Understanding How NLP Can Help Your Business
One of the largest sources of information (aka “data”) is the vast universe of written and spoken content on the web. Natural Language Processing (NLP) is one of the most significant advancements resulting from AI. NLP is the marriage of data science and AI, focusing on human-computer interaction, and processing and analyzing large amounts of natural language data. Understanding this bold observation requires that one evolves their definition of what constitutes “data”.
To understand what NLP can do for your business, start with viewing “data” as another word for “information”. There are two general categories of information: structured and unstructured. Prior to NLP, the only “information” available to technology was structured data – normally the output of a computer or other digital process. Virtually all technology outside of AI has been developed to process structured data, most recently the Big Data solutions that are still in use today. And there is no shortage of structured data sets that can be monetized with this technology.
Using NLP to Turn Unstructured Content to Structured Data
Now, consider the universe of written and spoken content that exists in nearly every marketplace; business news, white papers, industry publications, speeches, customer reviews, product announcements, sales briefs, and even clinical trials are just some of the examples one can name. Any one of these sources can provide valuable data within its unstructured and even conversational format. With NLP, these unstructured sources of information can be distilled into formatted and actionable data. There are three primary NLP functions that make this possible:
NLP allows for the determination if an unstructured source of information is relevant to a particular business use case. There are a multitude of unstructured information sources in existence that are constantly changing, and not all sources are of business value. Content Relevancy allows one to focus on sources that support the use case in question.
Once a source is determined to be relevant to a specific use case, Topic Modeling is used to identify the specific observations and information provided within, again ensuring relevancy to the use case.
Some relevant information sources can be extremely voluminous, with the majority of the content irrelevant or duplicative or illustrative, etc. NLP Summarization can transform a large unstructured information source into a concise and formatted summary of all relevant topics. Unstructured content such as clinical trials or legal filings are common examples of how the use of Summarization can allow a business to capture the essence of a source in a formatted data set.
Our Solution Advantage
Macrosoft AI Sales Enablement provides a 24x7x365 sourcing solution that continually scans the web for the appearance of information that has value (i.e. Content Relevancy) to one or more sales effectiveness use cases. End users of our solution identify which content sources are of potential interest (e.g. industry new services, competitive product announcements, publications/speeches by influencers, etc.). Our solution will automatically monitor the content produced by these sources, capture any topics that are addressed and are of relevance to a use case, and provide a summarization of this content to the end user. Access to the complete content source is enabled if desired.
A second approach supported by our solution is for end users to identify key marketplace influencers (e.g. customer advocacy groups, industry forums, business new services, prominent individuals) that they wish our solution to continually monitor for any content of consequence, having that content captured, summarized, and pushed to the solution interface.
With Macrosoft AI Sales Enablement, our customers can be assured that the appearance of potentially critical sales information will be automatically identified, validated for relevance, and proactively pushed to them in a format that allows for focusing all of their time on selling, making use of this information in the process.