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Since click fraud detection is a rudimentary application of click scoring, one thinks of click scoring as a tool to eliminate unqualified traffic. Click scoring can actually do much more, such as determine optimum pricing associated with a click, identify new sources of potentially converting traffic, measure traffic quality in the absence of conversions or in the presence of bogus conversions, and assess the quality of distribution partners, to name a few applications. Also note that scoring is not limited to clicks but can also involve impressions and metrics such as clicks per impressions.

From the advertiser viewpoint, one important application of click scoring is to detect new sources of traffic to improve total revenue, in a way that can not be accomplished through A/B/C testing, traditional ROI optimization or SEO. The idea consists of tapping into delicately selected new traffic sources rather than improving existing ones.

Let us consider a framework where we have two types of scores:

• Score I: generic score computed using a pool of advertisers, possibly dozens of advertisers from the same category.
• Score II: customized score specific to a particular advertiser.

Scores I and II are good. This is usually one of the two traffic segments that advertisers are considering. Typically advertisers focus their efforts on SEO or A/B testing to further refine the quality and gain a little edge.

Score I is good and score II is bad. This traffic is usually rejected. No effort is made to understand why the good traffic is not converting. Advertisers rejecting this traffic might miss major sources of revenue.

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