Jose reviewed an interesting journal article: “Path Data in Marketing”. You can read it at that link, at the Web Analytics Assocations’ Research Committees’ Peer Review Journal’s Project.

And, it got me into thinking more about path analysis.

To a web analyst, traditionally, a path analysis is examining a sequence of pages that were viewed. Back in the nineties we used to call such analysis ‘threading’: and we always chose to examine pages and the sequence.

Threading was computationally expensive during the nineties, when volume was low, and it continues to be very computationally heavy for vendors to this day – even with improved algorithms: the volume hurts.

How much does Page Path Analysis (Maddeningly: even when we use the word “click path analysis”, we don’t really mean ‘click’, we mean ‘pageload’.) really tell us these days anyway?

On highly interactive density sites: not much. One of the most common behaviors on websites is the single page visit. On this point, we treat all ‘bounces’ like a fail, when in reality, I think there needs to be a differentiation between a ‘bounce rate’, and a ‘reject rate’.

It’s entirely possible for a user to visit a website, engage with the copy, and leave to research more (a single page visit) . To the web analyst, that single visit is as a fail. Granted, a more seasoned web analyst would have a filter to differentiate return visitor conversion from first visitor conversion. If they could get that filter or that dimension cut. (Not to trivialize it: but it IS hard in some organizations).

Alright, so this, naturally, goes back to what’s wrong with the ‘time on site’ metric too. A single page visit, with 30 minutes worth of active engagement, would still be treated by most web analytics tools as being a bounce. If you believe what you read in some books, you’re told that a bounce is a fail all the time. It is not a fail all the time.

If you’re a blogger, for instance, you want to know that people came, they read, they screwed off. I’m not hurt if people don’t want to check out my other posts. My other posts are not relevant to everybodys’ interests. I’m successful if people are still skimming by the last paragraph. At present, I can’t see that. Some bloggers want you to click on their ads, because that’s how they get paid, and so, that’s a different form of path, isn’t?

When we only hit the web analytics server with a pageview, then all we’re measuring is pages. And there’s more to the path than pages.

There are specific actions that a user does that could be very indicative of interest and/or success. For instance, if a user lands on a page, and leaves before a 7 second load flag has been fired by a piece of Javascript, then we can treat that as a rejection. If a user lands on a page and scrolls slowly, the odds are good that they’re reading. If the mouse activity is heavily highlighting, or click on images – then that’s indicative of engagement. (For instance, we’ve trained users over the years to expect that when an image is clicked upon, it becomes bigger – for more detail). If a user begins filling out a form, or engages with a piece of JQUERY or an adverarea – then that’s another form of success. If the user has scrolled down as far as they can scroll: that too is success to some people on some pages.

In fact, the very idea of the ‘microgoal’, or ‘goal of a page’ is something that should come on back. The notion that some pages are routers, some are converters, some are completers, some are decision-support, and others are branding – I think is important. You WANT people to spend a lot of time with branding pages – but you don’t want people leaving at a router page. Sometimes you want people to take a very specific action on that specific visit – and you want them to take another action at a different time.

The broader challenge of path analysis is journey analysis. Stringing individual paths together to understand how multiple visits, over time, add up to a buying or a desired action. This, of course, is totally hard because people frequently begin and end their journeys on different devices at different times. That is not to say that we can’t measure the broader patterns and do the legwork to unify each part of the path.

That said, it would be far easier to take on journey analysis if we had a more robust path analysis available.