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I have to built a customer churn model for of a teleco. The churn rate is 15 %. There is no particular campaign conducted. By churn I mean customer leaving the teleco permanently.

Data is available from Jan 2012 to Dec 2014

Question

  1. Should i have a fixed performance window? i.e. i will only flag customers who churned from July 2014 to Dec 2014 as churners & use only data from before July 2014 to create predictors in the model.
  2. I will consider customers who churned from July 2014 to Dec 2014 only but performance window will be rolling. Example if a customer churned in nov 2014 i will create predictors using data before nov 2014. If a customer churned in sept 2014 i will create predictors using data before sept 2014.

Which is the best approach, using fixed performance window or rolling performance window? What is the pros & cons of these 2 approaches?

Thanks

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  • $\begingroup$ What is "churn"? You should explain such uncommon words! $\endgroup$ – kjetil b halvorsen Feb 17 '15 at 14:44
  • $\begingroup$ Added to original post as an edit. Please check $\endgroup$ – GeorgeOfTheRF Feb 17 '15 at 14:48
  • $\begingroup$ It would be helpful to know whether the act of churning is factual, i.e. in case of breaking of the subscription contract it is, but if we are talking about pay-as-you-go, than it's not. $\endgroup$ – coulminer Feb 17 '15 at 15:24
  • $\begingroup$ This for businesses to business. So customers break the contract when they churn. $\endgroup$ – GeorgeOfTheRF Feb 17 '15 at 15:58
  • $\begingroup$ @ML_Pro, My recommendation is to use a rolling window. This will help you incorporate seasonality (if it exists) in your model, and it will make your model more generalizable. I can elaborate on this, if you're still looking for a detailed answer. $\endgroup$ – Vishal Feb 9 '18 at 19:33

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