How NLP(Natural Language Processing) is Transforming Business
A few years ago we use to type keywords into google search to get effective results. Today we have the comfort of vocally seeking help like using the technology Assistant of Google called as Google Assistant, Alexa and Siri are voice assistants that process all our voice commands at the drop of the hat. Every time you ask Siri or Alexa or Google assistant for directions, a complex code is activated. The code let these applications understand our query, search the information we are looking for, and respond to us in a language that we understand. This is a recent development. Until now, we have to learn computers language to interact with them the way they understand rather than us. But now the computers are learning the language of ours. The ability of machines to use computational linguistics which is a combination of AI, Machine Learning and Linguistics to understand us when we speak/write to them as if they were other people is called Natural Language Processing.
Natural Language Processing (NLP) is a paradigm shift in how humans interface with systems. One of the most pragmatic tech trends, Natural Language Processing, has multiple applications in business today. Some of the most important applications of NLP in business today are:
1. Sentiment Analysis: Sentiment analysis is an application of NLP for extracting sentiment (categorizing it into positive, negative, neutral) from a chunk of text. It helps to analyze the attitude and emotional state of the writer (person commenting/engaging with posts). By knowing the trending opinion and having a real-time view of the customer’s pulse is a critical element of brand marketing. This application of NLP helps business organizations gain insights on consumers and do a competitive comparisons and make necessary adjustments in the business strategy.
2. Chatbots: A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, and mobile apps or through the telephone. Chat bots let customers easily interact with the brand through stimulated conversations so chat bots are the future of brand engagement.
Engaged customers are more likely to proceed to the bottom of your sales funnel faster.
For example, on a messenger app, a chat bot can initiate a conversation to promote an offer or update your customers about a latest product. In a nutshell, chat bots are offering personalized assistance to the customers by understanding and comprehending natural human language, and are fully capable of replying with similar natural undertone.
3. Customer Service: Ensuring customer loyalty by keeping them content and happy is the supreme challenge and responsibility of every business organization. High volumes of consumer interaction creates the need for a critical capability to prioritize which tasks to act upon first. Using voice to text, NLP and machine learning can more quickly deliver insights to the most important customer inquiries. NLP also aid in translating the caller’s speech into a text message which could be easily analyzed by the engineer.
4. Information Extraction: Many of the business or product decisions are driven by information found in the news, on social media and numerous Internet platforms. The majority of such content is present in the form of text, info graphics, and images. The main application of natural language processing is in taking these texts, analyzing and extracting the related information in one of the structured formats that can be used in a decision making process. For example, news of a merger between companies can have a big impact on trading decisions. The speed at which the merger, players, prices, can be incorporated into a trading algorithm can have profit implications in millions of dollars.
5. Managing the Advertisement Funnel: Based on what consumer needs and is looking for Natural Language Processing can intelligently target and place advertisements in the right place at the right time and for the right audience. Reaching out to the right patron of a product is the ultimate goal for any business. NLP matches the right keywords in the text and helps to hit the right customers. Keyword matching is the simple task of NLP yet highly remunerative for businesses.
6. Market Intelligence: Business markets are influenced and impacted by market knowledge and information exchange between various organizations, stakeholders, governments and regulatory bodies. It is vital to stay up to date with the industry trends and changing standards. NLP is a useful technology to track and monitor the market intelligence reports for and extract the necessary information for businesses to build new strategies. Widely used in financial marketing, NLP gives exhaustive insights into employment changes and status of the market, tender delays, and closings, or extracting information from large repositories.
7. Recruitment: Semantic search of resumes to filter the best fit is far more powerful than keyword match. NLP allows for easy, automated resume screening and processing that reduces hiring timelines and saves hours spent on manual testing. Automated pre-qualification with the help of chat bots allows for better candidate experience by providing immediate, constant and consistent real-time solutions, feedback and comments. AI and NIP-enhanced automated interviews improve the candidate's fit in the company by analyzing their words, speech patterns, and facial expressions, and help detect minor details one might have otherwise missed.
8. Media and Publishing: Publishing companies deliver news and content after aggregating and curating from a variety of sources. The news aggregator applications not only provides a customized news feed, but also compares its coverage over different types of media, and systematizes the information so that the user can make up his own mind regarding the particular topic. The process of aggregation is far more accurate for the reader’s preferences with NLP-based selection.