The key limitation, if I have understood things correctly, is that you currently can’t use segmentation within the API.  Which is fine when you are getting general numbers out but not when you need to create a segmented dashboard across a dozen metrics or so.  It also means you cannot get visits to groups of pages, e.g. visits which saw a product page.  However, there are a couple of workarounds.

For the first issue, you need to return to the old system of creating a profile per data segment.  So, as I have recently done, create a profile for New Visits, another for UK Visitors, one for Paid Search and so on.  Then, with your segments already created, you can easily extract top line numbers from each profile and combine to create that segmented automated report.

The second issue can be resolved through the use of goals.  Two key points to remember are goals can be created based on Head Match or Regular Expressions for page names and that they can only be triggered once per visit.  Given this, the number of goal conversions is suddenly equivalent to the number of visits in which a group of pages was viewed e.g. set up a goal for view a product page and the number of goal conversions is the number of visits in which a product page was viewed.

The API has not yet been updated with the upgrade to 20 goals so you can currently only use the API on the first 4 goals but hopefully that update won’t be far away.  Along of course, with the ability to access segmented data and also to extract the numbers from within funnel visualisations.  In the meantime, I hope these two tips are helpful.

Due to cookie deletion and multiple computer usage, it is difficult to get a true picture of the split between people who have never seen a website before and those who have.  However, recording whether the visitor had a cookie from this website previously does at least give an indication of this new/returning split.

What I like to be able to do is to segment out new visitors for a time period (week or month) and examine their behaviour on the website compared to visitors who had visited previously.  The new visitor segment should include all visits during that time period by these visitors, not just their initial visit.

Frustratingly, this information is usually not available as default in a web analytics tool unless you can segment at visitor level.  However, as long as you have one of the four metrics from New and Returning Visits or Visitors, you can calculate the other three.  And most tools will give at least one number.   As examples:

• Google Analytics gives New Visits and Return Visits
• SiteCatalyst provides Return Visits
• HBX contains Returning Visitors

The key to this is knowing that the first time a site is visited, that is both a new visit and a new visitor.  And as any subsequent visits by these people will be reported as a return visit, the number of new visits equals the number of new visitors.

With that logic in mind, it is simple to calculate all four metrics once you have a single one.  For example, assume that the tool available is SiteCatalyst (without access to visitor level segmentation via Data Warehouse or Discover):

• The number of Return Visits is available but none of the other three metrics
• Total Visits minus Return Visits gives New Visits
• New Visits equals New Visitors
• Total Unique Visitors minus New Visitors gives Return Visitors

And now it is easy to calculate the proportion of Visits that were New or Returning or to calculate the proportion of Visitors that were New or Returning.

The same principle can be applied to Google Analytics:

• New and Returning Visits is available (note that this metric is visits, not visitors as it is titled in the report)
• New Visits equals New Visitors
• Total Unique Visitors minus New Visitors gives Return Visitors

Of course, these numbers don’t mean that much on their own but do become more useful when trended over time or across different segments.

An interesting thing to look at can be the split in New and Returning Visitors for different time periods – day, week and month.  This can indicate the scale of the issue with cookie deletion, but more on that another time.

The last 9 months with Logan Tod has been very interesting.  Learning how to use HBX and Omniture SiteCatalyst and increasing my knowledge of Google Analytics.  Focusing my attention on looking for insights in the data which can be used to recommend improvements to websites or marketing.  Broading my experience with the range of websites that I have worked on and the nature of the work.

I still like to keep it simple though.  I think with the vast amount of web analytics data available for any website, it is easy to get lost in it.  And if you dive in too deep too quickly, it can be difficult to find anything as you are surrounded by too many pieces of data.  I think it is best to start simple, with the key actions or conversion points on the site, the top line metrics.  An understanding of these will generally lead to fairly basic questions about the performance of the website.

But answering these questions will lead to more and increasingly more complex detailed questions about performance and an understanding of the factors that truly are impacting on the performance and success or otherwise of your website and business.  The data itself and an exploration of it can lead you to map out what is important to know in order to make improvements.

One of the more frustrating parts of learning HBX and SiteCatalyst was the discovery of the basic reports that I feel are missing.  In particular, there is not a simple report that gives visits (or even responses) by traffic source.  Having to try and pull data from various sources into a single excel report (as the alternative) is painful with question marks over the accuracy.  SiteCatalyst has the option of Unified Sources via a Vista Rule, this needs to be mandatory for anyone using SiteCatalyst and I wish it came out of the box.  While I have various workaround for HBX, I finally came up with a version using multiple segments where I am satisfied with the accuracy, but no where near the ideal solution.  All tools that I have used need to work harder on getting the balance right between reporting performance and allowing analysis.  I know analysis leads to insights leads to recommendations leads to improvements leads to more money but simple performance reporting highlights where and when the analysis should occur.

Anyway, likely more on that in the future.  I might need to be careful or it could be too easy to write about the philosophy of web analytics ahead of the practicalities of it.  And I need to get a new simple WordPress theme.

Perhaps unsurprisingly, they are facing many of the same issues as those reported by large organisations with e-commerce websites.  The key issues are:

• no clear objectives from stakeholders
• lack of time
• lack of support of internal stakeholders
• insufficient knowledge of how to use web analytics data to make business decisions.

Given this, I aimed my presentation at addressing these issues, hopefully providing practical suggestions for actions that can taken to make web analytics more actionable within their organisations.  This presentation that I gave can be found below.  While it was directed towards universities, I believe the suggestions would be relevant for any organisation.