How Do I Love Natural Language Processing? Let Me Show You The Ways
More and more businesses are turning to Natural Language Processing (NLP) technology. They’re using it to improve customer experience, ensure round-the-clock access to data, and more. I’ve seen many NLP use cases in my job, but here’s a look at some of the ones I find most interesting.
When I hear NLP, the first thing that comes to mind is a more intuitive, human-like customer experience.
Artificial Intelligence makes it possible to derive valuable customer behavior patterns and provide a more personalized customer experience. Moreover, the customer engagement process itself can be automated to an extent that customers will only be dealing with virtual assistants and back office robotics, which in business terms means lowering costs and increasing revenue.
Chatbots and virtual assistants can easily handle basic enquiries, providing customers with information on how to perform certain actions. And this is especially big in the financial services industry.
According to the Banking Technology Vision 2017 by Accenture, 80 percent of bankers expect AI and NLP to boost technology adoption throughout their organizations, and 78 percent are looking forward to a simplified user interface that will ensure a more human-like user experience.
Data Analytics and Forecasting
Another major area of focus for NLP application within many organizations is data analytics and forecasting. To succeed in highly competitive industries, the entire organization must have access to information as close to real-time as possible. This helps staff make informed, data-driven decisions and gain a better understanding of key operational environments.
Just imagine the usefulness of available unstructured data generated by the world every minute. Here are some stats that will give you an idea of what is happening in the world, data-wise:
- Everyday, media outlets publish millions of pages of news
• Every minute, analysts produce several research documents
• Every second, professionals receive emails with important information
It is that never-ending information exchange that drives business intelligence managers, for example, to use NLP to improve their models. Applying NLP to this kind of data enables teams to process unstructured text, identify patterns within it and turn it into intelligible insights.
Companies can then better evaluate opportunities and targets, improve their risk management and make other informed decisions.
It’s good to stay on top of your industry’s news and trends, but how far will you go if you can’t answer your own company’s questions? Most industries today require its players to have real-time access to their internal company data; otherwise, they will find themselves at a large competitive disadvantage.
With natural language search, companies can perform searches across their own databases and secure accurate answers in a matter of seconds. For example, people operate in different time zones, and sometimes, when a decision needs to be made immediately, they simply cannot afford to wait for the data to be sent from elsewhere. And this is where applying natural language interface to databases comes in handy: all you need to do is ask your database a question. Once built into your database, the NLP interface will translate human language into an SQL request, process it and provide you with an instant answer.
This will allow everyone, from a C-Suite to a marketing specialist, to have instant access to data and make faster decisions. This will also help move staff to higher value roles, because all internal interactions with data will be handled with the help of NLP.
Here are some examples of how different roles use NLP search:
- Customer service manager: search for key customer insights such as their history, business transactions, the products they are using, etc.
- Sales manager: search for status of a customer, types of product they use, etc. for upsell opportunities
- Purchasing departments: search for accurate pricing data
- Business leaders: search for insights to make faster, data-driven decisions to stay ahead of the competition
Building an in-house NLP interface requires a lot of time and investment, as well as experienced and talented data specialists. Look into existing solutions to save you from that extra work. Existing solutions should easily, instantly and securely integrate into your systems, and will turn human language into requests, and return data in a suitable format.
The list of possible NLP application scenarios is endless. No matter what industry your business is in, we all need instant access to the enormous amounts of data generated both externally and internally.