Wow, that's very interesting!
I'm sure there are multiple ways to do this. Also, I don't know if the following suggestion is very good. But, it occurs to me that one could do the following:
Assuming that each product could have from 0 to 4 benefits configured for it, then there are 2^4=16 possible ways to configure a product alternative. So, respondent answers could be coded as a categorical dependent variable with 16 outcomes.
Now, the discount variable (with its four levels) could be set up as two separate alternative-specific attributes (one for the 2- or 3-benefit case and one for the 4-benefit case).
The alternative-specific attribute for the 2-benefit discount levels would be a generic predictor (applicable) for any of the 16 categories of the dependent variable which imply 2 or 3 benefits. The 4-benefit discount attribute would be an alternative-specific attribute predicting into the categories of the dependent variable that imply 4 benefits.