Good suggestions so far. Laura's suggestion is very good. With a binary variable, many analysts (and business users) like the output from the Decision Tree node. It can help you identify which variables might be important factors for churn and give you a model to score new data. Here is some good reading:http://support.sas.com/publishing/pubcat/chaps/57587.pdf A REALLY good book to pick up is http://www.amazon.com/Data-Mining-Techniques-Relationship-Management/dp/0470650931. It has a great chapter on decision trees and likely covers survival analysis too. Your data set has character variables that I *think* should be numeric. For example, Revenue would look like 22.000.000 which I think should have been $22,000,000 (or 22000000)? When you import the data into EM, make sure you spend the time to set the roles and levels of each variable. Churn would be Target and Binary. There are a number of other variables that should be set to Binary too. Customer could be set to a role of ID. ChurnDep should be Rejected because it is redundant to churn. The decision tree will split once on that variable and stop splitting. Calibrat should also likely be rejected.
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