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If you have one Google Analytics profile tracking multiple subdomains (or you want to do so); for example, one profile tracking:


when you view your Google Analytics data in the profile that is tracking all 3 subdomains it won’t by default separate out the data for each actual subdomain. You’ll need to add some additional code to your tracking script and also set up an Advanced Filter.

If each of your subdomains has pages that are named the same, you will see the data reported as aggregate for all them under that page name, therefore the data for those page names will be inflated and not actionable. For example:


will all be rolled up under /contact.html and you will not know what the true data is for the contact.html page on each subdomain. Instead, you will have one entry of /contact.html displaying the data for all traffic to contact.html on all 3 of the subdomains. Not good!

What you will want to do is see it by subdomain so you can attribute the correct data to the correct subdomain.

Luckily, this is easily fixed!

If you already have your pages tagged with the Google Analytics tracking script, just add the line in bold to your existing Google Analytics code (replace MyCoolWebsite.com with the actual domain, not the entire subdomain and of course make sure all of the sites have the same profile tracking number (seen below as UA-xxxxxx-x, the x’s will be replaced by your own account number and profile number).

 If you haven’t added the Google Analytics tracking script to your pages add the entire code below – again with your own specific Google Analytics account number and the profile number, and your actual domain:

<script type=”text/javascript”>
var gaJsHost = ((“https:” == document.location.protocol) ? “https://ssl.” : “http://www.”);
document.write(unescape(“%3Cscript src='” + gaJsHost + “google-analytics.com/ga.js’ type=’text/javascript’%3E%3C/script%3E”));

<script type=”text/javascript”>
var pageTracker = _gat._getTracker(“UA-xxxxxx-x”);

Next, you will need to set up an Advanced Filter, this will actually separate out the subdomains so that they will be reported separately under that profile:

The finished result is that instead of just 1 instance of /contact.html, you will see it reported in your reports with each subdomain added to /contact.html as follows:


Here is how to set up the Advanced Filter for Tracking and Separating out Subdomains:

  1. First log-in to Google Analytics.
  2. Select edit from the profile you created that will track all of the subdomains.
  3. Select edit in the Filters Applied to Profile section.
  4. Fill in the below information and Save.

First, here is the actual information for you to copy and paste, then below that is a screenshot of how it should look before you save and apply it to that profile.

Filter Type: Custom filter > Advanced
Field A: Hostname
Extract A: (.*)
Field B: Request URI
Extract B: (.*)
Output To: Request URI
Constructor: /$A1$B1


google analytics advanced filter for tracking and separating subdomains

Categories : Analytics
Comments (2)

Setting up basic Site Search within Google Analytics to gather data on internal site searches on your website takes less than 1 minute to do. Google Analytics Site search allows you to easily find out what users are searching for within your site and their behavior after the search results are displayed for them.

The benefits of knowing and using the information that internal Site Search data provides I will be posting about in a near future post, but in the meantime I suggest setting this up so that you can start collecting the data in the meantime.


Setting up Site Search within Google Analytics

Note: The only thing you will need to know ahead of time is what your search query parameter is for searches on your website. To find out what your search query parameter is:

1) Go to your website and do a search in your site search box for any keyword
(for my site, I am doing a site search for the word “test”)

2) For 99% of you, you can look at the URL of the search in your browser that is returned
(on my site it shows http://blog.joshbaker.com/?s=test)

3) The search query parameter for my site is s
(if I was to do another search for the term “ecommerce”, the URL would show http://blog.joshbaker.com/?s=ecommerce. The query is ecommerce, but the query parameter is s)

If you are still confused, visit the Google Help Docs on uncovering your query parameter  

Turning on Site Search:

Of course you will need to be logged-in to your Google Analytics account.

1) To the right of the website profile you want to add internal site search tracking to, select Edit










2) On the page that appears, on the top right, select Edit 






3) Scroll down to the Site Search section

a) Select the radio button next to Do Track Site Search

b) Enter in the query parameter for your site that you found earlier

c) Choose if you want to strip query parameters out of URL or not (most likely you will want to select “yes” so that you just are seeing the actual keywords searched when viewing the data in Google Analytics)

d) Select Save Changes



Site search is now activated in Google Analytics for your website and in just a few hours your data will start appearing.

Categories : Analytics
Comments (0)

It comes with no surprise that the heat maps produced from the data collection from studies using eye-tracking technology (following the eye movements and fixations of those participants in the study on how they look at a webpage) is all the rage in discussing and uncovering how visitors behave when looking or interacting with a webpage. However, many companies cannot afford to have this research done for their own actual website or are not willing to invest in their own study if they can indeed afford it. Therefore, many companies therefore rely on the common findings from reputable already published studies in order to increase the usability and effectiveness of their own websites (towards the bottom of this post I will discuss software that you can use to uncover related data on your own).

Photo by Jason-Morrison

Photo by Jason-Morrison (License: Creative Commons Attribution-NonCommercial)

 Probably the most famous study that over the past few years that we have all heard or read about are the findings from a study that Jakob Nielsen performed which uncovered the F-Pattern – the F-shaped patterns that users follow when reading content on a webpage of looking across the top of the content, then move down the page to read again horizontally across the page -but usually not fully across the page, and then finally quickly scan the left side of the page from top to bottom, this forming in most cases an “F” shape if you were to draw lines for where their eyes moved.

 A heat map was then produced to display the cumulative results from the study by providing insights into how the readers actually read the content on the page, not only visualized in the heat map by the density of those areas that were fixated on the most, but also by the colors red, yellow, green/blue and gray representing how “hot” a particular area was (red showing that area was viewed the most, blue the least, and gray not at all) that was read.

The data from the study was analyzed and the take-away was as stated by Jakob Nielsen, when discussing the Implications of the F-Pattern:

  • Users won’t read your text thoroughly in a word-by-word manner. Exhaustive reading is rare, especially when prospective customers are conducting their initial research to compile a shortlist of vendors. Yes, some people will read more, but most won’t.
  • The first two paragraphs must state the most important information. There’s some hope that users will actually read this material, though they’ll probably read more of the first paragraph than the second.
  • Start subheads, paragraphs, and bullet points with information-carrying words that users will notice when scanning down the left side of your content in the final stem of their F-behavior. They’ll read the third word on a line much less often than the first two words.

In the spring 2009 issue of Search Marketing Standard, Gord Hotchkiss of Enquiro, whom offers eye tracking services,  stated that:

“we do like eye tracking as a tool to gain insight into user behavior because it gives you a deep data set, especially when you combine it with post-session questions. It allows you to combine and compare what people physically see with what they remember seeing”

Tools You Can Use, Cost-Effectively, For Additional Visual Insight into Your Visitors Interactions

You can easily find a growing number of eye tracking heat maps and their summary findings and analysis around the internet these days. Of course there are those of us who want to know more about how people are using our website and typically are only using a web-analytics platform such as Omniture Web Analytics or Google Analytics to gather data and then interpret it. Then, there are those who want to gain even more insight or a more visual representation of user interaction (beyond the standard site-overlays that come with a web-analytics package) and luckily there many web-applications (typically implemented by adding a java code snippet to your web page or pages) available for you to use to do just this. While not as informative or a significant as eye tracking analysis, click tracking provides another level of analysis over what you are commonly seeing:

Here are just a few of the many available to help you identify with heat maps where your users are clicking enabling you to further identify usability or performance issues:

CrazyEgg – provides Click HeatMap, Click Confetti (find out where people click based on their referrer, search terms, etc.), Site Overlay to look at the hard data, ability to share results and export data. Live reporting.

ClickDensity – provides Click Heatmap, Click Map (shows where all clicks are on page), Hover Map which displays usage data for individual items on your page, Page Summary stats, specify an internal page (URL) that a visitor must have viewed in their session BEFORE the current page or a page (URL) that a visitor must have viewed in their session AFTER the current page, ability to save heatmaps etc as a .jpg.

ClickHeat – provide a visual Heatmap of clicks on a HTML page, showing hot and cold click zones. While not as robust as the tools mentioned above, it is however free (Open Source).

Have you used other clicktracking tools and would like to share them? Let me know!

Categories : Analytics, Usability
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You’re probably curious to know what content your visitors are downloading the most on your website. I bet you’re also interested in knowing this data for multiple reasons including gauging interest, popularity of each downloadable file, and to learn what the best page and the best page position for the links to each download are. Of course there are a multitude of reasons for your interest, but no matter the reason, it all boils down to wanting to learn more about how your website visitors interact with your downloadable files.


Example: Tracking PDF Media Kit Downloads
You just completed your PDF media kit for potential advertisers to download and you can’t wait to start getting all that advertiser revenue.  You put the media kit online and three weeks later you can’t believe it – what’s happening? Three weeks and you have no phone calls or emails of interest. Is it the media kit itself, the information presented in it, or maybe no one has downloaded it yet? You’re confused because you know that people are visiting the page that the link to the media kit is on, it’s your most popular page. If only you were tracking the number of downloads of the media kit could you narrow down what just might be wrong.


 Or maybe you would like to:

  • Track your MP3 or Wav Podcast downloads across your web site
  • Track to learn what your most popular .doc, or excel .xls file, or .zip file is
  • Track your Catalog downloads
  • Track clicks of links to external sites


Since your links are directly linked to a file and there is no actual page to put analytics tracking code on, you have to track it differently than you would a webpage itself. Luckily, most analytic software packages offer simple ways to get to track and extract this data.


With Google Analytics, it’s as simple as adding some additional JavaScript code to the link that goes to the downloadable file:

For example, if the link to your Media Kit was:
 < a href=”http://www.example.com/mediakit.pdf”>

You would add the trackPageview() JavaScript code to it as follows:
< a href=http://www.example.com/mediakit.pdf  onClick=”javascript: pageTracker._trackPageview(‘/downloads/mediakit’); “>

You could change /downloads/mediakit to whatever directory names that you would like, but clicks to your Media Kit would be found in your Google Analytics website profile in the Content Section under /downloads/mediakit in this example, or whatever directories you place in the code – each link that you want to track, if you want to track each downloadable file individually (which you would), you would name uniquely such as /mediakit1, /mediakit2, and so on.

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Upper Funnel Keywords in paid search (or even in organic search by definition) are those keywords that do not bring in visitors that convert in a traditional sense into an immediate purchase or lead, but rather are the keywords that convert the potential customer to the next stage of the “interest cycle”. These Upper Funnel Keywords typically bring potential customers or leads into your website that have not made up their mind to make a purchase (or fill out a form or take a trial) because they are still in their “considering their options stage” of decision making and are possibly not yet familiar with you or in some instances not familiar enough with you to complete a conversion during that visit.

You are going on vacation to a cold climate (let’s say the North Pole). You know you need to buy something to keep yourself warm, but you’re not sure exactly what options are out there because you live in a year round warm climate. So you do a search on the keyword phrase extreme cold weather coats. You end up at a website by clicking on a paid search ad that appeared when you typed in the phrase and notice that they have a section for cold climate coats and even list the temperatures they can withstand.  You think to yourself, wow this is great, now I have a better idea of what’s available and I kind of like Brand X Model 4567, but I am not going on the vacation for another 4 months so I am not going to purchase anything today because I don’t have the extra money. Forty-five days later you win $200 on a scratch-off ticket that you found and decide that you are ready to purchase a coat with your extra money, you do a search on Brand X Model 4567 since you knew the exact coat you wanted to buy now and end up back on the same website as before and purchase the coat.

The difficulty in measuring the value of these Upper Funnel Keywords is that they don’t produce single visit conversions -you can’t see the whole picture of entrance to conversion in your analytics data in a linear fashion.

For instance, a single visit conversion would show start to finish in one visit from entering your site to making the purchase. Here you easily have the whole picture from the PPC keyword that triggered the ad that they clicked on to enter your website and their entire path to the purchase.

With Upper Funnel Keywords, a typical scenario would be that the visitor arrives at your site from these keywords or phrases,  looks around, leaves, comes back another time reads more information, exits your site again, then finally comes back a 3rd time and makes a purchase.

Most only know how to measure the keywords that produce single visit conversions and thus deem these Upper Funnel Keywords more or less valueless because their value isn’t easily seen in a typically known fashion. The ROI isn’t easily visible.

In the example given earlier, the keyword phrase you searched on first, extreme cold weather coats, was the Upper Funnel Keyword phrase. You didn’t purchase during that visit of your initial search, but you did eventually go back and purchase based on the information you learned during that first visit. Had another website come up with a different but possibly similar featured coat that you liked, you would have purchased from there and a different coat. So there was definite value in that first search as it gave you the information you needed to make a decision, but you just didn’t purchase then. Now imagine if you were only looking at single session conversions, you would only be able to confirm that Brand X Model 4567 was a valuable keyword because a sale was associated with it. But in reality, without having a presence in paid search for extreme cold weather coats a sale wouldn’t have been made – thus showing the importance of being able to look at multi-session conversions to contribute back value (and ROI) to the Upper Funnel Keywords.

Avinash Kaushik on his Occam’s Razor blog this week made a powerful and very instructionally clear post on how to measure the success of Upper Funnel Keywords and I suggest you read his post for the details on how to do so.