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 addition to the good answers by users J. Delaney and Soeren Soerensen: One motivation for doing this might be that they think the response will not work well with a linear model, that its expectation is badly modeled as a linear function of the predictors. But then there are better alternatives, like response transfromations (see How to choose the best transformation to achieve linearity?  and  When (and why) should you take the log of a distribution (of numbers)?).
But another, newer, idea is to use ordinal regression. User Frank Harrell has written much about this here, search.  Some starting points:  Which model should I use to fit my data ? ordinal and non-ordinal, not normal and not homoscedastic,  proportional odds (PO) ordinal logistic regression model as nonparametric ANOVA that controls for covariates,  Analysis for ordinal categorical outcome
