I am wondering what are the advantages/disadvantages of breaking down a logistic regression in multiple steps, when they are available.
Let me explain what I mean by multiple steps: Think of it like the customer journey: A cold lead (
A) becomes a prospect (
B) who then becomes a customer (
A -> B -> C
I'm interested in predicting the conversion from
C, which can be done with a logistic regression.
I wonder if I could also do two logistic regressions, first from
B, then from
C, and multiply the predictions.
What are the differences between the two approaches?
Things to consider:
- What if the conversion rate from
Bis small? (Then the sample size for the 2nd model is small as well)
- Where does most of the signal come from? Maybe my explanatory variables explain most of
Bbut nothing of
C, or the other way around.