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Ujjawal
Quartz | Level 8

I am building a retail customer attrition model for saving accounts of a bank. I am targeting both soft and hard attrition (Dependent Variable).
By 'soft attrition', i mean if saving balance drops by 80% or more. By 'hard attrition', i mean customer closes his account.
My question - can i use % balance change as a predictor/ independent variable in the model. Since i am already taking customers having 80% or more balance dropped as an event in the dependent variable, would it be right to use the % change as an independent variable in the model?

6 REPLIES 6
Ksharp
Super User
What kind of model are you using ?
If it is logistic model , I think you can use % balance change as Y .
For example:

data have;
 set have;
retain n 1;
 per_balance_change=.........;
run;

proc logistic ...........
 model per_balance_change/n = ...........;

You can take HARD as per_balance_change=1  .
Reeza
Super User

As long as the information predates your knowledge of their attrition. So perhaps % change in prior month? 

 

Using it over same period as your definition of dependent would be incorrect as then this is Informatiom is after the fact. 

Rick_SAS
SAS Super FREQ

80% drop from where? Maximum all time balance? Previous three months?  As @Reeza says, you might want to introduce a lagged variable (3 or 6 months?) to help you formulate this model.

Ujjawal
Quartz | Level 8

Thanks everyone for your response. @Rick_SAS and @Reeza - Yes, it's 80% drop from maximum all time balance. I have considered it as a target variable. Can i use % balance change  from maximum all time balance as an independent variable? If i am not wrong, you are suggesting i can use % change in 3 months or 6 months as independent variables, not % balance change from maximum all time balance.

Rick_SAS
SAS Super FREQ

SAS will enable you to build either model. I don't have any intuition about which will perform best, so I will leave that modeling decision to you.   You can test multiple models on historial data and choose the one that performs the best.

Reeza
Super User

If it's too relate to your definition of attrition it won't make logical sense. It'll make your model seem more accurate than it actually is..

 

 

 

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