Data is everywhere. An enormous quantity of it.
And much of this data today comes in the form of natural human language. Just think of all the emails, social media posts, comments, books, articles, and the list goes on.
But human language is complicated, ambiguous, and messy. Pretty much the opposite of the highly structured data that machines usually deal with.
So how's it possible for computers to understand our language?
This article explores Natural Language Processing (NLP), its business applications and how it can help make business intelligence easier and more accessible to everyone.
Natural language processing (NLP) is a branch of Artificial Intelligence (AI) that allows computers to understand and process human language and to generate responses.
In its most basic form it can:
To understand and interpret human language, computers need to recognize and analyze words, sentences, and the context of a piece of content.
Example | If I write: 'The cars are blue', the machine should understand that 'cars' is the subject, 'are' is the verb, and 'blue' is the adjective describing the cars. But 'cars' can also refer to the Pixar animated movie, not only to the vehicle; its meaning will depend on the context of the sentence.
NLP is the process that helps the machine understand if the word 'cars' stands for automobiles or the movie.
Nowadays, tools like vocal assistants, search engines, and language translators all use NLP techniques to perform their tasks.
So what about business?
Natural Language Processing in business helps make analytical tools more accessible, democratic, and easy to interact with for the final user.
Let's see how.
Natural language analytics is a form of search-driven analytics that makes it easier and accessible for business people to ask questions, thanks to natural language.
By recognizing entities like names, places, or dates from the body of a text (a process called ‘named entity recognition’), computers understand what has been asked (text classification), retrieve the information needed, and finally present the insights in different formats, like visualization, or text.
Example | You're a sales representative who wants to investigate business performance, and you ask, "Show me the sales trend for pc holders in the last three months," the system creates a line chart showing the sales trend for the three months.
Thanks to Natural Language Analytics people can ask for data in natural human language, like they were speaking with a colleague.
This kind of feature can also be enhanced with things like autocompletion, which uses NLP and machine learning in order to predict the word you may write next with the highest probability. Think of when you write an email; Gmail usually suggests which word to write next.
Imagine analyzing any kind of dialog between your company and your customers to gain insights on their experience.
Conversational analytics is the practice of using Artificial Intelligence, more specifically NLP techniques, to derive data from human conversations with customers and respond appropriately.
It incorporates a few additional capabilities: voice recognition and voice response. It makes speaking with virtual assistants possible, by simply recognizing a voice input and reading the response like a human.
Conversational analytics also helps maintain the context so that if you're asking for more information, you don't have to repeat the entire question each time.
Example | If a sales manager asks, "What are the sales for this product for the past quarter in EMEA?", they receive the answer, and then ask "And what about the U.S.?", the system can understand that what they're asking for is the same information just in another region (the U.S. and not EMEA).
More advanced systems can also anticipate new questions, make predictions, and investigate why something happens (diagnostic analytics).
Crystal is the generative AI platform for business intelligence that supports Natural Language and Conversational Analytics to make data accessible to all employees.
It takes a human-first approach that enables business people to access and interact with data through natural conversations, receive recommendations, and collaborate in one easy-to-use, intuitive platform.
NLP classifiers represent the core of Crystal's intelligent capabilities. Each classifier is specialized in detecting and understanding specific aspects of the conversation with the person.
Crystal relies on multiple AI classification models to interpret all the different users’ requests or conversational inputs, from chit-chat interactions to specific references to topics.
To learn more about Natural Language and Conversational Analytics in Crystal, watch the iGenius webinar recording - Sales Ops
It's not a matter of if but rather when generative AI will be integrated into business everywhere. We believe, when it’s done, it should be safe and secure, giving organizations private access to their data, always putting people first.
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