6 Enterprise Natural Language Processing Use Cases

One of the most prevalent forms of AI is Natural Language Processing (NLP). NLP helps computers understand language like humans do. According to MarketsandMarkets, its market size is expected to more than triple, from $15.7 billion in 2022 to $49.4 billion in 2027. Let’s get into it.

NLP concerns all forms of spoken and written language, as well as sign language and body language. Thanks to NLP, computers can better understand and respond to users’ needs.

NLP uses unstructured data such as recordings from a call center, social media posts, news articles, and customer feedback. Unstructured data makes up about 80% of all data available to enterprises; however, it is more difficult for businesses to leverage than unstructured data. NLP is changing that, as it can process large volumes of unstructured data.

6 NLP Use Cases

Here are 6 popular use cases for NLP that illustrate its power and potential:


1. Customer Service Interactions: Whether phone calls, emails, or online chats, customer service interactions can be analyzed with NLP to determine sentiment and customer needs, as well as analyze the quality of existing customer service. Evalueserve used speech analytics, a type of NLP, to improve the customer experience with a global payments company’s call center. Read the case study here to learn more.


2. Chatbots: NLP is often used to create chatbots that analyze and respond to customer and prospect requests. Right now, NLP is only advanced enough to take the chats so far but still brings valuable time savings for the customer service team with that contribution. Chatbots can even be used for HR; for example, to communicate with employees about their job satisfaction.


3. Translation Software: NLP is a critical part of translation software because language is complicated and not as logical as if a machine created a language. So, it logically follows that, for translation software to work well, NLP needs to be used to help machines communicate in a more nuanced, human-like way.


4. Sentiment Analysis: In this instance, NLP analyzes content, whether that be social media, reviews, or some other form of customer feedback – to determine how customers and the public are engaging with the brand, as well as its competition.


5. Intelligent Document Analysis: Here, NLP is used to create summaries of longer documents or articles, saving businesspeople research time. It can also help in the area of risk management.


6. Document Search and Match: This is useful for hiring and talent acquisition. Document search and match involves a computer sifting through a large, unstructured dataset to identify what matches a set of criteria. It’s often used to go through resumes of potential employees, quickly determining who meets the primary job requirements and who does not.


NLP has countless more enterprise use cases that are transformative in their own right. Learn more about NLP and how it can help your business by reaching out to one of our experts today.

Leah Moore
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