The key to making a business case for any Analytics initiative, not just text analytics, is to identify specific business problems and pain points and use analytics to address them, instead of merely seeking insights.
The top business case for Text Analytics
Companies find themselves in a world where an increasing number of their customers are using social media, and the one thing, people LOVE doing on social media is talk (tweet/post/blog/whatever...) They talk about their experiences in dealing with the company and its services or products, about its competitors and about how they really feel. (Consumer feedback online: How you can spot it, sort it, and react quickly, using text analytics.)
So, as a company, you have all this customer feedback out there, in the form of text, just waiting to be gathered. The risk company management faces, for not capturing this customer feedback, is just too great to ignore. They face the risk of looking bad (think PR nightmare) and losing their competitive advantage, if they do nothing about it, which brings me to the single most important use case driving text analytics in the enterprise today, which is, the compelling need for Social Media engagement and analytics. While some may argue that, this is too narrow a focus for the application of Text Analytics and while other use cases for text analytics may have greater ROI potential, analyzing unstructured text for social media, is often the first and most appropriate use case for companies to begin with and demonstrate ROI, before moving to other use cases.
Meta S. Brown, has an interesting viewpoint on building a business case for text analytics and the pros and cons of taking a cost saving vs. revenue increasing benefit approach, which can be found here.
Key Trends in Text Analytics
There are some other major trends that stand out in Text Analytics, from this 2013 report by Hurwitz and Associates which are still relevant today and are useful to know when making the business case. I have listed below what I felt to be most relevant ones:
Text Analytics is moving beyond sentiment analysis
Sentiment analysis is evolving, with vendors, offering sophisticated sentiment analysis on multiple scales, rather than simply classifying a document, or a phrase, as positive, negative, or neutral. Text analytics is being used to identify "emerging issues" or the "birth of a trend". This helps companies to be more proactive in dealing with issues before they become real "issues" that could have been "nipped in the bud".
Marrying structured and unstructured data is becoming more popular in text analysis
End users have begun to realize the value of analyzing unstructured text data in conjunction with structured data (e.g. sales and demographics data) which can be used to provide a lift to predictive models.
The cloud is becoming increasingly important as a delivery model for text analytics
Companies that are lacking in in-house skills to aggregate and analyze unstructured information are turning to Software as a Service (SaaS) solutions for help.