Posted by: Pete Horadan So I’ve laid the foundation for the idea of unique signatures, now let’s look at them in more detail. On the access side, we start by creating a Device Signature for each machine that accesses an application. We track over 80 unique attributes of the machine, and utilize a proprietary statistical method for understanding the patterns and the evolution of change to the patter for a given machine. Using this approach, we can tell with great accuracy, which machines accessing a system are unique and create associations between devices and visitors. Another of our algorithms—keystroke dynamics—can actually get to the individual level if required. Keystroke dynamics works in conjunction with our Device Signature, analyzing visitor-specific typing […]
Tag Archives | Accuracy
Demand Ratings™ Done Right Part 1 — Signature Algorithms
Posted by: Pete Horadan I talked earlier about the insufficiencies of current tracking approaches and their impact on behavioral analytics. Let’s now talk about how it should be done—how we do it here at Scout Analytics. Basically, our technology is centered around a unique signature concept. We analyze dozens of unique attributes and utilize patent-pending algorithms to derive a unique signature for the device, network, even the individual user (biometric). You can think of signatures sort of like a genome—while there are overlapping characteristics—each carries a unique and identifiable pattern and sequence. Of course, this isn’t easy or everyone would be doing it. As you can imagine, machines, networks and even people change in different environments and over time—you install […]
Online Metrics Done Wrong Part 2 — Tracking IP Addresses
Posted by: Pete Horadan Last week, we looked at the serious acurracy issues associated with using cookies to track visitor behavior and engagement. This week we’ll explore the accuracy of another often-used factor, IP address. In short, IP address provides even less information about users than a cookie. The accuracy issue starts with the fact that one user may have many IP addresses. For an example, let’s look at me. I access different sites (e.g., TechCrunch) from home through my personal broadband, while at my coffeehouse through their broadband, or through the network at my corporate office. When I’m on the road, I broaden my access points to include hotels and airports. When using IP address to count visitors, the […]
Online Metrics Done Wrong Part 1 — Tracking Cookies
Posted by: Pete Horadan So we’ve been making the point all along that Demand Rating™ provides a new lens on subscriber loyalty. Realistically though, the usefulness of behavioral analytics or web analytics are gated by accuracy. So how do you get accurate online metrics? What’s really knowable? Let’s start with usage tracking. The traditional approach is through the use of tracking cookies. A cookie is a small piece of data that is stored on a device by the application. Each new browser that accesses an application is given a cookie, and the application relies on that cookie to track user behavior. The issue, of course, is that the cookie method of tracking is wildly inaccurate, and it’s simple to see […]
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