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)

Search Engine Strategies San Jose 2007 (Web Analytics viewpoint)

Thinking about Web Analytics or Increasing Publisher revenue, SES San Jose is by far the best Search Engine Strategies show out there – period! The commercial part is working and there is a clear monetary return from the event, but the real life networking (in contrast to online social networking) is simply phenomenal. You end up working 18 hour days though!

The following Web Analytics vendors participated as exhibitors:

• Omniture (OMTR)
• IndexTools
• Visual Sciences (VSCN)
• Google Analytics
• ClickTracks
• Unica

Where was Webtrends and CoreMetrics? ... hmmm. However; the size of the Google Analytics booth tripled in width and height; so from a m3 point of view - everything equalled out :-)

And YES; those 18 hour days includes the following three fantastic (must attend) networking events:

• Google Dance
• Search Bash
• Vintage tub & bath event

It is always super to see friends and contacts here.. a quick hello (from the top of my head) to:

• Avinash Kaushik (Google Analytics) - thank you for a wonderful breakfast at Google Monday morning - and I surrender - the food IS great! (...and thank you for the drive out to Allan's event)
• Brian J. Clifton (Google Analytics) - thank you for another GA sponsored dinner on Thursday :-) ...and insight into your web analytics book to come.
• Bret Crosby (Google Analytics) - YES I know :-)
• Timo Aden (Google Analytics) - blog link exchange. Coming right up my friend.
• Peter Biro (ClickTracks and co.) - cheers and thanks for the Boston invite.
• John Squire (CoreMetrics) - you win on that lunch debate! ;-)
• John Marshall (MarketMotive) - very nice meeting you and your wife; made the whole thing a tad less geeky (even though you then returned with a 1 click only presentation) :-)
• Akin Arikan (Unica) - good to see you; ..and thumbs up on your presentation on Brand measurement.
• Stephanie Yang (Offermatica) - thank you very much - giving me thorough insight into your newest offering.
• Jamie Smith and co. (Engine Ready) - somehow funny that you as one of our biggest partners ended up at the same hotel and same floor as me. Fooled by Randomness.. heh.
• Allan R. Dick, Jr. (Vintage Tub & Bath) - thank you for a wonderful evening out with “top of the pop” in Search!
• Shari Thurow (Omni Marketing Interactive) - thank you the update and best wishes in getting back into shape with that foot.
• Andy Atkins-Kruger (Web Certain)
• Anne Kennedy (beyond ink) - Remember to PING me, so we can set up that extra day in Chicago. I would love for your team to get detailed insight into IndexTools
• Patrick C. Price (Idealizer)- great to see you again and again and again - you were everywhere :-)
• Sebastien Doyen (LinkyAgency) - great to see you again and good luck with your NEW venture. You of course know where to go when the need for Enterprise Analytics comes, right? :-)
• Johan Stein (Alchemist Media) - great to meet up at last! – let me know if you need any help (input) on your conversion research v2
• Michael Stebbins (MarketMotive) - great meeting you and Congratulations on your new company!!
• Matt Bailey (SiteLogic) - Great chat on the bus (and YES, I hate you for having access to the Iphone.. he he)
• Anton Konikoff (Acronym Media) - As always; very nice meeting up Anton, even though you are ever so busy!
• Tim Ash (SiteTuners) - Great to heat about the book. Remember to ping me when you get there.
• Mikkel deMib - Det er jo et eller andet sted lidt imponerende hvor mange events vi ender med at vaere til med hianden rundt omkring.. Hygge.
• Jon Myers (Latitude) - Great catching up with you mate... and thank you for the Heineken’s :-)
• Dixon Jones (Receptional) - Loved the T-shirt. Brave though! :-)
• Bryan Eisenberg (Future Now) - great seeing you there. Sorry that I missed your session about converting visitors into byers.
• Rand Fishkin (SEOmoz) - The Wizard of Moz :-) – sorry about missing your session, but the Web Analytics one was up. But great to see you again.
• Jonathan Mendez - Great having dinner with you – and had you waited around, you could have joined the “Avinash bus”..

... I am MOST sure I forgot twice as many as I listed above and that I remember the instant I upload this.. Sorry about that, no offense intended! :-)

There was “only” some three session on Web Analytics – I know this is no Emetrics, BUT I think that is a bit thin as every single SEM and SEO company that I know heavily depend on Analytics. Find the session and blog posts about them below:



SES San Jose 2007 Web Analytics Sessions:

Web Analytics & Measuring Success

• Session: Web Analytics & Measuring Success (Online Marketing Blog)
• Web Analytics and Measuring Success (SEO Roundtable)
• SES:SJ - Web Analytics & Measuring Success Overview (E-marketing performance)

Analyzing the Analytics Players

• SES San Jose - Analyzing the Web Site Analytics Players (Bizresearch)
• Web Analytics Vendor Tools Comparison (And One Challenge) (Occam’s Razor)

Meet the Web Analytics Players

• no one blogged about this?
  

See you all next year! For sure.. :-)

EU and US JavaScript Disabled Index numbers + Web Analytics data collection impact

I had a great chat (as always) with Eric Enge about a set of web analytics research numbers of his; where part of the dialogue kept coming back to elements of JavaScript being Enabled or Disabled. During the chat I promised that we would do a cross analysis among a larger set of visits (not visitors), for us to get a decent attitude on what the JavaScript Disabled index number should be.

The whole reason that this might be of interest is; that most users of Web Analytics enterprise systems today deploy a page tagging solution such as Omniture or IndexTools – where the data collection methodology is in part based on a small JavaScript. It is therefore very likely that there will be a discrepancy in the collected data if you have visitors who have disabled JavaScript. Before concluding on the impact of this understood discrepancy, let me present the results:

JavaScript Disabled Index numbers
EU: 1.4%
US: 3.05%

Source: 1.000.000.000 visits across multiple industry web properties using IndexTools.
(VisualRevenue.com/blog – Dennis R. Mortensen)



Where one might, at first sight, be concerned about a 2% to 3% discrepancy, due to the fact that JavaScript is not enabled. This is normal and it is very common that people confuses or transmit to much value to JavaScript disabled numbers as they either expect NO data collection at all or very limited data collection. The fact is that most (all the ones I know) of the enterprise vendors have a noscript IMAGE “fall-back” data collection methodology – meaning that all of those who have JavaScript disabled will be tracked!

It is however valid to say that the IMAGE fall-back methodology do not have the same degree of nuance (the number of metrics) as the original intended JavaScript. But all the basic information is collected and tied together in a session. You might (dependant on your Web Analytics solution) even be able to configure the fall-back methodology to track custom actions, revenue and other information.

Therefore and as example, if we say you have a cool 4% sales conversion rate on your US visitors, the discrepancy in your Revenue Metric (KPI) should not be off more than 3.05% of your 4% and thus you cannot attribute JavaScript Disabled discrepancy to more than 0.12% of your Revenue Metric. On $1,000,000 in sales, that is only $1,220 - AND some vendors even give you an opportunity to track those last $1,220 as well :-)

Conclusion:
It is very likely for us to believe that we will see a continuous decline in visitors who have JavaScript Disabled and it is very fair to trust that visitors with JavaScript Disabled have little or no impact on your web analysis efforts. However; it is a fact that data might not be 100% accurate due to this data collection defect, but the impact is much smaller than the immediate believed 2% to 3% as first assumed.


So what are your JavaScript Disabled numbers; are they close to my index numbers or... am I completely off? :-)
Comments please. (especially if you have some RAW numbers on IMAGE disabled)

N.B.
I checked my blog and by pure luck, the JavaScript Disabled number for US visitors matched the Index Number almost spot on.

Online social network participation inequality and the impact on the User Engagement KPI

In my recent post about what and how to measure Social Networking websites I suggested the following online social network user engagement KPI’s:

• User Engagement
• Anonymous visitors to members conversion rate
• Active member length
• Time since last login
• Total time spent on site
  

With a comment added in regards to the User Engagement KPI saying that: “The User Engagement KPI is a custom session metric designed and calculated from a set of basic metrics such as for example; pages viewed, time spent on site, time since last login, comments posted or other content submitted, subscribed to a feed or alert and so on – the calculation of this KPI is highly dependent on the structure of the Social Network in question).”

I think it is of the utmost importance that I add a note to that, as you cannot presently look at an average or median User Engagement KPI without segmenting it, due to the fact that there is an online social network participation inequality. The fact is that most users do not participate very much!

Your UGC-users (User Generated Content-users) are split into three groups:

• Observers - those who do not contribute
• Contributors - those who contribute now and then
• Participators - those who account for most of your contributions

And as you probably guessed - the distribution of the contributions follows Zipf's law – should you plot the User Engagement KPI for each user (If you run a blog, you can for fun play with the idea that User Engagement equals “comments” – and as such plot the number of comments per unique user, assuming you have access to full visitor segmentation in your analytics tool).

I also suggested in the above mentioned online social networking measurement post that you had to create a KPI Index. Here you will have to extend the User Engagement KPI Index with an attitude on the distribution.


Online Social Network User Engagement KPI Index (Distribution attitude)

• 90% - Observers
• 9% - Contributors
• 1% - Participators

The general attitude and rule-of-thumb is not surprisingly that you have about 0.1% participators on a blog (which differs from online social networks in general, by having a very low participation) - this is of course something you have to determine for your blog or online social network.


My conclusion is that:

When using a User Engagement KPI (as suggested by myself) it is highly unrepresentative using either average or median numbers (or voices for that matter, as in specific comments) to conclude on the overall attitude of your online social network, due to the fact that most online social networks have participation inequality! Segmentation must be applied.

Client Pains with Web Analytics (PDF)

Today Manoj, over at web analytics world, released a free PDF - where he asked some of the top analytics experts (as he so nicely put it) what topics surrounding web analytics their clients found most troublesome. Along with the issues they provided solutions to help remedy the pains.

Analysts offering their insight and wisdom included:

• Eric Peterson
• Anil Batra
• Jason Van Orden
• Justin Cutroni
• Marshall Sponder
• Gary Angel
• Akin Arikan
• Dennis R. Mortensen
• Avinash Kaushik

I am of course happy that I could add my take on the issue to the report and I suggest you go download the PDF -- it is an easy read -- and worked perfect for my commute home today. Find my answer to the question below:

- - - - - - - - - - - - - - - - - - - - -

Client Pains with Web Analytics.
(Dennis R. Mortensen, COO IndexTools)

I think it is unfair to boil it down to one unique set of client pains. However; I think it is absolutely justifiable to confirm that clients (and I am saying this with my Vendor cap on) do have pains in deploying and finally operating web analytics to an extent where they actually do have a real return on investment. It would probably have been easier to list those elements of success that cannot be debated, but that was not the question! So here goes a characteristic client pain that might not be that obvious - and at the same time not really recognised as a pain to begin with (it is actually quite often, and wrongfully so, accepted as a web analytics tool choice failure).

Client pain:
“The inability to differ between reporting and analysis and the impact of confusing the former as the end game - thus creating a clear disconnect between investment and use of the deployed web analytics solution.”

I believe very strongly that one have to understand that reporting on any metric at pure face value within a very limited context is unsound and should not be confused with actionable insight from analysis! One should remember that:

• Reporting is produced by tools
• Analysis is done by people
• Processes are deployed by organisations

Concluding the above - we see that solving a client pain of this magnitude – one actually have to employ not only people, but the right people and at the same time create very clear set of processes around ones web analytics efforts. Which is grand wording to a problem that needs a solution today. First task would be to acknowledge that you are in pain as an organisation and I suggest a honest evaluation of the following three statements:

• Reporting does not require action (analysis without action is reporting)
• Reporting does not relate to benchmarking (analysis within your own data silo resembles reporting)
• Reporting is not a forward looking activity (analysis is rarely done from a retrospective viewpoint)

When you have acknowledged the pain (assuming it exist in your organisation) – then comes the long never ending continuous cure called web insight through web analysis!

ad:tech Chicago 2007 – Web Analytics vendors

I attended ad:tech Chicago over the last few days and I am of course cheerful (being a great fan of the long tail concept in general) – that I at long last got to see Chris Anderson. Almost worth the 17 hour trip there! :-)

The following Web Analytics Vendors attended ad:tech Chicago 2007:
(Which is not a bad view of who matters)

• Webtrends
• IndexTools
• Omniture (OMTR)
• Visual Sciences (VSCN)
• Google Analytics

And I finally had the opportunity to meet up with Josh Manion from Stratigent - the CEO of one of the bigger dedicated Web Analytics consultancies around.

Cheers.. and back home to Buda tomorrow.

N.B.
I am sorry to find out NOW, when I am back at the hotel, that Manoj Jasra from Web Analytics World attended without me knowing it. Being an avid reader of his blog it would have been nice to say hi and hello.


Therefore, anybody attending SES in San Jose - COMMENT - so we can meet up. Red Bull’s are on me! :-)