In the age of big data, Google Analytics has become a familiar tool for internet marketers. What may have flown under your radar, though, is one of Analytics’ nifty features: Google Analytics Experiments (GAE). (You may know it better by its original name: Google Website Optimizer.) GAE offers Analytics users a great platform for A/B testing with reliable data. Marketers considering doing any A/B testing or website updates should give GAE a look.
How Does Google Analytics Experiments Work?
Integrated with Google Analytics in 2012, GAE is a platform that enables website owners and marketers to conduct A/B testing. This testing is used to determine which elements on a webpage best accomplish a specific goal — for businesses, that objective should always lead to increased revenue.
A/B testing essentially splits web traffic, directing visitors towards an “option A” or “option B” landing page. The two landing pages are designed to look extremely similar, with one or two design elements that differ — so you can test which option performs better in generating clicks, traffic, or revenue. By identifying which elements engage and resonate with your audience, you can better optimize your site to provide a better experience for visitors, increase conversion rates, and ultimately achieve your business goals.
Similarly to Google Analytics, GAE requires you to install a snippet of code on each page you wish to involve in A/B testing. This can get a bit technical, especially if you don’t have full backend control of a website — in that case, GAE offers a “phone a friend” feature, where you can send a request for help to your webmaster. Once you’ve added this snippet of code to each webpage you want to analyze, you will also have access to historical Google Analytics data — a helpful feature that can be used as a benchmark for your upcoming tests.
Benefits of Google Analytics Experiments
GAE isn’t the only A/B testing tool on the market, but it does have quite a few advantages over other tools. The first, and most obvious, is that it’s free — as opposed to its two main competitors, Visual Website Optimizer (VWO) and Optimizely. While paying for either VWO or Optimizely will get you access to some pretty impressive features, GAE remains the best free option around and holds its own against its for-pay competitors.
One huge advantage to using GAE lies in its integration with Google Analytics. In the past, A/B testing results generated through VWO, Optimizely, or another service had to be reviewed using a separate platform — Google Analytics was often the platform of choice for this, but it required marketers to take an extra step in between getting results and analyzing them.
When you conduct A/B testing with GAE, you automatically have access to Google Analytics, as well. This makes both setup and analysis much simpler for marketers. GAE uses the same code on your webpage that Google Analytics uses, with a small addition to the script. So, if you’re already using Google Analytics, you don’t need two separate sets of code — simply make an addition to your original code to include GAE, and you’re all set. After you’ve run an A/B test, you can easily pull up and review your results, as you’re already in the Google Analytics platform; no extra steps required.
Additionally, when you run your A/B testing, GAE can refer to your existing goals in Google Analytics as conversion indicators for your test. No need to do the manual work of coming up with conversion indicators that match your other analytics goals.
If you already use Google Analytics, this integration can make a whole lot of difference in the process of gathering and analyzing data. You can simply set up your tests, run them, and review your analytics all in one spot — streamlining your process, and making it easier to look at your A/B test results in tandem with the other website data you gather through Google Analytics.
Limitations of Google Analytics Experiments
Although GAE was re-launched in 2012 with significant improvements, and its integration with Google Analytics makes for a smooth process, there are still a number of ongoing challenges for users of the platform.
One of the major restrictions of GAE is that it doesn’t allow for multivariate testing. Multivariate testing involves testing multiple variations of a page at once — for instance, testing three different calls to action with three different colored buttons, all at once. Offered by both of its main competitors, GAE’s lack of multivariate testing may hinder its appeal to larger companies that have greater web traffic, and a more urgent desire for data.
Even if you could employ multivariate testing, it would be difficult to manage multiple changes at once, because GAE doesn’t have a visual editor. This means that, unlike Optimizely, you can’t drag and drop, or change features of a test page while looking at the visual display of that page. The elements you’re testing must be switched and adjusted in the backend, before you can see what they look like on your webpage.
Then, there’s the matter of user experience; how visitors to your site interact with your A/B testing. A consistent issue across all A/B testing tools is that test pages will often take longer to load, or will load with a flicker. A flicker — also referred to as a “flash of original content” — tips users off to the fact that they may be involved in a test. Not only can it confuse a web visitor, but it may cause them to interact differently with your site, thereby impacting the quality of data you gather from the experiment.
VWO was the first platform to fix the flicker, with the creation of an asynchronous code. This code quickens the loading of a test page, which, while loaded simultaneously with the original page, removes the jarring technical blip for visitors. GAE has come out with something similar; though you may need web design experience to understand how to use it. GAE users would benefit by having a function like this built into the code they install on their site’s backend.
Getting Started with Google Analytics Experiments
A/B testing is an integral part of creating an effective marketing campaign — allowing you to review and assess not only the design of a page, but the effectiveness of calls to action, recommended promotions, and ad copy. By incorporating and responding to A/B testing, marketers can quickly and easily present customers with their “best face” on the web, using data-driven solutions.
With its restrictions on visual editing and multivariate testing, GAE is a tool best used as an introduction to A/B testing — perfect for a smaller client who wishes to do a bit of testing without having to fork over a monthly paid commitment.
Before getting started with any A/B testing platform, be sure to discuss with your team the outline of your ultimate goal (or goals). Whether it’s to have customers add an item to their shopping cart or click through to event details, objectives will vary based on the unique needs of your business.
If you do decide to go with GAE, Google has created several tutorial pages to help create your very first experiment.
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