Would this be a statistical mistake?
No, creating multiple models is not a statistical mistake, but it's probably not what you want to do. There is nothing wrong with building multiple models, and you can conceivably derive useful information from each model. However, it would be a mistake to make many inferences about store visits from your models and try to combine them. For example, suppose you create the following model and find it to be a good predictor of the number of store visits:
storeVisits = b0 + b1*Monday + ... + b7*Sunday
If you find this to be a good model and Sunday (for example) has the largest regression coefficient, you might conclude that on average more store visits occur on Sunday than any other day of the week.
Now, suppose you create the following model and, again, find it to be a good predictor of number of store visits:
storeVisits = b0 + b1*1oclock + b2*2oclock + ...
If the regression coefficient for 2oclock is the largest, you might conclude that on any given day, on average the most store visits happen between 2:00 and 3:00.
This so far is completely fine. Where you would be mistaken, however, is to assume that you can combine these conclusions and make a reliable prediction about the number of visits during a certain period on a certain day. For example, despite what you found about the busiest day (Sunday) and busiest hour (2:00 - 3:00), concluding that on average the most store visits happen between 2:00 and 3:00 on Sunday is not a sound conclusion.