What it is: Text Mining is a process wherein machine learning technology is used to read patterns in unstructured linguistic data like blogs, social posts, and help desk comments. Examples of this can include categorization, entity extraction, and sentiment analysis.
What it does: Text Mining allows companies to gather important information about their customers from data sources that would otherwise be impenetrably dense. Once trained on sample text mining algorithms break down unstructured communication by emotional tone and subject matter. This makes it possible to get a genuine sense of, for example, what customers feel about a new software update, or whether dealing with your customer support department is a pleasant experience, just by looking at everyday communications
Why it matters: A huge percentage of data is unstructured, and many predict up to 80 percent of data will be unstructured by 2025. This means that companies that don’t incorporate text mining tools (or other ways of incorporating unstructured data) will lose out on a primary source of valuable information.
What to do about it: Use text mining to take advantage of data sources that are currently untapped, or to speed up research that’s already taking place. Incorporate the resulting insights into targeted messaging, customer service, and wherever else they apply.