MPath Analysis: A process of determining a sequence of pages visited in a visitor session prior to some desired outcome (a purchase, a sign up, visiting a certain part of site etc). The desired end goal is to get a sequence of pages, each of whom form a path, that lead to a desired outcome. Usually these paths are ranked by frequency.

Is doing Path Analysis a good use of time? In my humble opinion the answer is a rather emphatic no, except for one exception (which I’ll discuss below). Almost always Path Analysis tends to be a sub optimal use of our time, resources and any money that is expended on buying tools that do “great” Path Analysis.

wWe usually strive to do Path Analysis in a quest to find this magic pill that will tell us exactly what “paths” our visitors are following on our website. If they “follow” the path we intended we celebrate.

If as usually it turns out that our visitors don’t “follow” the path that WE want them to follow then it back to the drawing board to redesign the site structure / architecture to get them to “follow” the path or at times, worse, hours of “analysis” on: what the heck were they thinking when they click on this button or go to that page (bad customers, bad customers!).

Challenges with Path Analysis are:

  • Imagine a website with five pages. Page one Start, Page five Finish. With a simple visualization in your mind you can imagine the number of paths that a visitor could take. Now imagine a website with 100 pages, now one with 5,000 pages. The number of possible paths quickly becomes infinity (well not really but you get the point).
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  • Most of our tools do a terrible job of representing this path: click forward, back to home, click forward, reverse to three pages ago, hit buy. In a world of linear path representation at a page level this is really hard to compute, even harder to depict. Yet this is exactly how our customers browse our websites.

  • On most websites the most common path is usually followed by less than five percent of visitors, usually 1%. As responsible analysts could we make any decision on something such a small fraction of site traffic is doing?

  • Even if the most common path is followed by 90% of the visitors current Path Analysis has two fatal flaws:
    • It can’t show / say which page in a series was most influential in convincing a customer to move on.
    • Current tools aggregate traffic into one bucket, when in reality each segment of traffic behaves differently (say DM traffic vs SEM vs “bookmarks” vs Print Ads). Segmentation is always key.

All of the above combine to make it quite sub optimal to glean any actionable insights that will lead to making our websites more endearing to our customers.

There is one exception to this rule. For structured experiences such as a Checkout or a Closed-off DM Landing Page experience (no navigation, just Next – Next – Next – Submit) Path Analysis can identify where the “fall off” can occur. Once that is identified we will still not know the Why (see Qualitative Metrics Post) but Path Analysis is helpful.

Here is example of new way of thinking about “Path Analysis” that I think is heading in the right direction. (Please see Disclaimers – Disclosures first.) There are atleast three more things I would like to see fixed in this version but ClickTracks address some of the usual fatal flaws here.

  • cIt is possible to break down a linear process into one in which we can group a bunch of related pages (say all product pages) into “groups”. This helps fix the problem of linearity because customers can go from A to B to C or C to A to B and it does not matter for related content.

  • It is possible for Visitors to show up in any stage at any point (this is actual behavior now with SEO influencing where people land). Google Analytics also has this feature(please correct me if others do as well).

  • Perhaps the cutest thing is that it shows which page in the “Path” is most influential in moving people to the next stage. This is awesome because one can simply look at the “darker shaded” pages and know, for example, that no one cares about system requirements but rather the page on our 10 year no questions asked return policy is the most important one in convincing people to add to cart.

  • It is also quite easy to view how different segments are influenced by different content, in my unreadable screen shot you can see All Visitors vs Visitors from Google. Imagine this intelligence then turned around and applied to personalization (!).

This is not perfect but getting there and I think all the vendors will soon coalesce around this innovation and we will all be greatly empowered. 

Path Analysis as it is practiced currently ultimately is like communism (with sincerest apologies to anyone in my audience who might be offended). There are overt/covert  intentions to control things, to try to regulate, to say that we know better than you what you want, to push out a certain way of thinking. I know this sounds extreme, and it is but simply for shock value and not to offend anyone.

The web on the other hand is the ultimate personal medium and one in which we all like different things, we all have specific preferences and opinions and a certain way we want to accomplish something. The beauty of the web is that all that is possible and cheaply with easily accessible technology. So why do typical Path Analysis and why try to “push” a certain way of navigation / browsing / buying? Why not get a deep and rich understanding of our customers and then provide them various different options to browse our website they want to and get to the end goal the way they want to.

Why not let democracy flourish? On our websites and in our customer experiences?

Agree? Disagree? Does Path Analysis work for you? Please share your feedback via comments.

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