Today, IBM is introducing a new social media monitoring tool, one that it says will measure consumer sentiment from data gathered on Twitter, blogs and other web services and networks.
The software, called the SPSS Modeler data mining and text analytics workbench, will use natural language processing (NLP) to analyze everything from product names and industry jargon to slang and emoticons, and it’s already being used by some pretty big businesses.
Navy Federal Credit Union, Rosetta Stone and Money Mailer are already using IBM’s software to understand how consumers feel about their brands, products and competitors. This software can also be put to good use by political groups, marketing and advertising agencies, research firms and many other organizations and businesses.
Data from the social web can also be merged with internal data to create even more accurate intelligence about consumers, IBM says. “Organizations can combine all of their structured data with textual information from documents, emails, call center notes and social media sources.”
Companies already using IBM’s application have used this data to improve CRM and make better-informed choices about products and marketing.
Rosetta Stone’s VP of Strategic Research and Analysis Nino Ninov said, “Predictive analytics allows us to leverage unsolicited and unbiased customer feedback and strategically improve our business. We now can also monitor competitor and industry websites[…] to maintain a current view and better understand how the public perceives our competition.”
SPSS Modeler data mining and text analytics workbench is a high-grade product and might be both too sophisticated and too expensive for the average small business. The interface itself is hardly what we’d call intuitive or user-friendly. But for larger enterprises that need robust technologies and can’t risk entrusting data collection to a startup web app, IBM’s software might very well provide the features they need.