Web Support Blog

October 5, 2006

Assumption: The Content Exit Page

Filed under: Content Effectiveness, Web Analytics — Chris @ 4:55 pm

I made some assumptions in my whitepaper on Content Effectivenss and Functionalism, so I want to discuss them over the next few weeks. The first assumption is that most users of your site will exit after reading (or viewing) the information they need, or when they give up. This assumption means that the best way to validate whether your content was effective or not, is to look at what the customer did after reading it. This is a model I have followed for years, but was recently supported by Elliott Masie.

As Mr. Masie pointed out in his discussion of Fingertip Knowledge (see my post: Fingertip Knowledge: Learning-on-Demand), people are more and learning as they need it. So, they are not reading ahead and learning what they may need later, rather they are searching for an answer once they run into an issue. Likewise, once they find the answer, they are going back to their original task. Take for example the case of a user who sent a document to their laser printer, and it jams. The user was not trying to learn all about the printer and specifically jams, the user was working on a document and wanted a printed version. Therefore the user will go to a support site, try and learn how to correct the jam problem, and then return to their real work.

You can apply this same concept to other support situations too. Take the example of the user who is writing code and he or she runs into a problem — perhaps a syntax issue. They go to Google, enter related terms, filter through the results. Once the user finds the answer, he or she does not continue this process, they go about their original task — writing code. So, from the perspective of the support site, they see the last transaction with the user is reading a piece of content.

If you are still with me, it is easy to then apply Functionalism to measure how effective an existing piece of content is (our Explainer/Converter page). Divide your Exit Rate by the number of times the content has been viewed and you will get a percentage (result x 100). The higher the percentage, the more effective the content. For example, if you have 1000 views and 10 exits then the rating is 1%; likewise, 1000 visits with 100 exits is 10%. This is an easy way to identify valuable content. Remember to also use the Exit Propensity concept: look across all your content and identify your worst offenders. I would encourage you to consider some weighting too — i.e. apply the formula to your most frequently viewed content instead of all your content.

Back to our formula (Exit Rate / Page Views * 100) If the percentage is low, then you need to look at other measures. First, consider the causes. Likely if users are viewing the content, the title and description was compelling enough for the user to click-through. So I would consider these possible causes:

  1. Mismatch between title and description and the actual content
  2. Content is outdated
  3. Content is incomplete
  4. Content does not written at the users’ level or too complex

All of these symptoms are difficult to diagnose without having an expert evaluate it — which can be expensive. Therefore if you have a lot of this, it may payoff to look at your creation process. (We will save this discussion for another time.) With that being said, there may be some cluses to look for. If users refine searches and look at other content on your site after viewing this content, it may be a sign of mismatch or incomplete content. If users have a tendency to spend a long time with the content, then it is likely at the wrong level or too complex. Through process of elimination, you should conclude that other content likely fits the outdated category.

Enough for today… I think this gives a lot to consider how it could fit into your organization. Again, I will continue to address the assumptions in the whitepaper over the next few weeks. Later, I will dig deeper into the analysis and diagnostic issues, so we can make corrections based on what the data is telling us.

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