Articles written in July, 2012

July 24 2012

How to Create a Content Experiment in Google Analytics

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In my last post, I discussed the deprecation of Google Website Optimizer and Google’s Announcement of Content Experiments in Google Analytics. As a quick reminder, Website Optimizer is a testing tool that automates A/B and multivariate experiments. Content Experiments is an A/B testing tool that is integrated into Google Analytics. The benefits of such integration are many:

  • Less code to implement
  • Analyze and test from the same interface
  • Easier to deploy (See the first bullet above)

Creating A Content Experiment in 7 Easy steps.

So you may be thinking, “Seven steps? In the GA interface there are only 4 steps!” And you are correct, there are four steps to creating an experiment, but there are three critical prerequisites.

  1. As you have probably discerned, a Content Experiment is for testing another version of content. You will need to have already identified a critical page in your conversion funnel or an underperforming landing page to test.
  2. After selecting your test case or “A” page, you need to have a hypothesis to test. For example, if you move the shipping cost calculations to this page/step in the checkout flow, more visitors will proceed to the next step and purchase your products.
  3. Once you have your hypothesis of how to make your conversions jump, you’ll need someone (creative team, vendor, freelancer) to create the new design/ functionality etc. of the B page that incorporates your great new idea(s).
  4. Once you have the prerequisites complete; drill into the GA Interface and go to Content>Experiments and enter the location of the page you want to improve.
  5. Enter the location of the “B” version of the page that you’d like to test.
  6. Set your options. Remember that you must have goals configured and the larger the audience, the faster you’re likely to have a winner declared.
  7. Add extra, system generated coding to the “A” Page and have GA verify that all elements are in place.

So the final step is to launch your experiment. In our next post, we’ll explore some next steps and best practices once the experiment is complete. For now, you should know that once you’ve launched the experiment, GA will dynamically serve your “B” page instead of the original page at the rate that you’ve determined in step 6 above. Once the system has enough data to determine which page (A or B) is more effective; it will declare a winner and you’re on your way to more conversions!

July 13 2012

Small Goals – Big Results, Using Micro Goals in Google Analytics

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Since childhood we have been told to “Think Big”, “Dream Big” and that “Bigger is Better.” But are these adages true for companies trying to define their Google Analytics goals? As marketers, we are trained to identify macro goals first. These are the goals that are most closely tied to ROI. They are our conversions and the money makers. However, the pressure to “Go big or go home” often leads us astray; defining success by strictly a number of conversions.

In the ever growing online industry, one size truly does not fit all. If we continue to look to macro goals as the only success metric, we end up manipulating data and ignoring the obvious misfit. In many cases, and as any jewelry adoring woman will tell you, good things in life can come in small packages.

When defining Google Analytics goals for your website, try to look at the big picture. If you are gauging success solely on conversions, you may be missing out on an opportunity to capture meaningful data on a smaller scale. Micro goals, or micro conversions, are a way to strategically track visitor engagement across your site. Though we would like to believe visitors come to our site strictly to convert; that is simply not accurate. Visitors browse, check out blogs, download PDFs and generally learn more about your company before they determine if they will officially engage. By tracking these behaviors, online marketers can begin to draw conclusions about events that eventually lead to conversions. Common micro goals include:

  • Using a Live Chat Feature
  • Social Engagement
  • Signing up for Newsletters
  • Viewing the Contact Us Page
  • Dowloading PDFs or Whitepapers
  • Viewing Videos
  • Submitting a Comment or Post

You might discover a visitor who downloads your white paper is 5 times more likely to submit a contact form and ultimately convert. The trick is to determine which of these activities are meaningful to your business and emphasize them in your future marketing plans. While micro goals will never replace the traditional conversions goals, they should be considered as part of the overall picture of customer site behavior and the sales funnel.

July 2 2012

Announcing Content Experiments in Google Analytics

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After your last log-in to Google Analytics (GA) you may have noticed that you have access to a new sub-section of the content reports. Google has launched Content Experiments by integrating an A/B testing tool into GA. Google also announced that Google Website Optimizer (GWO) will be going dark sometime in August 2012 as a result. Therefore, the rollout began with users that have run an experiment in Google Website Optimizer (GWO is an A/B and multivariate testing tool) and is continuing gradually to all users. If you are not sure whether you have this new feature yet, browse to Content > Experiments.

So is this a big deal? YES! If I’m not telling people they should always be segmenting then I’m saying you should always be testing. Content Experiments in GA allow you to run an experiment and improve your website performance right from where you do your analysis. Previously, you would identify a poor performing landing page in GA, for example, and then you would need to log-in to a testing tool like GWO to set up an experiment.

In addition to the unified analysis and testing that GA now provides; it’s also become easier to launch a test using a 4 step process that only requires you to have Google analytics coding on your site and a small extra control script on the A version in your test.

In my next post I’ll walk you through setting up an experiment, but for now you should know that you must have the control script on the A page as I mentioned above and a B version must also be live on your site for the experiment to launch. So you’ll also need a little help from your web team for the code and perhaps creative teams for the new landing page version. Start thinking of what you’d like to test and we will walk through how to tackle it.

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