get out of hand.
The better AI can understand human language, the more of an aid it is to human team members. It can help them prioritize important calls, recall important customer history information, deliver empathetic acknowledgements, and manage the more monotonous parts of the job that have traditionally taken up agents’ time.
But here’s the important thing to remember: as powerful as speech recognition, natural language processing algorithms, and sentiment analysis tools are, they’ll always be second string to the things they’re trying to mimic: human beings.
In other words? Natural language processing tools are an aid for humans, not their replacement.
How to bring NLP into your business
The best way to make use of natural language processing and machine learning in your business is to implement a software suite designed to take the complex data those functions work with and turn it into easy to interpret actions.
Experience management software like Qualtrics makes statistical natural language processing at scale useful to business managers by transforming vast quantities of customer service data and making it useful – with immediate results.
We’ve developed a proprietary natural language processing engine that uses both linguistic and statistical algorithms. This hybrid framework makes the technology straightforward to use, with a high degree of accuracy when parsing and interpreting the linguistic and semantic information in text.
Qualtrics’ technology uses a six-step, workflow-like process to identify and understand phrases, grammar, and the relationships among words in a way that’s comparable to the way people assign meaning to things that they read.
When paired with our sentiment analysis techniques, Qualtrics’ natural language processing powers the most accurate, sophisticated text analytics solution available.
Free eBook: Understanding AI: Your real-world CX playbook