Natural Language Processing (NLP) is the field of Artificial Intelligence that deals with computers understanding the way humans communicate in the real world, in a natural language. Moving from humans interpreting the language of computers, this field focuses on the algorithms a computer needs to understand the linguistics, structure, content, and meaning of a human language. It enables humans and machines to talk to each other without the need for any special skill. NLP is everywhere, mostly unnoticed and taken for granted as we don’t realise how often we make use of this technology in our daily lives. From using word predictions while texting, to interacting with a self-checkout machine at the grocery store or talking to the customer service online/via call, there are hundreds of algorithms running behind the scenes to give us a seamless experience. We will touch base with a few business applications that benefit from using improved NLP algorithms that help businesses retain customers by understanding their behaviour, feedback, and general contentment with them
1) Sentiment Analysis (SI):
Perhaps the most widespread and used application of NLP, sentiment analysis is a subset of text/speech analytics that deals with understanding the opinion the person who authored it. SI helps in reading the contextual sentiment and the intention behind a piece of text. This can be used to find out brand or product opinions, the degree of satisfaction or perception in context of a specific product or a feedback. Such feedback can target improvements in product development, customer support, advertising, marketing and social campaigning. For companies that provide products and services, analysing customer feedback in the form of surveys or reviews can tailor their future strategies. Business can find the link between customer happiness and customer retention and use that to target high risk customers with better offers/products to reduce churn. SI can be used to increase acquisition rates by targeting potential customers with contextual marketing. This can be done by analysing public social media content to find out if individuals are searching for products similar to what a business is offering, and then sending personalised marketing to them. Sentiment analysis can also be used to find customer satisfaction with the competitor’s products and services and altering the brand image appropriately to attract customers.
2) Semantic Analysis and information retrieval:
A business can have vast unorganised textual data that take a lot of resources to analyse. NLP can be used to understand the semantics (context) in the text a company handles. Examples include real time analysis of customer service, parsing of feedback emails or surveys, or developing speech or text software to hold a conversation with an employee or customer. There could be multiple and different lexicons between businesses, fields and contexts that a generalised out of the box software cannot process with accuracy. For example, a legal consultancy would need a chatbot trained on a completely different vocabulary than a sports betting company. Semantic analysis takes into account the context of an event before processing the meaning of a text related to that event. Text summarization, information retrieval, topic classification etc are more examples of NLP applications that save effort and resources while providing better results. Categorical text classification and sorting can be used to sort emails and documents into relevant groups to be processed by the relevant teams more efficiently. NLP can also be used to gain insight into the market competition using their social media and online activity, and can be used to plan marketing strategies. NLP is often used in parsing CVs for recruitment purposes, preparing job postings according to the current trends and analysing the potential employee profiles.
3) Chatbots and customer support:
Chatbots have become increasingly popular in the last couple of years, with multiple small and large businesses developing chatbots that integrate with social media or work independently. Chatbots can be used as Business to Customer (B2C) or Business to Business (B2B) interaction software to help with business intelligence. Chatbots used in customer service interactions improve the quality of service and turnaround times with minimal human involvement. AI fuelled customer service chatbots are often used in combination with human agents, on one hand they machine conversation can be handed over to an agent when the chatbot cannot process the context, and on the other hand, quicken the agents responses by providing recommended content for a conversation. Question and answer chatbots go through the past conversations and train on the clues and contexts for each conversation to be able to reply appropriately. Chatbots can also be used within companies to analyse employee satisfaction or feedback, which can be used to improve the work environment. Conversational chatbots can also do data analytics on the go as we talk to the bot. A software that listens to you ask, “what are the customer retention rates for this year?” during your meeting can parse the information to figure out the context of your question, then retrieve relevant information from the databases and display the output figures instantly. You can chat with your artificial assistant and ask it about your company numbers, your brand sentiment in the market, and even business recommendations if the bot has been trained on your past business decisions. You can use a neural machine translation algorithm to read you your product reviews or documents that takes a long time to manually translate and analyse. Apart from the above-mentioned applications, NLP has plenty more to offer to a business setting. From simple text analysis and classification to sentiment and semantic understanding or conversation agents, NLP holds the key in making businesses strategize their plans to keep up with the current world of large social media data and is an essential tool in every company’s kit.