Evaluating machine learning model with missing features
If less than 20-25% of behavioral data is missing, maybe you could try to impute missing data  using one of the following solutions : 


Impute missing behavioral data using some business rule or by training a machine learning model with demographics data as input and behavioral data as output variable.
Impute missing data with feature mean/median.
Impute missing data by picking up random value in the feature distribution (hot-deck).


In case you have more than 20-25% missing data, it will be really hard to impute values. I think in this situation you should consider creating a new model such as : 


The new model doesn't use behavioral data anymore.
The new model is based on a different train-val-test split in order to have behavioral data in each dataset.


If you can't create a new model neither impute missing data, I guess hot-deck would be the best option you have to avoid bad performance on unseen data.