Predictive Analytics World covers hot topics and advanced methods such as social data, uplift modeling (net lift), text mining, massively parallel analytics, in-cloud deployment, and innovative applications that benefit organizations in new and creative ways.

Predictive Analytics World San Francisco's February 16-17, 2010 conference program covers hot topics and advanced methods such as social data, uplift modeling (net lift), text mining, massively parallel analytics, in-cloud deployment and innovative applications that benefit organizations in new and creative ways. PAW is the business-focused event for predictive analytics professionals, managers and commercial practitioners.

Packed with top predictive analytics experts, practitioners, authors and business thought leaders, the conference includes keynote speakers Kim Larsen, Director Advanced Analytics at Charles Schwab, Andreas S. Weigend, Ph.D., Former Chief Scientist at Amazon.com, and Program Chair Eric Siegel, Ph.D., President of Prediction Impact and former Columbia University professor.

The conference also features speakers and case studies from varied enterprises such as 1-800-FLOWERS, Amazon.com, AT&T, BBC, Canadian Automobile Association, Charles Schwab, Continental Airlines, Deutsche Postbank, Google, Group RCI, IBM, PASSUR Aerospace, PayPal (eBay), Sun Microsystems, U.S. Army, Visa, Walmart Financial Services, and Younoodle, plus special examples from the U.S. government agencies CBP, NCMI, NGIC, NSA, and SSA.

The event follows PAW's successful 2009 launch, and covers commercial deployment across industries and software vendors. Attendees hear precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and what kind of business impact it delivers.

"No matter how you use predictive analytics, the story is the same," says Eric Siegel, Ph.D., conference chair and president of Prediction Impact, Inc. "Predictively scoring customers optimizes business performance." Siegel adds that predictive analytics initiatives across industries leverage the same core predictive modeling technology, share similar project overhead and data requirements, and face common process challenges and analytical hurdles.

Read more at http://www.predictiveanalyticsworld.com/

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