Ground-truth and feature extraction for predictive modelling
Your approach is a good one.
This way to extract features can lead to good results. 
But before moving forward I recommend answering these two questions:
Is there is any correlation between the behavior of the user today and yesterday ... Etc " temporal correlation/ temporal dependency" 
Is there is any kind of dependency between the users? 
Answering these questions can measure the quality of your feature ? 
Sometimes there is no need even to do calculations to answer these questions , if you tell us what kind of actions or activities you are looking at we may help in better way .
For example : if user 1 used his car today , there still a high prob to use it tomorrow, but if user 1 went to barbershop today there is a high probability that will not go tomorrow