I was exploring the options to design the approach to train test and predict the customer lapsing model.
Lapsed => Customer didn't purchase an item in the financial year (ex: Customer A didn't purchase any item in 2010 => Customer A is lapsed in 2010)
Say, I have list of 500 customers and 9 years of the data which tells whether customer lapsed or not (from 2000 to 2009 and I have to predict for 2010)
- Data Format
Customer_id x1 x2 x3 lapsed_2001 lapsed_2002 ... lapsed_2009 A a b c 0 0 1 B p q r 1 0 0
I'm considering two options to model this
Option A: Use all of the 500 Customers data make lapsed_2008 as Predictor Training Variable to fit the model and train the model. Now Test using lapsed_2009 Variable.
Option B:Use 375 Customers data make lapsed_2009 variable as Training variable and test for the rest of 125 Customers
Can anyone please explain me which approach to be followed and why ?