The next time you say “Hey Siri” or “Ok Google”, remember this—they are the products of mankind’s undying love to make machines interact like us human beings do. From understanding what machines want from us to what we want from machines—technology has come a long way. Siri and Ok Google are the products of Natural Language Processing or NLP as it is popularly known. NLP has been most commonly understood as a user interface technology, but its applications are wide ranging and applied extensively across domains and industries.
NLP has become a critical technology for enterprises as it unlocks insights and analysis from a huge amount of unstructured data. It is estimated that the total market for NLP software, hardware, and services will hit $22.3 billion by 2025, according to a report by Tractica. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025. Clearly, this is an evolving field with huge potential for businesses. So, what do businesses use them for? Here are some of the most widely used applications of NLP:
Market intelligence: Markets are influenced by how much and what kind of information is exchanged. Hence, NLP is perfect for garnering market intelligence as it facilitates the extraction of structured information from unstructured fields and plug info gaps by unlocking the value in unstructured data through event extraction. It makes unstructured data understandable to a machine. The NLP component in AI helps the computer in understanding the query better and start delivering answers rather than search results. A number of companies are using NLP to garner better market insights. Twiggle, for example, uses NLP and AI to improve search and ultimately convert searches to sales, reducing the number of irrelevant results by up to 20%. FiscalNote uses NLP and Machine Learning to analyze political, legal and regulatory information and claims it can forecast policy outcomes with over 94% accuracy.
Reputation Monitoring: Harvesting social and web data for brand monitoring can be a humongous task. In order to help track their brand reputation in the market, enterprises have turned to Sentiment Analysis and Opinion Mining to capture the emotions behind customer voices. By using sentiment analysis, businesses can analyze and comprehend customer responses published on social media platforms and act accordingly to improve customer experience. These responses could be in the form of product reviews, issues, topics, online & social media content relating to the business. NetBase applies NLP technologies to capture the sentiments of the audience rather than just focus on the keywords, as that will provide a better understanding of the audience preferences and behaviors.
Customer Service: Many customer issues can easily be resolved with an automated interface powered by AI, which uses NLP to recognize user intent. It could aid in translating the caller’s speech into a text message which could then be easily analyzed by the engineer and even use speech separation where it will identify each voice to the corresponding speaker and answer the calls separately and detect if the customer is unhappy. Chatbots and automated online assistants are also a natural application of NLP. Companies like Peerius, NOSTO and RichRelevance are using ML and NLP to enhance customers’ shopping journey and provide recommendations via FB remarketing, pop-ups etc.
Ad placement: Businesses earlier placed a lot of emphasis on demographics and psychographics and did detailed market research to find that out. Today, they can use keyword matching to do the same thing. NLP helps to target and place the right ads at the right time for the right kind of audience that your enterprise is looking at. It helps by intelligently sourcing the right keywords in the text and match them to hit the right kind of customers. Harley Davidson credits 40% of its sales in New York City to Albert, the ML platform from Adgorithms, by gaining insight into advertising effectiveness.
Businesses are turning to NLP to understand their customer better by analyzing the countless unstructured data available offline and online. It provides the ability to automatically extract dozens of entity types such as concepts and named entities, while tracking sentiment and perception. However, Most NLP methods are statistical and can’t go beyond without context or semantics. They can’t simulate a behavior they haven’t seen before—it will be a while before that kind of connection is established. But, NLP is continuously evolving and so will its business uses.