San Francisco -- I just returned from two days at Predictive Analytics World in San Francisco. I must admit, my expectations weren't very high. I'm not enthusiastic about version one of just about anything, a reticence that has generally served me well. This time was an exception. Kudos to conference chair Eric Siegel and producers Prediction Impact, Inc. and Rising Media Ltd.

A hobbled Siegel kicked-off the conference on Wednesday with his keynote: Five Ways to Lower Costs with Predictive Analytics. His focus was on variations of response and churn modeling along with risk management. Uplift modeling has to do with understanding whether a positive response was caused by modeling solicitation or would have occurred anyway. It is, of course, cheaper not to intervene with those inclined to respond. Siegel is a proponent of proving the value of analytics by contrasting results of experimental and control groups.

Though not a keynote, John Elder's multiple case study talk: The High ROI of Data Mining Solutions for Innovative Organizations, offered wisdom for practical predictive analysis. Elder sees the primary functions of data mining as 1) eliminate bad; 2) discover good; and 3) streamline/automate for efficiency. His examples for Anheuser Busch, Walt Disney World, the IRS and hedge fund optimization were quite interesting, but his broader message was on the alignment of business and technology to ensure success. Critical factors include committed business champions, a strong interdisciplinary team, data vigilance, and a methodology that analyzes across multiple time periods.

Usama Fayyad, CEO of Open Insights, LLC and late Chief Data Officer of Yahoo, followed the data path in his keynote: New Challenges in Predictive Analytics. At both Yahoo and Open Insights, data strategy involves turning data into insights and strategic business assets. For Yahoo, the magnitude of data handled – over 25 terabytes and hundreds of millions of events per day – is staggering. Fayyad sees Yahoo’s “model” as the funnel from awareness - purchase, involving brand advertising and marketing.

The confluence of search, behavioral targeting and social media has both expanded and complicated traditional modeling. Fayyad sites Flickr photo sharing, with 90 million users organizing, generating, and distributing their own content using the “wisdom of crowds”. This new generation of on-line marketing involves individual targeting and social networks that lead to social targeting. The opportunities – and challenges – for predictive modeling in this evolving context are enormous.

Finally, Andreas Weigend, former Chief Scientist of Amazon.com, delivered a passionate talk entitled: The Unrealized Power of Data. Weigend’s point of departure is the evolution of customer relationship management to customer managed relationships (CMR). Observing that 50 percent of Amazon customers did not come onto the site with the intention of buying, Weigend opines that companies must fully utilize both existing and new data to impact their bottom lines and strengthen relationships with customers. He argues that location data, individual self-reports and individual relationship information are special keys to marketing 2.0 – viral or social marketing.

Weigend is also a big proponent of scientific methods for learning in business. His PHAME approach will be covered in a subsequent posting.

More of Steve's observations will be available on his blog.