We recently launched a new reporting interface for our ad network customers to provide more actionable insights into downstream publisher sources. The early feedback from new adopters has been very positive. In this blog post, I will provide a brief overview of the new enhancements.

So, why did we decide to roll out a new reporting platform? As we are growing with our customers, we learned to better understand their most pressing business problems and how our products can help address those. Downstream publisher management and new publisher screening are critical to daily operations of ad networks. We were looking for new ways to tackle them in a more effective manner.

Based on insightful customer feedback we had received, we worked on the new reporting interface with the following design goals in mind:

• Present actionable insights into large volumes of click data to better highlight traffic quality from downstream publisher sources
• Easily handle daily click volumes of 10 million clicks and deliver sub-second interface response times
• Provide detailed background information about new publishers during the approval process before accepting traffic

Users of the new interface are greeted by the publisher dashboard that summarizes current network and publisher activity. It is the starting point for further investigation. At a glance, ad network staff find their top publishers sorted by different attributes, for example by volume and traffic. The “Movers and Shakers” sections highlight sources with significant traffic changes in certain dimensions. Here is a screenshot of the dashboard:

Throughout the interface, we added small sparklines to offer traffic information at a glance without having to leave the page. For example, an average click score is accompanied by a score histogram (see this previous post for more details) and a volume volatility graph. These cues help users to absorb the information more quickly and spot areas that warrant further investigation:

Another tab in the interface offers publisher screening capabilities. It supports ad networks in making better decisions about which publisher applications to approve before accepting any traffic. After entering a URL, a report will present detailed background information about the specific domain, combining reputation information from the Click Forensics community database and public sources like Alexa, Compete.com and whois.

Next to the highlighted features, we have added numerous other useful capabilities for our customers. The new reporting interface is now available to all of our ad network customers. If you would like to learn more please don’t hesitate to contact us.

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.

For our advertiser customers we are introducing an new integration option called the Click Forensics Redirect Service (CFRS). It’s an alternative approach to sharing click stream data with us and will often simplify the initial on-boarding process for new customers.

Instead of uploading web server log files, which often requires help from the IT department, customers only need to update their campaign settings to use CFRS. This change will send their click stream data with all necessary data fields to our Click Forensics scoring service.

Here is a conceptual overview of how CFRS integrates with paid search campaigns:

CFRS operates as an interstitial redirect page. It injects itself between the paid click and the landing page, thereby observing all click stream data. To enable it, customers update their destination landing page URLs to use a new format that will transparently invoke CFRS. This change will not impact the experience of end users visiting paid search advertisements.

From the initial customer feedback, we have learned that CFRS was an elegant alternative to sharing web server log file data and eased the initial on-boarding process. If you have been contemplating to improve your search marketing budget by removing irrelevant keywords and invalid clicks, please make sure to learn more about our offerings for advertisers.

Though many external parts of the online advertising industry are quite visible, there are areas that are more difficult to see. It often itches me to understand which ad serving and tracking technologies a particular web site is using.

Ultimately, this question can be answered by manually analyzing HTML source code and digging through JavaScript include files. However, this process is often quite time consuming and tedious. I have seen many people doing it over and over again, every day, for many sites.

Recently, I discovered a more convenient way with the Firefox plugin Ghostery. Created by Compete co-founder David Cancel, it is a non-intrusive, minimalist overlay box that informs Firefox users about ad targeting and tracking technologies used on web pages visited. Here is an example from the Boing Boing blog:

At a glance, I can now immediately learn about the advertisement underpinnings of every site I visit. That actually turns out to be a lot more interesting and enjoyable than I initially thought.

Obviously, the benefits I find by using this plugin are probably coincidental. David’s initial intention with creating it was to educate web users about ad targeting technologies that are tracking user behavior across sites, often with invisible tracking images and cookies. Since launching Ghostery late last year, he has expanded the necessary tag database extensively and a small community is building around it. You can also follow Ghostery on Twitter.

After using Ghostery for a couple of weeks, I can only recommend installing the plugin for Firefox if you like to understand more about the web sites you interact with. It’s a small download and quick install. Go and start watching the invisible web!