HPCwire: SPSS Unveils New Data Mining Workbench

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January 18, 2008

SPSS Unveils New Data Mining Workbench

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New predictive text mining combines structured and unstructured information to provide a complete view of customer behavior

CHICAGO, Jan. 14 -- SPSS Inc., the leader in predictive analytics software, today unveiled Clementine 12.0 and Text Mining for Clementine 12.0, the latest release of its market-leading data and text mining technology. Clementine 12.0, the industry's most complete data and text analytical workbench, now delivers increased analyst productivity, deeper information insight, and advanced visualization.

"Clementine 12.0 is the predictive analytics technology that enables organizations to become a Predictive Enterprise," said Bob Dutcher, vice president, product marketing at SPSS. "With the new version, our customers can extract even deeper insight from data and text, delivering greater return on all of their data assets. SPSS continues to lead the predictive analytics market by giving our customers the tools and solutions to unlock the value within their enterprise data, improve business processes and make more informed decisions."

Intelligent Modeling and Advanced Visualization

Clementine 12.0 offers faster and greater return on analytical investment through automated modeling, which quickly identifies the best analytic models and combines multiple predictions for the most accurate results. This gives an organization an unprecedented level of insight and prediction from their data to better solve a wide array of challenging business problems -- including customer churn, campaign effectiveness, customer value, marketing cost, fraud and risk analysis.

With new advanced visualization capabilities in Clementine 12.0, including rich and improved graph interactivity and custom tabular reports driven by a powerful visual design interface, organizations can now widely distribute and communicate results to speed smarter, more proactive decisions.

"Clementine 12.0 gives our business users and analysts the most comprehensive solution to gain deeper insights into our customer behavior to increase sales, profitability, and productivity for our organization," said Matt Wroblewski, director of marketing research at Lincoln Financial Distributors, an operator of multiple insurance and investment management services. "The new features have helped us better assess the value of different groups of customers and more effectively target at-risk profitable customers for retention efforts. The model comparison feature has made our users more productive by allowing them to execute and compare multiple models simultaneously."

Predictive Text Mining

Text Mining for Clementine 12.0 gives organizations a unique advantage to extract key concepts, sentiments and relationships in different languages from textual or 'unstructured' data, such as email, blogs, RSS feeds and surveys. Users can easily extract additional insight and predictive power from these channels to draw more reliable conclusions and take more effective action -- transforming Clementine 12.0 into a fully integrated data and text mining solution.

Federico Cesconi, head of customer insight and retention at Cablecom, a Swiss cable television company, said, "Our organization has been able to better understand and quantify reasons for customer churn through the use of SPSS predictive text mining software. We've uncovered concepts and relationships in text that would be too costly -- or even impossible -- to detect by any other methods."

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