08-02-2010 05:07 PM
2 weeks ago
The best analytical approach to an analysis must take into account the availability of and quality of the data as well as the business objective. It doesn't make sense to analyze data from the 'distant past' unless you believe the relationships will still hold for current data. In general, you would likely get a lot of different answers even with that detail available. In many cases, the business needs will help to identify how large each window is.
My assumption is that your recovery model would be tasked with identifying how to obtain the most revenue from people who still owed money after disonnecting their service. These problems might be well-suited to a two-stage model -- one model which identifies whether you receive any money from someone after they disconnect, and the second model which predicts how much money you will be able to recover after they disconnect. The product of these two models predictions generates an 'expected revenue per disconnect' which you could use to prioritize those accounts that will provide you the greatest revenue. There is a TwoStage node that accepts both a binary (Yes/No) target and a value(e.g. revenue) target and allows you to build this model automatically. You can also model the two targets separately.
I hope this helps!