Don’t panic, it is an actual visit. Yes, the visitor did leave after viewing 1 page. Keep in mind, that Google Analytics works with sessions. So if a visitor does not view an additional page on the site, Google Analytics will not have data on how long the visitor viewed the initial page. It won’t be able to pass the initial pageview data to the script on the following page.

Time to look at other metrics than fixate on avg time on site for a single visit.

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Google Analytics 1 visit 00.00.00 avg time on site, 100% bounce rate via The Pringle

Avinash Kaushik, in a interview on WebProNews regarding bounce rate, mentioned “I came, I saw, puked and left” as the quick lamens explanation, while the technical explanation for bounce rate according to Avinash is “Single Pageview Session” for the bounce rate metric. In essence, you will notice when going through your analytics stats that visitor bounces are in line with a visit without navigating further (no clicky clicky), unless an event is triggered during the visit. In contrast, Blogs tend to have a higher bounce rate as visitors are usually there to read an article they found in a search or link, and will leave when complete (as said, in most cases).

Overall site bounce rate metric (from the dashboard in Google Analytics for example) on occasion can be deceiving, as a quick site overview it will help to give a quick overview average figure for the site as a whole. However there are a few key areas that will help you evaluate bounce rate for the site more effectively such as Traffic sources report, Content Drilldown and Keyword Drilldown for the site. Before analyzing these reports, you may want to take a few seconds to get an idea for which area you may be looking to improve your visitor retention for the site, for example: are you looking to improve visitor retention from your individual traffic sources? Or are you looking to improve this on a keyword/content level? They may sound exactly the same; however approaches to solving bounce rate issues may differ slightly.

Before I go further, let me just explain why the post title mentions that it is not always call to actions as the answer to improving bounce rate. If you receive visits to a page from a phrase such as “red widgets”, however your site/content is dedicated to “building widgets” you are going to have a high bounce rate, call to actions will not solve this issue. Chew on that, let it swirl around, sleep on it

On the other side of the coin, if you have visits to a page for “green widgets” with the keyword “red widgets” which are not necessarily related to that page, however you have another page of content within the site that would provide a better landing page for those keyword referrals such as the “red widgets” page, then call to actions may be a step in the right direction, as well as reviewing the preferred landing page’s optimization (I’d suggest this as your first step).

Content Level

Using the content report (Landing Pages in Google Analytics Content Section) and well as the keyword report will display the associated bounce rate within these reports. I would however suggest using the content drilldown as a starting point, arrange according to bounce rate in a descending order, and then drill down into the high bounce rate content sections/pages of the site you wish to address.

Choose a page to work with, and cross reference this content page against the Keyword Entrances. This will give you an idea of content page specific keywords that are resulting in the high bounce rate, helping to give you an idea of whether or not you should be re-optimising the copy or providing alternative navigational paths for visitors landing on the page, in other words, do your “call to actions” need to be improved or if the actual content needs to be updated or changed.

Side note: In some cases there will be keywords with a high bounce rate that you may not be able to solve that easily. Other elements within a page may result in keyword referrals that may be unrelated to the content on the page, therefore resulting in a bounce. Page segmentation anyone? Example: Currently a top referring phrase to this blog is “Pringles market share” on occasion, I have no content optimized for this, however the text appears in the page for “Pringle”, resulting in the phrase referrals. Not something I can easily solve to reduce overall bounce rate.

Traffic Sources

Comparing the traffic sources against the bounce rate metric may be helpful in analyzing the effect of work done off site. Referrals from Facebook.com have a high bounce rate? Perhaps the content submitted does not grab the attention of the Facebook users, are you spending too much time on a traffic source VS the quality of traffic is may provide? Analysing the traffic sources against the associated bounce rate is a quick win to evaluate your offsite promotional work in certain areas. Again, does not necessarily mean your calls to actions need to be improved as the first action to improve your bounce rate, it may be your marketing within the related traffic sources may not be targeting the right audience correctly.

Round up

Remember improving or adding calls to actions is not necessarily the first step to improving your bounce rate within a site. Analytics tools are not always accurate therefore the necessity to cross reference data and use more than one metric in your analysis of you analytics data, simply evaluating keywords with a high bounce rate is not going to give you the data you need to make an accurate decision on how to address bounce rate within the site.

Cross referencing high bounce rate keywords/phrases against content will allow you to make a more empowered decision on how to address the issue. I.e. should I be optimizing other pages on the site that are far more relevant? Am I addressing visitor queries within the content? Is your content easily scanable? (Remember you only have a few seconds to grab a visitor’s attention on a landing page, can they find the information they need quickly/easily?)

Bounce rate can be a positive metric in some cases as it may help you improve the quality of the content within the site, ultimately improving visitor retention and increase overall traffic. Don’t fall into the trap of trying to spend time simply adding/improving calls to actions without getting the full picture from your analytics package as well as understanding what your visitors are looking for when landing on your site.

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I recently had the task of creating a new analytics account for a client, where the website had a few key subdomains, each with their own unique content, however the parent domain was simply a landing page, used to direct users to the relevant subdomains depending on the content needs. Now as the parent domain performs well within search engines (as well as the central point of contact for any navigational search), the need to track visitor data from the parent domain and then across all subdomains became a necessity.

Custom GA tracking script

After creating your analytics account and your first website profile (for your parent domain), you will need to make a small change to the GA tracking script, which you will be installing on the parent domain and across all subdomains and their pages.

In the above screenshot, we have added an additional line of code to the script (directly under the UA tracking line), pagetracker._setDomainName(“.example.com”);. This allows Google Analytics to use the same cookies across the various subdomains to track visitor data. The downside to this would be that you will not be able to differ between similar page names within the subdomains, such as area.example.com/index.html and location.example.com/index.html (Analytics reports only display the request URI). Adding custom filters to your profiles will however allow you to to differ between the subdomain pages.

Create additional website profiles for each of your subdomains

Creating custom filters

Now it’s time to start setting up filters for each of your subdomain website profiles. The current tracking script customisation tracks visits across all subdomains, therefore we will need to add a filter to each subdomain profile to only include traffic from that specific domain for your reports. Select the edit option associated with the subdomain profile and add a new filter. I only want to see visitor data associated with the subdomain profile i’ve created, now create a custom filter to include only from the subdomain associated with our profile.

As seen in the above screenshot, i am filtering all traffic to display only traffic associated with the specific subdomain website profile within the filter pattern. You will need to create a custom filter for each of the subdomain website profiles you have created within your Google Analytics account to track individual subdomain visitor data. Take note that additional subdomains can be added to the pattern filter if needed by simply separating them with a pipe (|).

Finally, our parent domain profile will also require a filter, as mentioned earlier, Google Analytics only displays the request URI, therefore it will be difficult to differ between pages with the same name. For this i need to create an advanced filter for the parent domain website profile that will append the hostname to the request URI to display in the analytics reports.

Ensure that the field entries for the advanced filter are entered in identically to the ones within the screenshot above. Save the filter and we are now complete. Your Analytics account setup should look similar to the diagram below.

Now you are ready to track subdomain specific data within the individual website profiles created, as well as see an overview report from the parent domain profile for all data across the subdomains and parent domain for all your reporting needs.

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Google Analytics subdomain tracking, custom script & filters via The Pringle

The answer is very simple, a website profile is exactly that, it is a profile for a website. Need more than 4 analytics goals for a website? The solution, simply create a duplicate website profile for you site.

Login to your Google Analytics account, select your analytics account where the website you are currently tracking is located. Remember that analytics accounts contain website profiles. The analytics account dashboard will then display the website profiles that are associated with the selected analytics account and it is from here that you are able to create additional website profiles, setup goal tracking and manage filters.

At the bottom of your analytics account dashboard you will see 3 options, “Add Website Profile” , “User Manager” and “Filter Manager”. The “Add Website Profile” option you will also notice can be accessed in a number of ways including links along the righthand side of the dashboard as well as in the top righthand corner of the dashboard.

Once you have selected the “Add Website Profile” you will be taken to a screen where you will need to choose between either creating a new profile for a domain (used when adding a new website to your analytics account to track) or the option to create a profile for an existing domain.

We are going to be using “add a profile for an existing domain” to create a duplicate website profile for a website you are currently tracking in Google Analytics. Fill in the details and remember to create a unique profile name for this duplicate profile that will help you differentiate between the duplicate profiles. Click Finish, and your duplicate profile has now successfully been created.

Please note as this is now a “new” profile, analytics data will only be gathered from the profile creation date. If you require historical data within this duplicate profile, ensure that you create your additional duplicate profiles when you setup your analytics account. Each profile will allow you up to 4 analytics goals per profile and you are able to have a maximum of 50 website profiles per analytics account. Creating multiple profiles for websites can be used for a number of other analytics tracking options, not just to maximise your goal tracking, but can be used in conjunction with analytics filters to track specific traffic (only want to see paid search analytics data?), subdomains and even social media data tracking. The options are limitless for their uses.

NB. When applying filters to a website profile, this will affect the data processing for that website profile. You will be unable to remove filters and see RAW data afterwards. In otherwords, once data has been processed with the filters associated with that website profile, you will be unable to reverse the filtered data. It is always a good idea to create 1 unfiltered backup website profile to refer to when in need.

Update:

Google updated goals within analytics in October 2009, now allowing up to 20 goals per profile. There are now 4 goal sets that can contain up to 4 goals each, allowing more freedom in what you may want to track as goals, from downloads through to purchases.

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I’ve hard coded my tracking script into the footer of my wordpress theme, so previewing posts is always tracked within the analytics. No point in creating a custom exclude filter for my home and office ADSL line, as the IP address always changes as well. So the other solution would be just to exclude all “preview=true” URI’s from being tracked, to help having to see my previews in my top content pages breakdown in analytics.

It’s an extremely easy task to do. Simply sign in to your Google Analytics account and edit your blog analytics profile and select the option to “add filter“. You will need to use the custom filter option as seen below:

Once you’re done, you need to select the type of filter you are wanting to create, we are looking to create an exclude filter, so make sure you have the “exclude” option checked. Now you will need to change the “filter field“, which defaults to “visitor geographical domain“, to “Request URI” towards the top of the dropdown (dropup? ). URI meaning everything after the domain name, very different to URL.

Next, you’ll need to add your pattern. As this works in a similar manner to the head match option when creating goals in analytics, you will only need to add the common “pattern” that will appear in URI’s that you do not wish to track. Simply add your “preview=true” for the filter pattern, don’t forget to name your custom filter, then save. You are all done.

And you’re done! Remember this will only affect any new data tracked going forward and does take some time before you will see updated data within your analytics profile. As a side note, be very careful that the “preview=true” function is not used somewhere else within wordpress legitimately, as you may very well want to track that data.

Sometimes it’s best to tweak and customise yourself, than having to really on plugins to easily solve a need/problem. Hope this little tid bit of info may help some of you.

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Exclude filter in analytics for WordPress post/page previews via The Pringle