Do you advertise with Google AdWords? Do you have your Google Adwords paid search ads show up on their Content Network? Not sure what that exactly means?
Google allows you to not only display your ads in Google’s search results pages when someone performs a search on Google, but also on sites that use their Google AdSense program to automatically display paid search ads that are relevant to their website copy – although many times it’s not always relevant – but which makes this post relevant to you!
You can set up lists of sites or domains that you do not want your Google AdWords ads to show up on via the Excluded Placements section (found in the Networks tab of the new Google Adwords interface). By viewing the detailed stats of your ads on these sites, assuming you have your conversion tagging set up to show AdWords conversions correctly (surprisingly many put it on the wrong page) you can determine if your ads that are appearing on specific sites on the content network are profitable or not for you.
If not set-up properly though, you may be surprised to find out that your ads are still appearing on sites that you thought you had banned through the Excluded Placements section in the AdWords interface.
According to ROI Revolution’s Make Sure Your Excluded Placements Are Actually Being Excluded post, there is a easy fix in the Excluded Placements section to make sure you are correctly banning an entire site from having your ad appear on it.
Unless you specifically want to ban just a certain subdomain such as www.about.com, or marketing.about.com, but want your ads to appear on other subdomains of their website, you need to enter in just about.com. Entering in the root domain (about.com) without the third level (www, marketing, etc.)you will be successfully banning the entire domain not just that specific third level entered.
For more detailed information, you can view Google AdWords help section on setting up Excluded Placements.
There are just certain times when running a multivariate test to optimize web page conversions will produce unreliable results. Results that either will not yield statistically significant outcomes, or outcomes that even though the numbers may show statistical significance at the end of your test, would not be reliable enough to roll-out and see the nearly the same results much longer than after that particular testing period ends. Remember you are looking to take one step forward and improve your web pages conversion, and not two steps back rolling-out a page that ultimately performs worse than your control; which is quite possible if you are not mindful of certain instances.
Such instances include:
Seasonal traffic – Testing pages during specific high seasonal times for your business although may produce statistically significant outcomes by looking at the numbers themselves; the changes made based on the test outcomes would not be reliable after the seasonal traffic ends. The user intent during these times in most cases is not typical user intent or behavior displayed during the non-seasonal times, and in some cases also between seasonal times.
Traffic sources that fluctuate in delivery volume -If viewed at in a line chart would show high peaks and or low valleys (or may even show times of nonexistence traffic). This traffic volume is too unstable and therefore an unreliable indicator of ongoing performance. A specific instance would be running a multivariate test on a landing page that the traffic delivered to the test is from various different email campaigns. Also be careful of a test that suddenly receives a spike in traffic due to a current event for example that would send a large volume of traffic of non-typical visitors into your test.
Low traffic volumes – if your page does not receive enough consistent traffic of a certain volume than the likelihood of high confidence statistically significant results is slim-to-none in most cases. You need to have enough traffic to produce enough conversions (a conversion being anything you deem to be one, from a registration, to even a download) that your results will be accurate. Many conversion optimization experts say at least 10 conversions per day is the absolute minimum needed to run a test.
And if you’re A/B testing:
When you can’t run your control in unison with your test panels – without simultaneously running your control panel along with your test panels you will not be able to accurately assess the results of your test. You need to be able to assess how each of your panels or page combinations, both control and test panels, perform under the identical conditions and time period. The only way to accurately do so is to have them run simultaneously with your traffic randomly split amongst them.
Segmenting Visitors for a Deeper Bounce Rate Insight
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Bounce Rate by definition for a web page is the total number of visitors who enter your website through a particular page and then leave your site without viewing another page divided by the total number who visit that same page. Simplified, bounce rate is the percentage of people who enter your web site, visit only that single web page they entered on, and then leave – without viewing another page. The bounce rate of your high traffic or important web page’s is a highly actionable metric to know.
Although most web analytics tools make it simple to extract your websites or an individual web page’s bounce rate, looking at the bounce rate when presented in aggregate is definitely not as insightful or actionable as it could be and potentially lead to misinterpretation.
Let me explain – your bounce rate by default, without segmenting the data, is typically presenting a bounce rates determined from multiple visitor segments – both the good and the bad. Most likely then, you are then making possible inferences on that bounce rate to that page that could potentially affect all visitor segments – not just the ones that are bouncing.
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Let’s Take a Simple Multiple Choice Test:
Question: Who is your site actually built for?
Hint: It’s the same group of people that should be designing your website.
Choose your Answer:
A) Your Company (web design team, marketers, IT, etc)
B) The end user (i.e. your actual or potential customers)
If you chose B, you are correct! If you chose A, then you probably work for a company whose website isn’t effective as it could be and still wondering why.
If your company is like most companies, implemented ideas and then changes to your websites layout, design, navigation and other features are usually made based on internal decisions by HiPPOS (Highest Paid Persons Opinion). Decisions based on irrational factors such as your competitors site looks a certain way so “we should do it too”, Amazon does it, my ego wants it, and even “it looks prettier”.
A high-performing website is designed by your user’s interactions based around common website usability best practices and grown from there.
- If your users can’t easily get to (or find) what they want, they can’t buy what you have.
- If your web page, product presentation, whitepaper, etc., doesn’t provide the right value to them in their minds, they won’t be willing to exchange money, information-email address, phone number, and so on for it.
Luckily, your users are telling you how they want your site to work, look, or communicate every day in your analytics data. They tell you how they feel about the perceived value of your whitepapers when they fill out their personal information for it, or when they don’t. When you run a multivariate test, they are also telling you how they want your page to work (i.e. we convert more when your page looks like this!)
Your end user is telling you how to design your site, listen to them – they are the ones who you’re trying to convert to a lead or generate a sale from, not your co-workers. Now, why would you listen to anyone else?
photo by Darren Hester
If you have one Google Analytics profile tracking multiple subdomains (or you want to do so); for example, one profile tracking:
http://www.MyCoolWebsite.com
http://blog.MyCoolWebsite.com
http://youlove.MyCoolWebsite.com
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:
http://www.MyCoolWebsite.com/contact.html
http://blog.MyCoolWebsite.com/contact.html
http://youlove.MyCoolWebsite.com/contact.html
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><script type=”text/javascript”>
var pageTracker = _gat._getTracker(”UA-xxxxxx-x”);
pageTracker._setDomainName(”MyCoolWebsite.com”);
pageTracker._initData();
pageTracker._trackPageview();
</script>
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:
www.MyCoolWebsite.com/contact.html
blog.MyCoolWebsite.com/contact.html
youlove.MyCoolWebsite.com/contact.html
Here is how to set up the Advanced Filter for Tracking and Separating out Subdomains:
- First log-in to Google Analytics.
- Select edit from the profile you created that will track all of the subdomains.
- Select edit in the Filters Applied to Profile section.
- 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
