Will Z-scores help me compare my variables? I have two variables:
1) Percentage of population of different geographic regions which has bought a certain category of product (say hair sprays) in the last MONTH
2) Percentage of population which has bought a specific brand of hair spray in the last YEAR.
I am trying to in which region the brand is doing better compared to the general use of hair spray, and in which it is doing worse. 
Since the first variable is for a month and the second for a whole year, i can't just compare them like that. So I thought i'll transform them into z-scores, and take the difference between the two. Question: Is that a valid approach?
(Side question: the distribution of the second variable has quite a strong positive skew, while the first is fairly normal. Is that a problem?)
Thanks in advance for any advice!
EDITS in response to questions:


*

*For variable 2 I only have data for that one particular brand, so I
can't aggregate across brands. While variable 1 is only for the whole product category encompassing all brands - can't dissaggregate that either.

*Sample size is over 100 geographical regions

*I am attaching a scatter plot of the two variables. One of
which shows variable 2 on a log scale. 

*Just to clarify, I am not so much interested in the overall relationship between the two
variables, but more in arriving at a metric that will tell me for any
specific entity (region) how well it is doing on variable 2
(sales of specific brand) compared to variable 1 (sales of general
product category). Now, of course, I can see that maybe the
regression/scatterplot approaches suggested might be an intermediate
step to achieve that goal. But how do I progress after that?


Thanks so much for the suggestions so far though! 

 A: OK, from your comments, it seems you have data on one brand across several regions, both for one month and for one year. 
Comparing your brand in one region for one month vs. one year is probably not useful; you would have N = 1 and no way to estimate variability. You could multiply month by 12, but if there was a difference between month and year you'd still not really know much - is it a property of the month? 
More likely, you want to look across regions. This could answer a question such as "does our brand do better in region X vs. region Y?" For this, I'd look at two separate regressions, each with "use" as the dependent variable (either for month or for year) and with "region" as the independent variable.
This assumes that your data across regions is in comparable metrics (that is, not total number of people). 
EDIT IN RESPONSE TO COMMENTS:
Given what you've said, I think the best thing is to not transform the data. First, form a ratio for each region: Your brand sales/total sales (you can multiply your brand by 12 if you want to, but it isn't critical). Then use that ratio as the dependent variable in a regression with region as the independent variable
A: I thought I'll report back after I have done some more research on this. The conclusion I draw from this that that two approaches could be taken.
1) Using Z-scores, as initially proposed. When intorduing Z-scores many statistics textbooks give the example of comparing grades in two school subjects,  e.g I got  70 points on a math exam and 85 points in history, but math was the more difficult test. So how do i know which subject i am better in? One converts the socres into Z-scores and then i can compare them. A scholar.google search shows that a number of scientific papers have used this approach for similar problems in all sorts of situations. It appears to me close enough of an example to the problem at hand here too.
2) Using regression. Run a regression of variable 2 on variable 1. The regression line (fitted values) tell me where the sales of the specific product in a region should be given the general sales of the whole product category there, based on the overal relation ship. The difference between that and the actual datapoint (residual) tells me how well the region is doing.
