BI Blogs

Attending the Predictive Analytics World (PAW) Conference is truly a rewarding experience. Compliments go to Eric Siegel and the conference organizers for assembling such an interesting cast of case studies and speakers.

Day 2 kicked off with a key note from Kim Larsen from Charles Schwab & Co. on net lift (or uplift or incremental) modeling using SAS. The latter topic is a perennial favorite among data mining practitioners. Net lift models are designed to target undecided or swing customers (similar to targeting swing voters in an election campaign) to get incremental lift in revenue from marketing campaigns, while not targeting customers who would have bought it whether or not they received a promotion offer. The key learning from Kim’s session is to use appropriate variable selection and transformation methods while preparing data to maximize returns from Net Lift Modeling.

Next-up were my colleagues, Manya Mayes and Fiona McNeill, who shared a cross-industry text analytics case study and best practices for classification, sentiment analysis and search techniques in the SAS lab session.

I also hosted Birds of Feather topics on in-database analytics and rapid predictive modeling during lunch hours on each day at the conference. Other SAS topics included were Twitter analysis, sentiment and social analysis, and time series data mining. These topics generated good discussions in a short group sessions around trends, adoption and customer interest/readiness.
Continue reading “More from Predictive Analytics World”