LSTM for capturing multiple patterns
Modeling is a process where you invent hypotheses and then try to reject them. Without seeing your data, there is no guarantee any of these would go far. But here are some thoughts.


90 days of daily observations seem too few, which won't lead to much confidence no matter what model you use. 


1.1 If you treat each user as one sequential observation, yes your dataset is sufficient for models like RNN. But ask yourself the question, are there users out there who are unique and different and you don't know about how they differ from the rest.  


You seem to start with a belief that there are three types of users. I would suggest don't. Let the data tells you how many types are there. And for the purpose of predicting future usage, you don't care about this type information. 
Recurrent neural net is a good choice if you believe the user behaviors are temporally dependent individually and have cross-sectional dependence. Otherwise, a time series model like ARIMA may do a better job and require less resources to fit.
Lastly, if you want to normalize the data, you should do so by subtracting each row by the row mean and divide by the row standard deviation. By adding the mean as the first observation, you're going to confuse the neural net and it's not smart enough to treat this data point differently. Unless there is a huge variance across users, I would start without normalization, fit a model, figure out some sort of out-of-sample MSE, then repeat on normalized data.


EDIT : There is no reason to train multiple RNNs on this set of data if you do not have a good segmentation argument.