The most interpretable, widely used, and easy to implement approach is a simple difference-in-differences model. Here, use linear regression with fixed effects for site (or "geospecific groups" as you call them), fixed effects for continuous amount of spending, and baseline revenue. Model as an outcome the new revenue. Consider log transforming the outcome, since markets, volumes, and other denominators may vary between sites in a way that fixed effects do not explain. Calculate confidence intervals for the "spending" variable to see by what percentage revenue increased for each $1,000 spent (or other suitable contrast) from baseline.
As an exploratory analysis, calculate interactions between the spending variable and site to see if the trend (if any) is driven by site-specific historical trends. This will assess the validity of using each site as its own historical control, among other possibilities. A limitation is having only 1 control site with 0 spending increase (which is what I assume you mean by curtailment).
Your rationale for using a non-parametric test doesn't make much sense: the comparisons aren't being made explicit, you don't estimate a meaningful effect, and there is a loss of efficiency in using rank based tests when linear models would be even approximately well suited.