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We are suffering from an overwhelming deluge of data that’s accelerating all the time. Most of this data is unstructured, composed of text, speech, images and videos coming from many different sources.
Text is one of the most important pieces of unstructured data for businesses and comes from external and internal channels. The way in which they can take advantage of it is to use Natural Language Processing (NLP).
In simple words, NLP consists of a blend of language, machine learning and artificial intelligence. It builds a technology that allows us to interact with machines in the same way as we conduct a human-to-human conversation.
Human language is different from programming language in that it can be used in many different ways and dialects, abbreviations, slang and nuances make it difficult for computers to understand. That’s where NLP can help.
NLP has multiple applications in business today because it can extract valuable insights from unstructured data. Natural language processing examples include sentiment analysis and spell-check. Spam filters and auto-complete are also examples of NLP people use every day. Business leaders should explore the following business uses for NLP.
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NLP can bring improvements in the recruitment of candidates. Businesses can choose from a wide range of available software, test it and decide whether it meets their requirements.
For example, semantic search of resumes works far better to find the best fit than using matching keywords. As well as saving time, NLP can even help to remove any unconscious bias and increase diversity when hiring candidates.
Natural language processing enhanced automated interviews allow hiring managers to determine the fitness of a candidate for the company. Tools analyze speech patterns, words, facial expressions and other minor details that might otherwise be missed.
NLP is being used in chatbots to engage with candidates and provide real-time answers and feedback. Mya is an example of a recruiting assistant that engages candidates throughout the recruiting lifestyle.
A critical part of marketing is to understand the emotions of customers. NLP can help to derive this type of insight from textual data. It is a great tool to analyze responses to business messages used on social media platforms.
It can analyze the emotional state of a person engaging with or commenting on posts. A combination of NLP and statistics is used to assign values to text, such as positive, negative or neutral. It is even possible to identify the underlying mood of consumers (happy, sad, angry or annoyed).
Whether it’s identifying social mentions about a brand, uncovering negative reviews, or knowing what people say about a business, using NLP makes it possible to build campaigns and drive strategy that better address customer needs.
Businesses need to stay up to date with changing standards and industry trends. Knowing what competitors are doing, and on a larger scale, what an industry is doing overall is important in developing effective business strategies.
This insight is often buried in infographics, market reports, and news articles as well as on company websites. NLP helps businesses to make sense of all this information at scale and quickly which can have implications on business decisions. For example, if news of a company merger is uncovered in time, it could affect trading decisions.
NLP is widely used in financial marketing, for example, where market conditions shift daily. Analysts must have relevant, real-time content and NLP provides this as it can extract information more accurately and efficiently. This makes timely and informed decision making possible.
Businesses want to keep their customers happy and NLP provides multiple ways of enhancing customer service. With the high volumes of customer interaction, it’s important to be able to prioritize which tasks to act upon first. Using voice-to-text, NLP and machine learning offers insights into which customer inquiries are most important.
Chatbots offer virtual assistance and can handle low-priority, high turnover tasks. They offer real-time, around-the-clock assistance to customers for simple problems and save time, effort and cost. They can attend to multiple calls, fetch queries and respond to customers or transfer a call they can’t deal with to the correct department.
NLP enables businesses to target the right audience and place advertisements in the right place at the right time. NLP makes it possible to analyze social media posts, purchase behavior etc. to target potential buyers.
It is not only possible to identify possible customers but segment them to provide them with the best product options or services for them. A business may even be able to identify new customers they hadn’t even considered targeting before.
Advertising costs plenty of money and it helps if marketers are able to optimize how they spend their advertising budget.
Another way that NLP can grow businesses is by improving their content marketing strategy. Content strategy tools powered by NLP and AI can analyze articles as they are written and give detailed instructions to writers so the content they write is of a higher quality. Software tools can also analyze recent stories and current events so writers can instantly create relevant content.
Today it is possible to create content for the search engines that is also better for users because the tenets of human readability and machine readability are moving closer together.
Short, simple sentences, one idea per sentence and closely connecting questions to answers are all tenets of both. It’s best to be specific first and then get to the nuance, avoid marketing speak and use subheadings and bullet points to break up the text.
These are just a few of the applications of Natural Language Processing in business. Regulatory compliance, biometrics, process automation and data visualization are some other applications we will see more of in the future. NLP is a key because language is one of the main indicators of intelligence. It utilizes techniques to provide meaningful information for businesses that can transform the way they operate.
Author – Edward Huskin
Edward is a freelance AI and Big Data consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights. You can reach him at his LinkedIn profile.