Explaining the Click Forensics ClickScore
A common question we hear is what our ClickScores mean. In this blog post I want to shed some light on how our scores should be interpreted and present a real-world ad network example.
Whether from an advertiser, publisher or ad network, the Click Forensics scoring engine evaluates every individual click from a paid click stream. Given all the required attributes that describe a paid click, the engine will score each click on a scale from 0 to 1000.
Our ClickScore is a gradient score and, in a nutshell, the higher the score the higher the value of the visitor behind it. Here is a graphical presentation of the overall ClickScore ranges:
To break it down further, a score can be both an indicator of fraudulent or unwanted click activity and a proxy for the likelihood of conversion. Any ClickScore below 100 indicates an invalid click which is typically considered machine-generated traffic or click fraud, while any score above 100 indicates a valid click.
The propensity of conversion increases with a larger ClickScore. A score of 500 denotes a special position in the gradient range as it represents the average odds of conversion based on our training data set of billions of clicks. Scores above 500 indicate a higher than average propensity of conversion. Likewise, scores below 500 denote a lower than average propensity of conversion.
Though our service assigns a score to each individual click, our customers often prefer to look at aggregate scores across a large set of clicks. Depending on customer usage needs, these aggregates can be based on specific sources (e.g. where the traffic is coming from) or on specific attributes (e.g. a particular bid keyword).
For example, our customers like ClickScore distribution charts as a means of traffic quality visualization. In such a chart each individual ClickScore counts towards one of ten buckets. Each bucket represents a score range of 100 points. Below please see a real-world chart from an ad network customer. This chart represents all traffic received from a specific publisher over a the course of a day:
Overall, this publisher can be considered a good traffic source with an average ClickScore of over 500. The majority of the traffic is on the right half of the distribution chart, though there is a significant invalid portion.
For an ad network, such a traffic analysis offers actionable business insights that helps improve traffic acquisition costs from downstream publishers. Further, within a specific traffic source an ad network can identify the invalid traffic referrers to provide input for filtering, routing and pricing decisions. When managing large ad networks with thousands of publishers, detailed downstream traffic information becomes a valuable business asset.
I hope this blog post has helped to shed some light on our ClickScores and also how they can be used by an ad network. If you have any further questions about our products and services please don’t hesitate to contact us.

