I believe this classification was first enunciated by Alex Langhshur at eMetrics Toronto (2008). It’s worth expanding upon.

Consider the following classification for web analytics metrics:

  • Pre-Click Metrics
  • On-Site Metrics
  • Post-Click Metrics

To unpack that:

  • Pre-Click Metrics refer to all activities that led to a visit to a digital owned property. (E.G. paid search keywords, referring domain, any traditional spend)
  • On-Site Metrics refer to all the activities that can be observed on the site. (E.G. Visits to specific pages, graph analysis / path analysis, time spent on site, and a host of very specific things like the nebulous world of engagement.)
  • Post-Click Metrics refer to all the activities that occur after the visit. (E.G. Money getting transferred to your bank account, an email address getting opted-in by the user, and even the nebulous world of recommending the service to a friend.)

The advantages of this structure include:

  • A clear, simple, linear chain of causality from beginning to end.
  • Enables the separation of the On-Site metrics as its own intermediate variable.
  • Enables clean segmentation among visitor attributes: returning visitor, returning customer, geography, and so on.

The disadvantages of this structure include:

  • Is too linear, since it does not explicitly call out the impact of repeat visitation.
  • Is too linear, as it does not highlight the importance of loyalty and retention.
  • Is too linear, as it does not highlight the interplay between bricks and clicks.

If you draw an arrow from Post-Click back to Pre-Click, you can get a nice cycle going. There’s no reason not to. It just complicates the classification.



I’m Christopher Berry.
I tweet about analytics @cjpberry
I write at christopherberry.ca