{"id":10589,"date":"2016-04-21T11:30:33","date_gmt":"2016-04-21T16:30:33","guid":{"rendered":"https:\/\/www.hostgator.com\/blog\/?p=10589"},"modified":"2019-04-30T11:08:32","modified_gmt":"2019-04-30T16:08:32","slug":"google-content-experiments","status":"publish","type":"post","link":"https:\/\/www.hostgator.com\/blog\/google-content-experiments\/","title":{"rendered":"The ABCs of Creating An A\/B Test With Google Content Experiments"},"content":{"rendered":"

\"The<\/p>\n

Which of these two calls-to-action<\/a> would be more successful when placed on one of your website\u2019s pages?<\/p>\n

'Want to learn more? Click here!'\u009d<\/strong>
\n'Limited-time offer click here now!'\u009d<\/strong><\/p>\n

Think that\u2019s an easy one to answer? Actually, it\u2019s just a rhetorical question; there\u2019s no correct answer. For example, a reader just being introduced to a product might respond better to the first option offering more information, while another who\u2019s already been pre-sold might jump on the second choice. Different audiences will respond to content in very different ways.<\/p>\n

In fact, there\u2019s no legitimate way to determine which one would be 'more successful'\u009d until you define 'success.'\u009d Is it total number of sales? Gross income? ROI? Size of the email list you\u2019ve built? You have to specify the goal you\u2019re trying to accomplish before you can judge 'success.'\u009d<\/p>\n

Choosing between calls-to-action<\/a> is difficult enough. It\u2019s even harder when you\u2019re trying to decide between two different versions of an entire page. That\u2019s when A\/B testing becomes imperative. And thankfully, Google Analytics now makes it easy to create an A\/B test for your content, with Google Experiments.<\/p>\n

What Is A\/B Testing?<\/h2>\n

Even if you\u2019re not familiar with the concept of A\/B content testing, it\u2019s simple to understand.<\/p>\n

Let\u2019s say you\u2019re considering changing one of your website\u2019s pages (call it 'content A'\u009d) and have written an alternate version of the page (call it 'content B'\u009d) that you think might convert better with your visitors. If you show half of your traffic 'content A'\u009d and half of your traffic 'content B,'\u009d and then measure the results (remember, you need to define your goal in advance) you\u2019ll be able to tell which page works better and whether you should make a permanent change.<\/p>\n

You might think that anything that sounds so easy must be difficult or expensive to implement. It was, once upon a time, and there are still a number of complicated and costly in-house software programs you can use for A\/B testing and much more sophisticated analysis. However, there are now a number of web-based solutions which will let you do A\/B content experiments, from the inexpensive Optimizely<\/a> (which has a simple-to-use WYSIWYG interface) to the well-known and much pricier KISSmetrics<\/a> (which lets you dive deeply into a massive pool of data).<\/p>\n

For standard A\/B content testing, though, you can\u2019t beat the Experiments system<\/a> that\u2019s built into Google Analytics. That\u2019s partly because it\u2019s surprisingly full-featured, partly because it\u2019s not too difficult to set up and mostly because it\u2019s absolutely free.<\/p>\n

Let\u2019s look at how it works.<\/p>\n

How To Create an A\/B Content Test With Google: The Preliminaries<\/h2>\n

We\u2019ll assume that you already have a Google Analytics account and that the code is installed on your website. If not, go ahead and set it up<\/a>. We\u2019ll wait.<\/p>\n

Welcome back! You probably think that we\u2019re going to dive right into setting up an A\/B content test, but that\u2019s getting ahead of things; you wouldn\u2019t pull out a recipe and start baking unless you knew you had all of the ingredients you needed on hand, and we\u2019re going to do the same thing here: ingredients first.<\/p>\n

To start, make sure you have your 'B content'\u009d the page that you want to test against your current page ready to go and posted on your website, with the URL readily available so you can cut and paste when you need it. (You can actually test up to twelve pages at one time in the Google Analytics Experiments tool, but for now, it\u2019s easier to discuss A\/B testing than A\/B\/C\/D\/E\/F\/G\/H\/I\/J\/K\/L testing.)<\/p>\n

If you\u2019re going to test click-thrus to another page, have that URL available as well. Next, decide exactly what you\u2019ll be using as your criterion for success; most will want to maximize revenue or transactions, but some may want to track the time spent on the page, ad clicks, or another metric. It\u2019s important to make this decision before setting up your test.<\/p>\n

Finally be sure that your Google Analytics tracking code is posted on both pages that you\u2019ll be testing.<\/p>\n

How To Create an A\/B Content Test With Google: The Experiment<\/strong><\/p>\n

OK, let\u2019s get started. Open your Analytics control panel, where you\u2019ll select the 'reporting'\u009d tab, then look in the left-side navigation bar for the 'Behavior'\u009d section, and click on 'Experiments'\u009d underneath it. Click the 'START EXPERIMENTING'\u009d button on the next page, and you\u2019re ready to set up your A\/B test on the screen that opens. (If for some reason there\u2019s already an experiment set up in your account, click on 'Create experiment'\u009d to start a new test.)<\/p>\n

The 'Create a new experiment'\u009d screen will open.<\/p>\n

    \n
  1. Enter whatever name you\u2019d like to use for the test in the 'Name for this experiment'\u009d field; make it something unique that will be easy to recognize later because your test will be running for weeks or months, and you may want to create other experiments in the meantime.<\/li>\n
  2. 'Objective for this experiment'\u009d is the most difficult area of this screen. It\u2019s where you\u2019ll be identifying the criterion for success you decided upon in the last section. There will be existing choices in the 'Select a metric'\u009d drop-down menu like Adsense (to measure impressions or clicks), Ecommerce (to measure revenue or transactions), and Site Usage (to measure metrics like time spent on site or average number of page views). If you want to use an Ecommerce metric, you\u2019ll need to have Ecommerce reporting enabled in your main Analytics setting and the correct JavaScript added to your pages. You can get exact instructions about this in the Ecommerce section of your Analytics dashboard.<\/li>\n<\/ol>\n

    You can also create your own goal, such as click-thrus to a new page or number of video views, by clicking 'Create a new objective.'\u009d The easiest way to set this up is to select 'custom'\u009d on the first page that comes up, enter a name for the goal and select its type (Destination, Duration, Pages or Event) on the second page, enter the relevant destination page or event (the one you have ready to cut and paste) and an optional monetary value per click or sale on the third page, then click 'Save goal.'\u009d Click back to the 'Objective for this experiment'\u009d page and you\u2019re all set. Don\u2019t worry, this is actually a lot<\/em> easier than it might sound.<\/p>\n

      \n
    1. The next choice on this screen is 'Percentage of traffic to experiment.'\u009d This selects how much of the traffic to your site will see the original page and how many will see your 'B content'\u009d page but there\u2019s one tricky thing to consider. With this drop-down, you\u2019re choosing what percentage of visitors will be participating in the test, not<\/em> how many visitors will see each version of the page. That means that if you choose 50%, you\u2019re not<\/em> deciding that 50% of visitors will see 'A'\u009d and 50% will see 'B.'\u009d You\u2019re deciding that 50% will see 'A'\u009d by default (the normal page they\u2019d view) and 50% will be entered into the A\/B experiment so 75% of your visitors will end up seeing 'A'\u009d (50% + 25%) and 25% will see 'B.'\u009d Bear this in mind when making your selection.<\/li>\n<\/ol>\n

      One other note: if your alternate page is very different than the original, you may want to limit the percentage of visitors participating in the experiment in order to minimize potential revenue or conversion losses. You can always increase the percentage in the middle of the experiment if things are going well.<\/p>\n

        \n
      1. The 'Email notification'\u009d choice is self-evident.<\/li>\n<\/ol>\n
          \n
        1. 'Advanced options'\u009d has one important selection you must make. Checking 'Distribute traffic evenly across all variations'\u009d will ensure that each of your pages continues to receive an equal amount of test traffic. If it\u2019s not enabled, Analytics will automatically start sending more traffic to the page that\u2019s performing better. The former will give you a test that\u2019s standardized across the testing period, while the latter will start maximizing performance as the test proceeds while still rendering accurate results. There are two other advanced options you can consider: 'Set a minimum time the experiment will run'\u009d will prevent Google from naming a 'winner'\u009d too soon, and 'Set a confidence threshold'\u009d allows you to decide how decisive a measurement you want before Google declares one page better than the other.<\/li>\n<\/ol>\n

          Allow yourself a sigh of relief. It now gets much easier. After you\u2019ve saved your changes, the next screen is 'Configure your experiment.'\u009d This is where you copy and paste the URLs for the 'A content'\u009d and 'B content'\u009d pages; click 'Save Changes'\u009d and you\u2019ll be shown the experiment code for the A\/B test. Copy it and paste it onto the 'A'\u009d page, right below the <head> tag near the top, then click 'Save Changes'\u009d again.<\/p>\n

          If you\u2019ve done everything right, Google will validate the code and tell you that you\u2019re all set or if there\u2019s a problem, you\u2019ll be shown the errors which need to be corrected. In rare cases, Analytics won\u2019t be able to find the code on a complicated page or a web server whose settings prevent it. If this happens and you\u2019re sure you haven\u2019t made any mistakes, don\u2019t worry about the validation. Click 'Start Experiment'\u009d and off you go. (You can also choose to 'Save for later'\u009d if you\u2019re thinking about making changes.)<\/p>\n

          How To Create an A\/B Content Test With Google: The Results<\/h2>\n

          Once everything is set up your A\/B experiment will start right away, and after a day or two you\u2019ll begin seeing results which can be viewed in your Experiments list.<\/p>\n

          The main window will show the test\u2019s status, major details and the number of visits the pages have received; if you click on the experiment\u2019s name, you be taken to a more detailed window. There you can see a wealth of information in table and graph form, based on the goal you selected when the test was set up. This can include the percentage of users who accomplished the goal, and the numerical or monetary value of their goal completion, if applicable. You\u2019ll be able to tell how well each page is doing, see comparisons between page performance, and even Google\u2019s estimate of the probability that the new page will outperform the old one by the time testing is complete.<\/p>\n

          If you allow the A\/B test to run to its normal completion, you will see one of three possible status reports:<\/p>\n

            \n
          1. Ended (Time limit reached), which means the experiment ran for three months (or the time period that you chose during set up) without a clear winner.<\/li>\n
          2. Ended (No winner), which means there was no statistically significant difference between the performance of the two pages.<\/li>\n
          3. Ended (Winner found), which we probably don\u2019t have to explain. The winning page will be identified on the data page, along with all of the specifics.<\/li>\n<\/ol>\n

            FAQ<\/h2>\n

            Here are a few frequently asked questions about the use of Google Experiments for A\/B testing and their answers.<\/p>\n

            Q: Should I start making changes to my site if I see an early trend in my reports?
            \nA: No. It can take several weeks for traffic to stabilize and reliable trends to emerge; even if the numbers look overwhelming, Google won\u2019t declare a winner for at least two weeks to allow data to stabilize.<\/p>\n

            Q: Is it a good idea to test more than two pages at once?
            \nA: It can be, but bear in mind that additional variations will mean that a lot more traffic will be needed to draw reliable conclusions, since visitors will be divided between all of the tested pages. More traffic requires more time, so be prepared to wait longer for results.<\/p>\n

            Q: Should I consider a 'multi-armed bandit'\u009d experiment?
            \nA: For those who aren\u2019t familiar with the term, it refers to the 'Distribute traffic evenly across all variations'\u009d option discussed during the set up of your experiment. You\u2019ll remember that if you don\u2019t choose this option, Analytics will begin diverting more traffic to the better-performing page over time, which is known as the 'multi-armed bandit'\u009d approach (named after a hypothetical slot machine experiment).<\/p>\n

            While this may seem counter-intuitive to proper testing procedures, there are advanced mathematical models showing that this approach is not only statistically accurate but more efficient, so you get results more quickly while maximizing performance. If you trust the science more than your gut, it\u2019s a good alternative.<\/p>\n

            Q: Should I run more than one experiment at the same time?
            \nA: As we\u2019ve mentioned you can up to twelve concurrently, but be aware that as you run more and more tests, they can start interacting and produce results which are difficult to analyze. You can try it, but it\u2019s safer to run just one or two at a time unless you\u2019re sure they won\u2019t conflict.<\/p>\n

            Q: Can you run a Google Experiment with pages that serve dynamic content?
            \nA: Not if the content is served by means of permalink-type URLs. If the pages use query-string parameters, you should be OK.<\/p>\n","protected":false},"excerpt":{"rendered":"

            Which of these two calls-to-action would be more successful when placed on one of your website\u2019s pages? ‘Want to learn…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[],"class_list":["post-10589","post","type-post","status-publish","format-standard","hentry","category-web-and-hosting-tips"],"yoast_head":"\nCreating an A\/B Test With Google Content Experiments | HostGator<\/title>\n<meta name=\"description\" content=\"Looking to create an A\/B test with Google Content Experiments? This post will guide you through everything you need to know. 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