ARMONK, N.Y., May 11. IBM today announced new software that enables users to uncover and analyze information from social media sources, such as social networks and blogs, and then merge that with vast internal data for faster, more accurate insight and predictive intelligence.
The new data mining and text analytics software allows users to monitor changes in consumer, constituent and employee attitudes, uncover deeper insights, and then predict key factors that will drive future customer acquisition and retention campaigns. For example, companies can now extract sentiment from the use of emoticons and slang terminology that people often use in describing their view toward a product or service. As part of today's news, IBM is also announcing customers such as Navy Federal Credit Union, Rosetta Stone and Money Mailer that are making faster and more personalized decisions with predictive analytics software through insight gained by extracting information about client sentiment from a variety of data sources.
Recognizing that each industry has unique priorities and its own vernacular, the new software analyzes trends and captures insights from industry-specific terminology. Within these domains, the software includes new semantic networks with 180 vertical taxonomies (from Life Sciences to Banking and Insurance, and Consumer Electronics), and more than 400,000 terms, including 100,000 synonyms and thousands of brands. This allows customers to draw better links and understanding between sentiment and products without having to spend time building their own definitions.
For instance, in the banking industry, the semantic network knows that a "floating rate" is a "Mortgage Loan," and "Variable Rate Mortgage" and "Adjustable Rate Mortgage" are synonyms. It can also detect that "estate planning," "older people," and "retirement planning" are related to "reverse mortgage."
With IBM predictive analytics software, customers can directly access text, web and survey data and integrate it into predictive models for more comprehensive recommendations and better business decisions. It uses natural language processing (NLP) to allow clients to pull key concepts, opinions and categories relevant to their business from these data sources to uncover deeper customer insights.