Fun with Web Metrics

What's going on?

Then and Now

A few years ago I was asked to write a series of articles on web metrics for the National Science Digital Library (NSDL).

I’ll be revisiting all of the topics in these papers (especially path analysis) over the coming months.

What’s interesting (to me at least) is that most of the issues/challenges/insights haven’t changed much in this time:

  1. Usage doesn’t often follow a “simple” pattern: time on site isn’t monolithic: for most information-driven sites, there’s a distinct bimodal distribution of very short visits and longer visits.   One thing that has occurred to me (and something I’ve been meaning to follow-up on) is to see if there’s a characteristic change in behavior with repeat visits by the same user (insofar as I can determine that it’s probably the same user), chiefly the behavioral shift from discovering what the site has to offer and “getting your feet wet” in terms of the navigation and user interface, to more-frequently jumping to specific places on the site for quick information retrieval.   For classroom web usage, that behavior might manifest itself in searching for materials on a specific topic, bookmarking it, and then using those materials in class (or in an assignment) later on.   (Another interesting experiment would be to see if specific content is accessed from the same rough location within a short span of time – this would’ve been harder to do in the past, but geolocation services are readily available – so add another “project” to the to do list!)
  2. Path analysis is still tough to “crack” – even if thousands of visitors are coming to the site with the same mindset, they aren’t hitting the same content and they don’t get to that content in the same way (and that’s also influenced by how the site’s information is laid out, the UI and navigation, etc.).   I’ve made some headway on this, and I’m still doing tests when I have the time…
  3. Even though metrics has been a hot topic for several years now, there’s still an over-reliance on both scalar data (i.e., single values, esp. averages from which we’re somehow supposed to infer deep meaning), and “Top 10” lists (which can identify “hot” topics) but typically without the follow-up demographic or time serial analysis to discover overall usage patterns.

In other ways, things have changed.   For example, there’s less “terror” involved regarding registration (thanks to social networking sites), although the underlying issues of privacy and attempts to otherwise compromise one’s security have grown, not lessened.   Putting those aside for the moment (because our intentions are completely noble), it gives us FAR more information to work with.   Even so, other developments (such as geolocation services) means that the “anonymous user” isn’t quite as anonymous as they were in the past.

To quote Spiderman: With great power comes great responsibility. …  🙂


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September 14, 2010 - Posted by | NSDL |

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