Web Analytics Wednesday – Stockholm (Sep. 2007)

As we all anticipated; Lars Johansson (with a little help from us sponsors) put together a stellar web analytics Wednesday last night. And if possible even better that the last web analytics Wednesday - Stockholm (Mar. 2007). Great show Lars!

The main event of the evening was the Presentation “web Analytics is easy” by Eric Peterson – this including his RAMP pitch (which you should go look up on his site – it makes a lot of sense)

AND I of course enjoyed sitting in the Panel with these smart folks:

• Ian Tickle, Webtrends
• Dennis R. Mortensen, IndexTools
• Jonas Damgaard, Omniture
• Eric Peterson, Web Analytics Demystified Inc.
• Steve Jackson, Satama
  

Where we had a great debate and some really interesting questions, such as working wither either engagement metrics or buzz metrics. And the first people to ask questions were given books from Avinash, Eric .. and I brought 10 Web Analytics Shootout 2007 reports.

Other great comments from the event:

• World Tour Day Two: Stockholm, Sweden. (Eric Peterson)
• Web analytics Tuesday in Finland and Sweden proves that web analytics is hard (Steve Jackson)

Avg. Time per Visit - Standard definition

The Web Analytics Association (Jason Burby and co.) recently releases 26 Standard Definitions to Promote Consistency among us Vendors (PDF) - this is GREAT news and should make it much more straightforward to communicate web metrics among not only traditional report consumers, but also more experienced analysts! However we are not there yet ...and please see this post as an inspiration to continue the splendid work. One of the standard definitions are “Visit Duration” – specifically described as follows:


WAA Standard definition: Visit Duration

Definition/Calculation
The length of time in a session. Calculation is typically the timestamp of the last activity in the session minus the timestamp of the first activity of the session.

Comments
When there is only one piece of activity in a session (a single-page visit or singleevent visit), no visit duration is typically reported.


This is a fine definition and one that I certainly agree on. BUT then comes questions on the subsequent metric that we could call “Visit Duration Average” – first question is what to name the metric and second question is on how to calculate the metric. Before concluding anything. Let us take a look at Google Analytics and IndexTools (because that is funny enough what I use here on my blog – still waiting for those Omniture blokes to call me ;-)

- Avg. Time per Visit 4m 46s (IndexTools)
- 00:07:09 Avg. Time on Site (Google Analytics)

Two different naming conventions and two different calculations (assuming we both collect and accurately calculate the metric). *send me your definition from Omniture, Visual Sciences or other decent Web Analytics vendor and we might be able to help out our good friend Jason. I searched through my WebTrends Marketing Lab account and could not find anything. Anybody?

So first (and I am completely biased here) – I do not really think the Google Analytics “Avg. Time on Site” naming convention is clear enough confirming that we talk about a Visit (session).

But more importantly - WHY do the numbers differ?

Google Analytics discounts Bounces (or Single Page View Visits as the new WAA standard definitions calls it) and IndexTools includes ALL visits into the calculation. So to be clear, if we have:

1000 visits
300 bounces
2300 minutes spent on site

Remember that page tagging solutions like IndexTools and Google Analytics do not count time spent on site for bounces (Single Page View Visits). Thus in the above example the 2300 minutes are spent on 1000-300 = 700 visits. Even though we know for a fact that those 300 bounces (Single Page View Visits) in fact did spend time on the site.

1. IndexTools = 2300 minutes / 1000 visits = A Visit Duration Average on 2.30 minute. (or Avg. Time per Visit 2m 30s as we would write it)
2. Google Analytics = 2300 minutes / 700 visits = A Visit Duration Average on 3.29 minute. (or 00:03:29 Avg. Time on Site as my friends at Google write it)

So NOW the questions goes, who is right and who is wrong? :-)

In point of fact, I think both calculations are justified and I see a reason to include both of them. It might be a bit clumsy to start pre-segmenting visits (visits with MORE than 1 page view) as our friends from Google Analytics have done (sorry Avinash and Brian). Looking at a calculation where we discount bounces, we are actually just saying that we want to look at the, to some extent, more engaged users (those who decided to dig further into the site). I therefore prefer the original calculation (as Google Analytics used to have it as well) where we look at ALL visits. Then one can always segment to separate more or less engaged users from the others, whether that is page views or not.

As an example. Let us look at May 2007 (I can of course recreate these results with on the fly segments):

1 page views or more segment = 04.11 (the complete data set)
2 page views or more segment = 10.54
3 page views or more segment = 17.38
4 page views or more segment = 22.30

As you can see the more page views people look at the more time they spend on the site. And all of a sudden starting on 2 page views or more seems awkward.

Conclusion (this is for you guys at the WAA):
If you add another definition to your standard set about visit duration, I suggest you call it: “Visit Duration Average” and the calculation is the total length of time spent in all visits (sessions) divided by the total number of visits (session). Hereafter one can segment on page views or more intelligent engagement metrics.

Further fine comments on the subject:

• Google Analytics changes tack (iMEDIA connection)
• Google analytics- average time on site (leapfrogg)
• Google Analytics: Bounce Visits are excluded for Avg. Time On Site (Things and stuff)

Interview with Dennis R. Mortensen, COO of IndexTools – by Lars

Lars is somehow the de-facto European president of the WAA – or at least the one European WAA voice I hear the most :-) .. nevertheless; he has done some tremendous work in setting up Web Analytics Wednesdays in Stockholm (I attended a Web Analytics Wednesday in Stockholm – which had a whooping 70 something attendees)

I did an interview over at his blog the other day and you can find it here:
Interview with Dennis R. Mortensen, COO of IndexTools

About the following subjects:

• What makes IndexTools unique?
• Do you think the new release of Google Analytics increases your need to be innovative? If so, how?
• What share of your clients are located in Europe?
• If IndexTools didn’t exist, which tool would you use and why?
• What is your biggest challenge in 2007 and how will you overcome it?

And I think I conclude it perfectly and to an absolute A – answering: “Finally, we are really a bunch of exceptionally nice people” – I love the fact that we do not have five full time communication "experts" on board, that would delete comments like that... ha ha.

Web Analytics Wednesday Stockholm

Lars Johansson put together a super Web Analytics Wednesday event last night! The main events of the evening, beyond some really pleasant networking, was two presentations by Practitioners and the Vendor Panel:

  

• Practitioner Case Study: Jannike Tillå, Aftonbladet (reporting KPI’s on a hourly basis – I am impressed)
• Practitioner Case Study: Massimo Pascotto, Scandinavian Airlines
• Vendor panel, moderated by Lars Johansson & Mattias Malmnäs

I (IndexTools) participated in the Vendor Panel together with Google , Omniture , Webtrends , and WebSideStory and one of the surprising questions and debates was about accuracy in Web Analytics tools. Surprising, because this is a debate that was somehow closed over the last few years. All vendors on the Panel use pretty much the same data collection methodology - and yes, WebTrends' patent-pending first-party cookie solution is no different ;-). Brian from Google (as part of the audience this evening) – added a few truths about why we just have to accept that there is no such thing as 100% accuracy. This included the fact that:

• A) Not all visitors accepts 3rd party cookies
• B) Not all visitors accepts 1st party cookies
• C) Some visitors use both their “WORK” computer and their “HOME” computer and are "counted" as two.
• D) ...

If you want to do dig a bit deeper into this subject; go read Avinash’s super post about Data Quality Sucks, Let’s Just Get Over It – and the one advice you should take from that post is, if you ask me:

- Assume a level of comfort with your data

And of course – it’s always nice to meet up with Brian, Sara, Matthew, Ian (Thank you for the WebTrends powered “Belgisch bier” .. and of course the opportunity to say hi to the smart people from Pricerunner.com.