Natural Language Processing (NLP) is a growing trend in IT and business, proving that AI alone isn’t enough. In fact, recent research reveals that the global Natural Language Processing market size is expected to grow from $11.6 billion USD in 2020 to $35.1 billion USD by 2026. This growth is for good reason. AI-powered interfaces augmented with NLP, like chatbots, can be more accessible to a greater number of people than a simple search engine tool alone.
Understanding the business uses of Natural Language Processing can help your organization realize all of the benefits of AI and strategically coordinate goals at every level. In this post, we will cover everything you need to know about Natural Language Processing, and how it can take AI to the next level for widespread use in your organization.
What is NLP
Natural Language Processing is an engine that analyzes user input, aims to detect user intent, and identifies the relevant answer, knowledge, or automated process. NLP is a subfield of computer science and artificial intelligence focusing on language interactions between humans and computers. This is helpful in providing contextualized answers and communicating the proper information to people no matter where they are in the world or which language they prefer. This will also take into account colloquialisms and other important factors when someone is searching or communicating with AI incorporated with ITSM.
AI without NLP or machine learning, ML, (which refines answers over time by analyzing search terms, inputs, and data) may be able to regurgitate programmed data, but only when specific terms are entered.
For example, consider if you are searching in a self-service portal for knowledge articles about how to reset your cell phone. Without NLP, you may be able to pull up these articles with specific search terms like “cell phone reset” or “reset cell phone”. But, unless the AI is programmed with additional parameters it will not pull up articles for “iPhone reset” or “mobile phone not working”. With NLP, however, especially when equipped with machine learning, the AI can analyze and understand your sentiment or intent. So if you refer to a cell phone as a mobile phone, or if your regional language has another term for reset, you can still find the information for which you are searching.
Why is NLP Important for Business?
I touched on the very surface use of NLP in the point above (searching for knowledge articles) but the business uses of Natural Language Processing and the benefits go beyond simple search results.
The most important aspect of NLP is that it helps AI interfaces, such as chatbots and virtual assistants, better understand human language so they can converse with humans more efficiently and more accurately. For example, let’s say that you decide to order an “8-inch Amazon Fire” tablet using your voice assistant. Without NLP, your assistant might not recognize your request and could even result something completely out of context, such as “setting an 8-in surface of the Amazon forest on fire!”
When it comes to increasing user engagement with self-service or self-help platforms, including with the addition of a chatbot or virtual assistant, you need to ensure that customers will feel comfortable engaging and conversing with these technologies. That starts with these technologies actually understanding what the end user is asking for, in the user’s native language, and providing accurate responses. After all, nothing is more frustrating than trying to decode pre-programmed terminology to access help.
Using AI equipped with NLP and ML as part of an employee self-service portal connected to an intelligent knowledge management database and service management solution, businesses can expect to see major time savings. As they say, time is money. When low value-added activities are automated, agents can focus on more complex problems. And when employees spend less time waiting for resolutions to their technical issues, the external customers can be the focus of their time.
In short, NLP offers a way to save time and money for agents, employees, and really anyone who utilizes the self-service portal.
4 Examples and Use Cases of Business Uses for NLP
The benefits of NLP extend beyond the IT department. The following examples are just a few of many possible business uses for NLP:
1. Connect Remote Workers from Various Regions to Necessary Knowledge in Any Department
One of the main uses for NLP is through an employee self-service portal. This is especially helpful when you have a team of remote workers or multiple offices spread throughout geographical regions who may have different ways of phrasing the same requests, but who also need access to helpful information.
NLP in a self-service portal doesn’t necessarily need to be tied to connecting employees with the IT service desk. It can help connect remote workers to answers they might need from each other by creating knowledge articles connected to their roles and stored within an intelligent knowledge management database. It can also connect employees to other internal service departments, like Human Resources, facilities, or marketing. This can help employees feel empowered to solve their own problems – be they IT-related or role-specific – even in a remote work landscape.
2. End-User Customer Support Automation
Employee support isn’t the only area where NLP can help connect people to knowledge. Externally facing customer support portals can also utilize NLP to provide personalized service to customers, which ultimately supports and contributes to the greater business goals.
As an example of a consumer chatbot with NLP, consider the Amazon customer service bot. This bot is used worldwide to process refunds, request the shipping status, and ask when new items will be in stock -- but in order to provide service to the wide range of customers who might be using the bot, Amazon uses NLP technology. Without NLP, customers in the U.S. may request shipping status by saying something like “where’s my package?” while customers in another part of the world might type something closer to “post delivery date” and both customers would be directed to a different area, rather than the bot recognizing that they are asking the same question.
By providing contextualized answers based on the region and dialect, customers can expect more personalized, one-stop-shop service through the chatbot.
3. Security Authentication
Outside of knowledge-related chatbot and self-service portals, NLP can help with security authentication. This sounds like something from a sci-fi movie, but in reality, NLP can help with security authentication by identifying contextual data for each user.
How does this work? Through NLP-powered question generation.
For example, software developers can create algorithms that compose unique security questions that only specific users can answer. This will access specific information for each user, and store the data related to that information. So, let’s say you indicated that your favorite film is Dirty Dancing and you use that as a security answer. Using NLP-powered question generation, you could use quotes from the film as additional security questions. For example, “Name a quote from your favorite film.” Or maybe something more specific like “Who puts baby in a corner?”
For a more simplistic example, NLP can help detect the way you answer security questions and using machine learning can together help decipher if you are actually the person requesting access.
4. Feedback Collection and Report Generation
Collecting feedback from users in different regions can be a tough task, especially when you want to leave a few areas open for manual input rather than relying on drop-down menus. But with NLP, you can more easily decipher and refine that feedback using automation. For example, imagine you have a chatbot that asks users to give feedback at the end of an interaction with your service desk. As part of NLP, sentiment analysis can help decipher the intent of the answer, and categorize these answers as positive, negative, or neutral.
After the feedback and data is collected and accurately stored, NLP can help analyze the correct data needed to generate reports, regardless of the way the feedback was provided or phrased. It all comes back to using the power of the technology of NLP to teach the artificial intelligence and machine learning how to decipher and decode each users’ intentions using sentiment analysis, whether or not it is a user creating feedback.
Time to Incorporate Bots in Business
Anytime you want to consider incorporating a new chatbot, AI, NLP, machine learning, or automation technology into business, it is a good idea to think about the processes that can easily be automated to start. Once you have considered which business units will benefit from automation, AI, ML, and NLP the most, you can implement new technology to see the biggest benefits for the business.
To learn how EasyVista can help you achieve automation success with AI, get a demo today.