AI-powered solutions are revolutionizing domains where humans even recently were thought indomitable. Natural language analysis is a good case in point. Read on to learn how natural language processing can help you save time and money streamlining daily tasks in your company.
In a nutshell, natural language processing (NLP for short) is about using computers to analyze, understand and manipulate human language, in the form of either text or speech. Combining linguistics and computer science, NLP dates back to the 1950s, but has now been revolutionized by machine learning techniques based on deep neural networks.
Models developed thanks to deep learning algorithms handle the intricacies of human language considerably better than the once widespread statistical approach. It is no wonder, then, that AI-powered natural language technology was quickly applied to solve real-life problems, as does the machine translation Google has brought us.
Google Translate was among the first solutions to implement at scale this new approach based on neural networks. But the possibilities for NLP business applications only begin there. NLP systems can improve the internal business processes in your company in a number of ways.
Harvesting internal messages
NLP can be used in business to harvest the information contained in a company’s internal communications. Your employees daily exchange thousands of emails and communicator messages. These data are unstructured, so it would be too time-consuming to analyze them manually. But text mining methods can transform these unstructured texts into meaningful information.
For example, text classification allows you to tag and categorize texts.Topic analysis, meanwhile, will help you understand the main subjects in the texts you are analyzing, while sentiment analysis can show you how the creators of text feel about particular products or services. Finally, text summarization allows you to extract key information from content. All these NLP techniques can bring you valuable insights and actionable information to streamline the work in your company.
Improving communication with customers
One of the most common concerns companies face today is how to provide effective client or customer support. Hundreds of thousands of requests, inquiries, e-mail and other communications from clients must be handled swiftly and accurately. Doing so can give a company an edge in customer service, a key factor in driving company growth.
The constant pressure companies must shoulder to maintain high-quality customer service can of course be costly in terms of both money and man-hours. Here too NLP brings a game-changer: chatbots, or software that can text back and forth with humans, answering frequently asked questions and other queries.
Having software handle such correspondence allows human agents to focus on more complicated cases requiring a human touch, thus saving the company time and money. There are a variety of types of chatbots: from very simple ones that direct clients to given resources, to more sophisticated ones that lead domain-specific conversations. With chatbots in your company’s lineup, clients will be more satisfied with your services and thus more loyal and willing to recommend them to others.
Reviewing large quantities of legal contracts
NLP for business intelligence can also be used to review gigantic volumes of legal contracts. Global tax and accounting titan Deloitte uses AI to look for changes to control provisions in contracts when, for example, a client is selling a business unit. Such a job could take dozens of employees half a year to complete, but a crack team of six to eight AI engineers can now handle it in less than a month.
Another member of the Big Four, Ernst & Young (EY) uses an NLP system to review lease accounting standards. When a new regulation is issued, the system extracts the relevant information to be verified by humans and ensure that all documents comply with the law.
EY’s AI-powered, human-in-the-loop system
proved three times more consistent and twice as efficient as human-only teams. Moreover, the company achieved break-even ROI in less than a year. In both of the above cases NLP solutions helped cut human labor costs while considerably speeding up the entire process.
Harvesting insights from unstructured data
IDG
predicts that by 2025 there will be 175 zettabytes of data globally. It is also estimated that 80% of current data is unstructured – that is, it has not been structured with a predefined data model and is not organized according to any predefined rules.
Typically, unstructured information is text-heavy and includes documents, e-mails, social media posts, and the like. As it is virtually impossible to control the flow of such types of information, companies make only minimal use of the unstructured data they hold. Natural language processing is the right tool to harness this gold mine of hidden insights. NLP algorithms can extract meaningful information from unstructured data that can be used to make more informed decisions.
Another technology being used to harvest insights from unstructured data is sentiment analysis, which classifies emotions buried in text data available online
. Twitter or social media in general are a good source here
. Sentiment analysis is often used to determine how customers are feeling about a particular brand, product or service. Possessing such knowledge will improve your communication with customers. Knowing how they feel about your company, you can address their concerns directly and thus improve brand awareness and overall customer satisfaction.
Analyzing helpdesk tickets
Finally, natural language processing can help you handle support requests that are pouring in from your clients. A typical support agent has to classify incoming tickets to redirect them to the appropriate team. This process takes a lot of time, as every ticket must be read and analyzed, and is also ripe for errors. After all, who among us can maintain a constant level of concentration throughout the day?
NLP tools can automate ticket classification by tagging and routing them automatically to a dedicated team. The classification model can classify tickets using different criteria such as topic, urgency, sentiment analysis (e.g. positive, negative or neutral) and language.
Apart from classifiers, one can also build a model that extracts relevant information from tickets. It goes without saying that such a classifier or information extractor can considerably speed up the work of help desk agents by taking care of simple and repetitive tasks. In fact, omnichannel support desks such as
Salesforce integrate AI-powered automated ticket classification and routing into their software because it clearly improves internal business processes. Last but not least, automated ticket classification offers scalability, so more agents don’t need to be hired to deal with a jump in in incoming tickets.
NLP applications in business—takeaways
As you can see, natural language processing can help you run your business more effectively by reducing the amount of manual work as well as the costs associated with it. If you would like to know how your company can monetize NLP, contact us to see how we can help you build customized NLP solutions.
