Is there ever a reason to solve a regression problem as a classification problem? Would there ever be a reason to repose a regression forecasting problem as a classification problem by, for example, using classes to describe if sales in one year are 10%,50%,90%, or >100% of current levels. Those things could naturally be inferred from the results of a regression, yet I have seen people doing such classifications that actually feels more like regression problems.
I'm a newbie to the forum and ML in general so I hope I did everything right when posing the question :)

In general, there is no good reason. Grouping the data as you describe means that some information is being thrown away, and that can't be a good thing.
The reason you see people do this is probably out of practical convenience. Libraries for classification might be more common and easily accessible, and they also automatically provide answers that are in the correct range (while regression for example can output negative values etc.).
One slightly better motivation I can think of is that the typical outputs of  classification algorithms can be interpreted as class probabilities, which can provide a measure of uncertainty on the result (for example, you can read a result as giving 40% probability for the range 10-20, 50% for the range 20-30 etc.). Of course regression models can in general provide uncertainty estimates as well, but it is a feature that is lacking in many standard tool and is not "automatic" as in the classification case.
