New IBM Predictive Analytics Software Supports Customer Relationship Strategies

Helps clients combine data from social media with internal data for better business insight

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IBM has released new software that enables clients to analyze and predict information from social media sources (such as Twitter, wikis, RSS feeds and blogs) and merge it with internal data for faster, more accurate insight.

The new data mining and text analytics software also enables companies to extract sentiment from the use of emoticons that people often use in describing their view toward a product or service. This allows clients to monitor changes in attitudes and uncover deeper insights to predict key factors that drive future customer campaigns and take decisive action.

Recognizing the demands from industries having unique priorities and different terminologies in their data insight, the new software also supports and analyses support and analyze industry-specific trends, capturing insights from industry-specific language. Within these industry-specific domains, the software includes new semantic networks with 180 vertical taxonomies (such as life sciences, banking and insurance, and consumer electronics), and more than 400,000 terms, including 100,000 synonyms and thousand 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.

Organizations can combine all of their structured data with textual information from documents, e-mails, call-center notes, and social media sources. By incorporating text sources into modeling efforts, users can extract, discover and explore relationships between concepts and sentiments, including emoticons and slang terminology, leading to better insight to reach specific customers, constituents, employees, or students at a specific time and through a specific channel.

Achieving True Customer Intimacy with Predictive Analytics

Customer interactions through any channel are now truly evidence-based and result in more predictable (and profitable) outcomes. IBM predictive analytics software allows customers to translate customer knowledge into action. The result is a more effective customer relationship management strategy, including advertising and marketing campaigns; upsell and cross-sell initiatives; and long-term customer loyalty, retention, and rewards programs.

The latest version of IBM SPSS Modeler data mining and text analytics workbench is now available. Text analytics workbench is available only in IBM SPSS Modeler Premium edition.

More information about IBM business analytics are available at

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