Businesses around the world are increasingly relying on AI and natural language processing (NLP) to help them improve efficiency across the board. How can NLP help a business is not a tough question. By leveraging technology, companies can get information about customers’ needs, behaviors, and preferences faster and more accurately than ever before. NLP is growing exponentially in its significance around the globe, as its market is forecasted to reach $112.28 billion by 2030 at a CAGR of 24.6%.
What does NLP stand for in business?
Natural Language Processing (NLP) is a branch of machine learning related to the recognition, generation, and processing of oral and written human speech. The technology lies at the intersection of artificial intelligence and linguistics.
NLP allows computers to read, interpret, understand, and respond to human language. Processing is generally based on the intelligence of a machine that decrypts a person’s messages into information meaningful to it.
Companies use this technology for a number of tasks including:
- Processing, analysis, and archiving of large documents
- Analysis of customer reviews or call center records
- Running chatbots for automated customer service
- Answers to “who, what, when, and where” questions
- Text classification and extraction.
Who is harnessing NLP?
NLP-based applications surround us at each corner. They include search engines, machine translation, chatbots, virtual assistants, and many more. What’s more, an increasing number of companies are implementing it into their business processes to boost productivity and drive growth.
A stellar example of NLP use cases in business is Google which introduced the LaMDA model with its exceptional conversational skills. The algorithm has proved itself so effective for search queries that some researchers even call it “sentient” – the model shows superior responsiveness and provides the most accurate results.
Source: Unsplash
NLP uses in business across industries
NLP streamlines and automates a wide range of business processes. Additionally, companies can better analyze available data and make informed decisions. Here are some examples of NLP applications in business:
Marketing
Marketing is all about staying informed and addressing the needs of your target audience. Marketers use sentiment analysis, automated summarization, and text generation to know all the ins and outs of the market, customers’ needs, and emotions, as well as generate content and ads.
Business applications of natural language processing are infinite, it’s just a matter of innovative business ideas that can turn the technology into a way profitable for businesses and convenient for customers.
Finance
ChatGPT for NLP helps financial analysts to find the necessary data quickly with automated search and filtering and make prompt decisions. What’s more, the technology is critical for comprehending financial sentiment and forecasting customer pain points.
Education
Another application of NLP in business is in education. NLP can streamline the interaction between learners and teaching systems. The bedrock of conversational AI, NLP in teaching allows it to recognize queries from students and matures with time to deliver responses imitating that of human teachers.
Human resource. NLP is a treasure for HR specialists as it transforms unstructured data into valuable insights to aid decision-making and speed up the hiring process. Identification of fraud and gaps in records and the elimination of human biases are some other tangible benefits here.
Law
To prepare for trial, lawyers have to spend hours reviewing documents in search of materials relevant to the case. NLP technology can automate the process by filtering out multiple documents, saving time, and avoiding human error.
Healthcare
Healthcare systems around the world benefit from NLP analyzing medical records and gaining informed insights. The technology also significantly increases the chances for successful disease detection, providing more personalized services and improving patient care.
For instance, platforms like SyTrue’s NLP Operating System enable doctors to interact with medical records, removing the need to hunt for key observations and allowing them to focus on patient care. There is also Amazon Comprehend Medical, which is a service using NLP to study painful conditions, drugs, and treatment outcomes from patient records, clinical trial reports, and other electronic medical records.
Top 6 benefits of NLP in business
NLP has opened up incredible business opportunities in multiple directions. Let’s expand on the top 6 ways how NLP can help in business today:
Automate tasks
The technology has the power to generate reports, analyze, and extract vast amounts of data without human intervention. Chatbots based on NLP can perform a large number of routine operations that workers are loaded with, and thus enable employees to focus on more strategic initiatives.
Following this strategy, American insurance company Safe-Guard Products automated its claims processing. The implementation of NLP resulted in reducing the time for processing one claim by 75% and increasing the team’s overall performance by 30%.
Extract value from heavy documentation
NLP techniques such as document clustering and topic modeling make it easier to understand a variety of materials in large volumes of documents including company reports, articles, or scientific papers. It can be used to automatically extract relevant information from unstructured text and convert it into structured data that can be easily analyzed. This can significantly reduce the time and effort required for manual data entry and review.
What’s more, the use of AI-powered tools and algorithms can help improve the quality and accuracy of records, ensuring that employees have access to the most up-to-date and reliable information when making decisions.
Source: Unsplash
Understand your market
Another vital benefit is the possibility to study your customer’s sentiment analysis can play a weighty role in business growth. Customer satisfaction has a direct influence on your profits – studies have proved that a customer who had a positive experience with the brand is likely to spend 140% more than one with a negative experience.
Leveraging sentiment analysis also allows for evaluating your competitors’ product offerings and, thus, providing relevant offers to your customers. Eventually, you can boost revenue, provide prompt customer support, decrease backlog, and increase customer satisfaction.
Get actionable insights
No wonder NLP has been considered the key BI trend in 2023. Implementing NLP in business intelligence systems can dramatically increase the accuracy of your forecasts and the quality of your product or service.
Today is the time when the NLP machine learning models become indispensable and allow us to gain insights we had not been able to do efficiently on our own. Its advanced capabilities allow businesses to unlock market insights with lightning speed, eventually driving better decisions and improving enterprise profitability.
Increase employee performance
It all starts with the HR department and its huge volumes of unstructured texts. Its wide number of algorithms enables greater accuracy and speed to key HR business processes while reducing human bias in decision-making.
The absence of human bias as well as human error makes 64% of employees at workplaces trust chatbots more than their managers. NLP-based chatbots can function to employees’ advantage, allowing them to get prompt responses to work-related questions. In addition, NLP tools can boost performance by notifying workers of tasks and events, automating administrative tasks, and saving precious time that employees will spend on more complex tasks.
Enhance search
Regarding the search for relevant documents and FAQs, NLP can improve keyword searches by:
- removing the ambiguity of words based on context (for example, the words “net”, and “crane” have multiple meanings and can lead to the wrong search results);
- comparing synonyms (when the algorithm selects documents containing the word “automobile” if the word “car” is searched);
- accounting for morphological variants (which is critical for considering different language variants and dialects).
Effective scientific data searches based on NLP technologies can greatly facilitate access to the latest research for doctors, lawyers, and other professionals. That’s why tech giant Google has modernized its search query processing with advanced NLP features, which allows it to understand complex queries and address users’ needs.
Source: Unsplash
How are NLP models used in business?
We’ve found out how NLP helps businesses outperform competitors and improve customer experience with minimum effort and maximum profit. All of this becomes possible thanks to extensive NLP models that form the tech foundation for your business growth.
Let’s consider in more detail several NLP models that are increasingly used in various industries.
Machine translation
Deep learning and machine learning algorithms have enabled an automatic translation machine to comprehend, analyze, and generate human language in a way that’s more organic and contextual. The system doesn’t translate every word separately. Instead, it analyzes the meaning of a whole phrase or sentence and identifies punctuation signs, parts of speech, and their relation.
This algorithm is present in online translators and built-in services for various applications. Companies can harness machine translation capabilities to interact with foreign customers and counterparties. A case in point, eBay uses machine translation to enable cross-border trading and connect buyers and sellers from different countries.
Virtual assistants
A virtual assistant has to both analyze the received data and anticipate the conversation as a real interlocutor. In addition, the system should distinguish the main noise with high accuracy.
Text analysis
There are many tools for text analysis that help evaluate texts of different volumes by special criteria. Some are designed for professional use, while others help in training employees, evaluating their work, or generating content.
The model encompasses a number of techniques to benefit businesses in multiple directions. Among them, we can mention data extraction, sentiment, and topic analysis, named entity recognition, and others. They help recognize customer trends, and product performance, refine content recommendation systems, and make informed decisions.
Wrapping up
Natural language processing in business is the core of human-machine communication allowing machines to understand human language and respond in a comprehensible for people way. The technology’s capabilities are increasingly harnessed across industries, which makes it an integral part of process automation, providing superior customer service and ramping up a business on the whole.
Finally, the rising demand for NLP implementation as well as its ever-increasing usage ubiquitously don’t leave a single doubt about the technology’s further spread and innovations in this sphere.
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