Hazards, Densities and Repeated Events for Predictive Marketing - Bruce Lund, Magnify Analytic Solutions

This is a talk within the topic of survival data mining. Survival data mining is applied to time-to-event analyses based on large transactional datasets where the emphasis is on prediction. The question discussed in this talk is how to predict the time of repeated events. These repeated events might be a purchase, a service visit, or a late payment. Specifically, the goal is to find the probability density for the time to first event, the probability density for the time to second event, etc. Two approaches are presented and contrasted. One approach uses discrete time hazard modeling (DTHM). The second, a distinctly different approach, uses a multinomial logistic model (MLM). Attendees should be familiar with PROC LOGISTIC but there is no requirement for knowledge of PROC PHREG.

About the Presenter

Bruce Lund is a data miner and predictive modeler with 30 years of experience in direct marketing in the automotive industry including the development of in-market models, long-term value models, and loyalty/defector models for automotive clients. For the past 15 years, Bruce has been a consultant for Magnify Analytic Solutions, a division of Marketing Associates of Detroit MI, Wilmington DE, and Charlotte NC. Before Magnify, he was customer database manager at Ford Motor Company and a mathematics professor at the University of New Brunswick in Canada. He has a PhD in mathematics from Stanford University. During the past years he has been a frequent contributor to SAS user groups.