The Answer to Click Fraud?

CNET explains how the recent $90 million click fraud settlement by Google does not mean we won’t see future law suits.

Hidden in the article is what I feel is a viable solution…

Some experts say the solution is to have an independent auditor that would use data from the search engines and advertisers to determine in a neutral environment whether clicks are fraudulent.

Everyone – the search engines and advertisers – should embrace the idea of an independent audit of click activity. By independent, I’m not talking about a SEM firm or one of these “click fraud detection” firms that are popping up with claims of 35% click fraud. I’m suggesting a truly independent group that has no incentive on what amount of invalid click activity they find.

Until advertisers are willing to open up their campaign info, and search engines are willing to share their data, we’ll always have this dark cloud of doubt hanging over the paid search industry.

  • Anonymous

    Part of the problem lies in the area of determining a fraudulent click.

    Some may define a possible fraudulent click that lands on a page and doesn’t go any further, as some click fraud patterns may indicate. But we all know that we’ve gone to pages before and it’s just not the information we were looking for and turned back.

    Others may define a fraudulent click as more than one click from the same ip address in a given period of time. “Fraudulent?” Maybe. “Invalid,” yes.

    My point is that there’s a lot of math that can go into determining different click patterns of invalid clicks that should, in effect, be reimbursed. If an independent party is to audit clicks and, say, assign a percentage that gets reimbursed to advertisers on a continual basis, these click patterns will have to be agreed upon by both search engines and advertisers before doing so. With the process of reaching this agreement, and some click fraud companies having patented detection methods, this effort could become quite a bit more difficult than it may seem.

    Ben