Five NLP applications to power your business
Unstructured text is the largest human-generated data source, and it grows exponentially every day. The free-form text we type on our keyboards or mobile devices is a significant means by which humans communicate our thoughts and document our efforts. Yet many companies don’t tap into the potential of their unstructured text data, whether it be internal reports, customer interactions, service logs or case files. Decision-makers are missing opportunities to take meaningful action around existing and emerging issues.
The practical side of NLP in Business
NLP tools are helping companies understand how their customers perceive them across all channels of communication, whether emails, product reviews, social media posts, surveys, and more.
Not only can AI tools be used to understand online conversations and how customers are talking about businesses, but they can also be used to automate repetitive and time-consuming tasks, increase efficiency, and enable workers to focus on more fulfilling tasks.
Here are some of the main applications of NLP in business.
Sentiment analysis identifies emotions in text and classifies opinions as positive, negative, or neutral.
By analysing social media posts, product reviews, or online surveys, companies gain insight into how customers feel about brands or products. For example, companies analyse tweets mentioning their brand in real-time and detect comments from displeased customers right away. They send out surveys to find out how customers feel about the service.
A retail company can use sentiment analysis on reviews of articles to gather information to improve an article.
For example, a marketing department for an electronics company might launch a campaign for its new reasonably priced portable chargers. Based on sales, it might think that it is doing well. However, in reality, the customer might not like the product and may take to social media to complain about it. If the company can analyse those tweets and reviews using NLP, it will be able to understand what people are talking about, their sentiment (positive, negative, neutral), and even how emotional they are about it (from the words used in the tweets).
This can also be used for the detection of how urgent e-mails are. For example: “I have been waiting for an answer from your company for 3 days after delivery has gone wrong.”
We also have solid experience in smart reacting tools. For example, we can identify “sensitive” / sentimental comments based on sentiment analysis (NLP). If a user posts a sentimental comment on the content of the company’s socials, a warning will be sent to the social media manager to respond to this as soon as possible.
We can extend this to identifying and selecting trending hashtags and best timing suggestions to post content.
We develop this in one integrated solution, everything is built and tailored to the needs of our clients.
Ask our NLP expert Julie Derumeaux for more details.
Today, with even the smallest business potentially serving a global client base, the need to communicate across languages and cultures is growing rapidly. However, cross-context communication is no always easy. Unless great care is taken, many things are lost in translation due to differing interpretations of even correctly translated communications.
The costs of translation failures are often more than just financial. Miscommunication can lead to loss of reputation and legal exposure. For this reason, many companies are investing in NLP technology to help them in clear and accurate communication between cultures and languages.
We can also train your translation tools to understand specific terminology in any given industry, like finance or medicine.
Text extraction enables you to pull out pre-defined information from text. If you deal with large amounts of data, this tool helps you recognize and extract relevant keywords and features (like product codes, colours, and specs), and named entities (like names of people, locations, company names, emails, etc). Companies can also use our text extraction tools to automatically find key terms in legal documents, identify the main words mentioned in customer support tickets, or pull out product specifications from a paragraph of text, among many other applications.
There are also many possible applications here for accounting. For example, automatically classifying invoices, giving suggestions for entries, entering recurring invoices. The efficiency gain for accounting firms is potentially massive with this technology.
This can also be very valuable for financial traders to have an insight into what is happening and what people are talking about. NLP can be used to track news, reports, comments about possible mergers between companies, etc.
Chatbots are AI systems designed to interact with humans through text or speech.
The use of chatbots for customer care is on the rise, due to their ability to offer 24/7 assistance (speeding up response times), handle multiple queries simultaneously, and free up employees from answering repetitive questions.
Through NLP, it is possible to make a connection between the incoming text from a human being and the system generated a response. This response can be anything starting from a simple answer to a query, action based on customer request or store any information from the customer to the system database.
Chatbots actively learn from each interaction and get better at understanding user intent, so you can rely on them to perform repetitive and simple tasks. If they come across a customer query they’re not able to respond to, they’ll pass it onto a human employee.
In other words, NLP based chatbots can significantly assist in cutting down costs associated with manpower and other resources entangled in repetitive tasks as well as costs on customer retention while improving efficiency and streamlining workflows.
Topic classification helps you organize unstructured text into categories. For companies, it’s a great way of gaining insights from customer feedback or forecasting trends. Imagine you’d like to analyse hundreds of open-ended responses. How many responses mention your customer support? What percentage of customers talks about your pricing?
Also, you can use topic classification to automate the process of tagging incoming support tickets and automatically route them to the right person.
We are currently working on a research project called ‘Trendify’ together with Roularta Media Group and the fact-checking company Trendolizer, a trusted partner of Facebook. Our solution will use topic modelling. With the rapid dissemination of information via social and digital media, it is a challenge for journalists to deliver qualitative, nuanced journalism. Trendify aims to optimize editorial workflow and news quality by developing a suite of AI-powered media monitoring software tools. These should enable journalists to collect relevant information from digital data streams, such as articles and comments on (news) websites, blogs and social media feeds.
It’s a great tool to summarize large amounts of texts and our solutions are made to scale to any organisational needs.
Contact us for more information
With our expertise we can deliver fast and accurate results tailored to the needs of your organisation.
Feel free to contact our NLP expert Julie Derumeaux and have a chat with her to discover how NLP can help accelerate your business.