Back in July I missed this great post by Dave Morgan at AOL but thanks to Danah Boyd’s post it has surfaced again.    The findings are very surprising and very relevant to anybody running click or online advertising campaigns.   Dave summarizes the findings very concisely as follows:

We learned that most people do not click on ads, and those that do are by no means representative of Web users at large.

Ninety-nine percent of Web users do not click on ads on a monthly basis. Of the 1% that do, most only click once a month. Less than two tenths of one percent click more often. That tiny percentage makes up the vast majority of banner ad clicks.

Who are these “heavy clickers”? They are predominantly female, indexing at a rate almost double the male population. They are older. They are predominantly Midwesterners, with some concentrations in Mid-Atlantic States and in New England. What kinds of content do they like to view when they are on the Web? Not surprisingly, they look at sweepstakes far more than any other kind of content. Yes, these are the same people that tend to open direct mail and love to talk to telemarketers.

What does all of this mean? It means that while clickers may be valuable audiences, they are by no means representative of the Web at large

Indeed, this means that many online marketing campaigns may need to dig a lot deeper to obtain a positive ROI, and for some campaigns positive ROI is not attainable.    If, for example, irrelevant clickers (not to be confused with click abuse) mean you’ll have to spend a few dollars to reach a single prospect, and your margin on your product is only a few dollars, you may be fighting a losing PPC battle for online hearts, minds, and pocketbooks.    On the other hand if your target audience is, say, midwestern stay at home soccer moms, you may want to up your PPC spend dramatically because your nickel or dime per click could be worth many times that in prospective sales.

Obviously Dave’s post is only the beginning of the big story which has yet to be written,  and I’m not clear how representative this sample was of all PPC activity (I think it was broadly representative though – they looked at billions of data items).  However this helps me understand why some of my PPC experiments have failed to yield much of a return.     A good travel experiment given these findings would be to look at midwestern travel patterns and try to advertise popular packages to Mexico  or other commonly travelled points south in the winter.   Since women are the main travel planners this match could work well to increase the normally very low conversion I have seen on travel related PPC spends.