Natural Language Processing (NLP), a branch of artificial intelligence, focuses on human and machine communication. To put it simply, it is the science of extracting meaning from text data and enabling human-machine interaction in the simplest way possible. The goal of NLP is to teach a machine to understand and respond in human language.
It’s incredibly simple to demonstrate your return on investment with an NLP investment and see returns quickly. The Natural Language Processing market is anticipated to reach US$ 48.46 Bn in 2026, which is quite encouraging for businesses considering NLP investments. Additionally, it is anticipated that by 2024, a quarter of all businesses would have included NLP in some way in their customer care.
How NLP Works
Humans talk in a hundred distinct dialects and languages, each with its own grammar and syntax. We also employ slang, omit punctuation, and abbreviate words in both spoken and written communication. It is pretty hectic and unorganized, just like us. Our internet communication follows the same format.
In contrast, computers communicate in a rather organized way using a lot of zeros and ones. Additionally, NLP enables computers to learn how to listen, comprehend, and react in a human language. NLP employs a variety of methods to comprehend the subtleties of human communication.
NLP is a rapidly growing field in the world of technology and data science. It involves the use of algorithms and machine learning models to analyze, understand and generate human language. It has numerous applications, including customer service, market research, and sentiment analysis.
Use Cases for NLP
One application where chatbots are used is in customer support. They have recently raised a lot of controversy. As conversation systems, Facebook and WhatsApp each have more than 1 billion users. Additionally, businesses are being impacted as more individuals spend their time communicating online. Most customers now communicate with brands via chat. Using NLP and NLU (natural language understanding), a subset of NLP, the conversation may be automated rather than adding extra staff to handle these incoming consumer demands.
In this manner, chatbots and NLP may significantly reduce your costs while automating your customer support. Scalability of your customer service is another benefit of implementing chatbot automation with NLP. A chatbot can manage more than a million clients if one customer care representative can handle one phone call and five chats at once. Additionally, automated chatbots improve your clients’ overall experience since they respond to their inquiries quickly, accurately, and round the clock.
Marketing and Advertising
Marketers are swiftly adjusting to the new digital environment, where before audience psychographics and demographics were their primary concerns. NLP assists businesses in developing individualized and incredibly focused marketing campaigns by analyzing digital footprints including social media impressions, emails, search terms, and browser behavior. Social prospecting is made possible by NLP technology, which can sort through brand discussions on social media and identify potential customers.
To find expressions of interest and profiles of persons who fit the customer’s criteria, NLP often utilizes keyword matching. However, the most recent NLP technology also considers the context in which the term was used. Customers are shown material that they are actually interested in to drastically increase the number of conversions.
Online Reputation and Sentiment Analysis
Online Reputation Management refers to the process of monitoring, managing, and influencing an individual or a company’s reputation on the internet. With the increasing use of the internet, social media platforms, and customer review websites, it is essential to maintain a positive online reputation. A negative online reputation can impact the credibility and trust of a brand and potentially lead to a loss in business. Therefore, companies and individuals engage in online reputation management to control the narrative about their brand and to ensure that their online presence accurately reflects their values and offerings.
Sentiment Analysis, also known as Opinion Mining, is a process of using Natural Language Processing and Machine Learning algorithms to identify and extract subjective information from text data. It is used in online reputation management to analyze customer opinions, feedback, and reviews about a brand or a product. Sentiment analysis helps businesses to understand the overall tone and emotion behind customer feedback and can be used to improve customer satisfaction, product quality, and brand reputation. By analyzing large amounts of data, sentiment analysis can provide insights into customer preferences, opinions, and trends, enabling businesses to make informed decisions and improve their online reputation.
Why NLP Works
In today’s business world, it’s crucial for companies to understand their customers’ needs, preferences and opinions. NLP provides businesses with the tools to do just that. Here are some statistics to show the impact of NLP on customer understanding:
- 92% of customers feel more satisfied when they receive a quick and accurate response from customer service. (Zendesk)
- NLP-powered customer service chatbots can handle up to 80% of common customer questions, freeing up time for human agents to handle more complex inquiries. (Forbes)
- 68% of customers prefer using chatbots for quick answers to simple questions, instead of waiting on hold for a customer service representative. (Oracle)
- NLP sentiment analysis can accurately determine the sentiment of customer feedback with an accuracy rate of up to 85%. (Stanford University)
- Companies using NLP in market research see an increase in customer insights by 40%, and a reduction in research time by 60%. (Gartner)
In conclusion, NLP provides a powerful tool for companies to better understand their customers. By analyzing customer interactions, sentiment, and feedback, businesses can gain valuable insights into customer behavior, preferences, and opinions. This, in turn, allows companies to improve customer satisfaction, engagement, and loyalty.