Speed-trap and SAS Promise More Accurate Web Analytics

Here’s an intriguing claim: a February 2 press release from SAS UK touts “SAS for Customer Experience Analytics” as linking “online and off-line customer data with world class business intelligence technology to deliver new levels of actionable insight for multi-channel organisations.” But the release and related brochure make clear that heart of the offering is Web analytics technology from a British firm named speed-trap.

Speed-trap employs what it says is a uniquely accurate technology to capture detailed customer behavior on a Web site. It works by providing a standard piece of code in each Web page. This code activates when the page is loaded and sends a record of a displayed contents to a server, where it is stored for analysis.

Although this sounds similar to other page tagging approaches to Web data collection, speed-trap goes to great lengths to distinguish itself from such competitors. The advantage it promotes most aggressively is that the same tag is used in all pages, and this tag does not need to pre-specify the attributes to be collected. Yet this is not truly unique: ClickTracks also uses a single tag without attribute pre-specification, and there may be other vendors who do the same.

But there does seem to be something more to the speed-trap solution, since speed-trap captures details about the contents displayed and user events such as mouse clicks. My understanding (which could be wrong) is that ClickTracks only records the url strings sent by site visitors. The level of detail captured by speed-trap seems more similar to TeaLeaf Technology, although TeaLeaf uses packet sniffing rather than page tags.

Speed-trap’s white paper “Choosing a data collection strategy” provides a detailed comparison against page tagging and log file analysis. As with any vendor white paper, its description of competitive products must be taken with a large grain of salt.

Back to the SAS UK press release. Apart from speed-trap, it seems that what’s being offered is the existing collection of SAS analytical tools. These are fine, of course, but don’t provide anything new for analysis of multi-channel customer experiences. In particular, one would hope for some help in correlating activities across channels to better understand customer behavior patterns. Maybe it’s just that I’ve been drinking our Client X Client Kool-Aid, but I’d like to see channel-independent ways to classify events—gathering information, making purchases, searching for support, etc.--so their purpose and continuity from one channel to another become more obvious. Plus I think that most people would want real-time predictions and optimized recommendations as part of “actionable insight”—something that is notably lacking from SAS’s description of what its solution provides.

Bottom line: speed-trap is interesting, but this is far from the ultimate analytical offering for multi-channel customer experience management.