Forum Moderators: martinibuster
If you need to do that to kick out the MFA's you'll have to start again before you get to the end of it.
A better approach were to show us a lost of ads/advertisers they chowed in the past day on our adsense account, make it a matrix that also says in what geo location it was shown.
The info is there already for the scammers through the rpeview tool,now please give the regular people access to the info they need to combat the scammers.
Is there an answer between those two positions?
Yes: sampling. Like most websites in the known universe, there's a power law (~1/N) distribution of traffic across pages in my domains. So, it takes a relatively small amount of time (though I would dearly love to replace some of that time with automation) to do regular samples of who is advertising where in a manner that accounts for the majority of ad displays. I can use this to detect trends, assess the relative importance of advertisers, etc.
In addition to sampling based on traffic volume, I also try to detect outliers, like a low-volume page that gets some weirdly high CPC once in a while, to try to locate opportunities I could exploit if I had more of the right kind of content. Unfortunately, the limited number of Google channels makes this difficult to do well. Getting a decent sample of a low-traffic page requires tying up a Google channel for an extended period of time. I really need an automated system that lets me rotate a certain percentage of available Google channels through the low-volume pages, searching for "interesting" results.
Knowing who my best advertisers are and what products they appear to spend the most money promoting regularly influences content development, sometimes in small ways (choosing which keywords to SEO for -- or at least not omit), sometimes in large (choosing what topics to write about). I am never at a loss for what to write about next; if the weblog data isn't screaming it out, then the advertiser data is. Usually they both have something to say, and it's a matter of deciding where the best ROI likely lies.
It also makes it easier to glance at a competing website, see what advertisers they're getting that I'm not, and try to figure out why (and whether I should care).
This probably comes from my paper magazine days, where I would watch the marketing manager analyze and track competing magazines every month. There was no way of knowing precisely who paid how much for which ad (since all our competitors were willing to sell off the rate card), but one could make estimates and track trends.
Effectively, I treat Google much as I would a competing paper magazine, because they hold advertising financial details that are important for decision making. They won't give me those details, but I can make estimates and track trends, which is much better than nothing.
This would be much more difficult if I ran websites that were prone to get a significant percentage of geo-targetted ads. OTOH, I'm fortunate enough to live in a metropolitan area that does result in me seeing some geotargetted ads, and that has stimulated some regional content development that I would not otherwise have thought of.
Thanks to thinking about the Markus model of success, I've come to see that even websites that are not regional in nature can have opportunities to exploit geotargetting.
If you're a content developer trusting in the Google promise to keep you blissfully ignorant of your advertisers, then you've got one arm tied behind your back, IMO.