Warning: this is a shamelessly bragging post about website optimisation. But it’s also a good insight into how to actually analyse your traffic statistics, based on bounce rate, average time on site, and returning visitors.

After about one year after launching LAb[au]’s website redesign, here is an excerpt from Google Analytics:

Google Analytics for lab-au.com, June 2010
Google Analytics for lab-au.com, June 2010

0,91% bounce rate

The bounce rate describes the proportion of users whose next action after landing on your website is… to leave it – hence the “bounce” metaphor. That means the lower is the better.

I’ve never seen such a low bounce rate: it means most people that land on the homepage interact with it. (Ok, I’m hearing the skeptics among us who will probably state that it’s because you actually need to click to get to some content. Before you complain about “mystery meat” navigation, just understand this: web designers and developers are not LAb[au]’s target audience; we are reaching out to actors on the art, architecture scene, curators, public and private institutions, etc… which are used to – and expect – creativity, out-of-the box thinking, daring, clear cut identity profiles. Anyway.)

3:11 minutes average time on site

Now, apparently, people enjoy what they discover: they stay for more than 3 minutes on the site. On the other websites i track, it never reaches such a long time (it’s more between 1:30 to 2:30 minutes).

83,76% new visits

That mean 16,24% returning visitors. A quick look into the Visitors loyalty screen show most visitors are, as usual, first-time visitors. But 3.53% have returned more than a hundred time. This seems quite a lot to me (hell i don’t even qualify to that returning visit rate!).

Bouncing rate lab-au.com, June 2010
Returning visitors, lab-au.com, June 2010

Why such a big amount of first time visitors?

LAB[au]’s work is truly very inspiring and attracts quite a lot of attention from “niche contexts” such as architecture, digital art and cultural magazines, both online and offline. This generates a consistent and slowly but rather steadily increasing qualified traffic flow (as shown in the graph below).

Bouncing rate lab-au.com, June 2010
Visitors flow to lab-au.com, June 2010

How about the impact of Social media?

It’s quite difficult to get a clear picture from google Analytics in regards to social media: i wish they were integrating a specific screen that gather the relevant information. Still, part of the information is there, hidden in the mass, waiting to be mined.

For instance, the efficience of “share-this” buttons

Via the Events system, I do track the number of times people hit a “share this” link via Facebook, twitter and myspace. Basically, only twitter and facebook are used by visitors. Last month, these were hit 16 times. Not bad, i guess, since it’s increasing steadily. It’s not much not either, yet i lack comparative data so i can’t really make up my mind. Besides, i don’t use “share this” buttons myself, I mostly do cut-and-paste (i’m a control freak). Since i suppose i’m not the only control freak on the planet, this is just an estimate (statistics are estimates). So, back to the “refering sites”, this time looking at visitors coming from Twitter and Facebook

I’m rather pleased by these statistics as the website interface was truly an experiment into a different type of navigation system, integrating hierarchical and transversla navigation. It takes some time to get used to. From what i can see from both user experience captures and traffic statistics, it’s not too difficult. Relieved i am. For now.