The choice of the statistical method most appropriate for your data and goal are up to you, but from what I can tell from your description it sounds as though a Generalized Estimating Equations (GEE) model might be worth considering. If you have multiple measurements of demand over time for each store, you might assume that the measurements within a store are more correlated than measurements between stores. A GEE model allows you to account for correlation within clusters or subjects (stores in your case). See the examples in the PROC GEE documentation in the SAS/STAT User's Guide. You would need to choose an appropriate distribution for your response variable (demand) to be specified in the DIST= option. If this is strictly a positively-valued variable, then the gamma or inverse gaussian distribution is often used, generally paired with the log link function (LINK=LOG option). Note that those two distributions do not allow for zero values. You will also need to choose an correlation structure for the form of the matrix of correlations among the measurements within stores. Since GEE protects against incorrect choice of the structure, simpler structures are often used. The LSMEANS statement can be used to compare the levels of intervention. GIven all of that, the code might look something like this.
proc gee;
class store interven;
model demand = interven / dist=gamma link=log;
repeated subject=store / type=exch;
lsmeans interven / diff ilink cl;
run;
