normalize identification values properly
It is possible that the other variables you're feeding into the NN are simply bad at predicting sales.  Sell prediction is a notoriously hard problem.

Specifically the addressing of mapping a multi-state categorical variable to the NN's {0,1} input range:  Another idea is to change that one, 5-state variable into five boolean variables.  Rather than {0,0.25,0.5,0.75,1.0} on your one variable, make each of the five boolean variables represent a single day and make [1,0,0,0,0] equal Monday, [0,1,0,0,0] equal Tuesday, etc.  I've personally had more success both with training good networks and introspecting the network itself when spreading out states of classes like that.

Other hacks you can try:
 * Take out the the 'day' column all together and see if any of the other variables get used.
 * Plot the distribution of spend as a function of day.  Even if nothing else comes of this current model, it sounds like you've found one interesting insight already.
 * Consider also trying different models.