Classification vs Regression Model what should I choose?
The segmentation sounds rather arbitrary if it is simply a reflection of the revenue you are trying to predict rather than having been derived from other bjective data that then predicts revenue it is unlikely to add very much (though obviously past revenue is a good predict of future revenue - the question is does the sementation improve the prediction vs just using the past data.

So if it were me I would be inclined to do the following:

(1)  try some dimension reduction and/or clustering to explore whether or not you really have meaningful customer segments rather than just an arbitrary cutoffs by revenue.

(2) if you have a good segmentation try creating a customer classifer to predict segment memberhip

(3) if your classifier has some value use the (non-linear) segment prediction as a additional input to a regression to predict revenue.

(4) validate and/or test the model against some reserved data not just the main data set.

With any regression though correct model specification is vital. So the first thing you need to do is explore the dataset (plot lots of stuff against lots of other stuff, look at some distributions, etc) to try and understand its structure before you specify a model.

Particulary, if your primary data source is the time series of past sales you probably need to look at including mean-reversion and momentum terms in the specification (see ARM, ARIMA etc models ).

(5) It would also be fun to try a Recurrent Neural Network, though I have never done this myself.