Jozo Kovac

@ DataLLigence My guess is, the definition of churners should be modified. Well what we are trying to predict is 'soft' churn i.e. our analysis showed that 20 days of inactivity eventually leads to hard churn. So we actually are trying to model the 20 day dormant behavior and predict subscribers with an inactivity of 20 days. Could you suggest any alternative churn definitions from your experiences? I have a few questions/suggestions: 1.

If your event rate is around 17% and you say that at 50% cutoff you're getting a very good classification, there's something fishy! How can a logistic model trained to fit only 17% be better than what information the dataset has? Unless, you're measure of accuracy of fit is different from misclassification! Remember, the model usually fits the remaining 83% well, so the misclassification there would be low as compared to the 17%.

thanks Erik, You are right, the most important place to dig is in Customer Care system or better say CRM database. What I want is that what are the steps in an order way to design the prediction model and of course which model best suits for analyzing telecom data.