How to rank observations with two variables (website performance) I have no background in statistics, so I find myself confused by this simple problem. I'm not even sure which search terms to use. 
I have some website performance data. I have the number of times a page was requested/visited, and how long it took to load (average time and percentiles in seconds).

I want to identify pages that are often requested/visited but take a long time to load. So for example, if an "FAQ" page takes 30 seconds to load but is only visited a few times a day, this is less important than a "Search" page that takes 2 seconds to load but is used thousands of times a day. The purpose is to make a "Top 10" list or something like that which some website developers will use to improve performance.
I had thought to do something simple like create a new column with NUM_TRANSACTIONS * AVERAGE and sort that, but I'm wondering if there is something wrong with this approach.

SOLVED: Edgar's answer worked well for me. I created the plot he suggested, and the scoring mechanism, and they produced similar results. The number markers indicate ranking.

 A: NUM_TRANSACTIONS * AVERAGE is the total time spent loading this page (e.g. page 1: the sum of all 891 343 loading times of this page), since AVERAGE = TOTALTIME/NUM_TRANSACTIONS. This is not a good measure for your problem, since you lose the information which pages are requested frequently.
For the start, it might be helpful to plot the NUM_TRANSACTIONS versus the AVERAGE time to load (since we don't know anything else about the pages like number of images/javascript code/whatever) and inspect visually if there are unusually high AVERAGE loading times associated with high NUM_TRANSACTIONS.
Alternatively, if you want to identify the Top 10 problematic candidates, you could find the overall average loading time: TOTALAVERAGE = (sum of all (NUM_TRANSACTIONS*AVERAGE))/(sum of all NUM_TRANSACTIONS), then sort your pages by decreasing NUM_TRANSACTIONS and take the 10 topmost pages where AVERAGE is above TOTALAVERAGE.
Bulding on this, you could calculate a rough SCORE = NUM_TRANSACTIONS*(AVERAGE-TOTALAVERAGE) for all pages and take the Top 10 with the highest positive SCORE.
