Predictive Analytics World Recap

Predictive Analytics World (PAW) just ended today, and here are a few thoughts on the conference.

PAW was a bigger conference than October's or last February's and it definitely felt bigger. It seemed to me that there was a larger international presence as well.

Major data mining software vendors included the ones you would expect (in alphabetical order to avoid any appearance of favoritism): Salford Systems, SAS, SPSS (an IBM company), Statsoft, and Tibco. Others who were there included Netezza (a new one for me--they have an innovative approach to data storage and retrieval), SAP, Florio (another new one for me--a drag-and-drop simulation tool) and REvolution.

One surprise to me was how many text mining case studies were presented. John Elder rightfully described text mining as "the wild west" of analytics in his talk and SAS introduced a new initiative in text analytics (including sentiment analysis, a topic that came up in several discussions I had with other attendees).

A second theme emphasized by Eric Siegel in the keynote and discussed in a technical manner by Day 2 Keynote Kim Larsen was uplift modeling, or as Larsen described it, Net Lift modeling. This approach makes so much sense, that one should consider not just responders, but should instead set up data to be able to identify those individuals that respond because of the marketing campaign and not bother those who would respond anyway. I'm interested in understanding the particular way that Larsen approaches Net Lift models with variable selection and a variant of Naive Bayes.

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