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 (C
).
A -> B -> C
I'm interested in predicting the conversion from A
to C
, which can be done with a logistic regression.
I wonder if I could also do two logistic regressions, first from A
to B
, then from B
to C
, and multiply the predictions.
What are the differences between the two approaches?
Things to consider:
- What if the conversion rate from
A
toB
is 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
A
toB
but nothing ofB
toC
, or the other way around.