From the title I was hoping this would talk a little more about the model used. At 45 minutes runtime, I'm guessing it's something more interesting than a basic linear model.
Nice, I presume the dependent variable is binary, implying a logit/probit model? I don't really get why 3 different stat tools were used, ie Stata, R and SAS... R seems indeed plenty sufficient, or even Stata.
A side comment: My current company sells to enterprises. Out of curiosity, last year I tried to look at the usage patterns of customers who left (and stayed). I had a simple hypothesis: those who didn't use the software regularly were the most likely to leave. Surprisingly, this had no correlation. It was another reminder, that in enterprise sales, low or high usage patterns are sometimes not a predictor of customer retention. It obviously depends on the industry, but sometimes it has as much to do with internal politics, salesmanship, and other things that are simply outside the scope of your code.
Ryan, Just a note but I'm assuming you're going to take action on the emails you send to users that are likely to cancel rather than just having the act of the email be the deterrent to cancel.