{"id":86327,"date":"2020-05-20T10:41:00","date_gmt":"2020-05-20T17:41:00","guid":{"rendered":"https:\/\/thegood.com\/?post_type=insights&p=86327"},"modified":"2023-10-09T07:29:03","modified_gmt":"2023-10-09T14:29:03","slug":"a-b-testing-tools","status":"publish","type":"insights","link":"https:\/\/thegood.com\/insights\/a-b-testing-tools\/","title":{"rendered":"How A\/B Testing Tools Work (A Guide for Marketers)"},"content":{"rendered":"\n

A\/B testing tools can seem daunting, even though the concept is simple.<\/p>\n\n\n\n

In an A\/B test, you\u2019re showing two versions of a web page \u2014 or even smaller changes within a specific web page \u2014 to visitors. Half see one variant, half see the other.<\/p>\n\n\n\n

You determine the effectiveness of each by observing visitor actions. The variant that prompts them to do what you want them to do most often (buy something, sign up for something, click a link, etc.) is the winner.<\/p>\n\n\n\n

That process \u2014 testing one variation of a web page against another \u2014 can continue indefinitely. There\u2019s always room for improvement and new items to test. A\/B testing<\/a> is a direct method of field-testing your ideas.<\/p>\n\n\n\n

But how do you set up A\/B testing on your ecommerce site? Do you need to subscribe to a service, or can you do it for yourself? How complicated is the backend process?<\/p>\n\n\n\n

That\u2019s exactly the kind of information we\u2019re going to cover here.<\/p>\n\n\n\n

How do A\/B testing tools work?<\/h2>\n\n\n\n

At the guts of every A\/B testing tool is a snippet of code that decides which variation of a particular web page each visitor will view. In its most simple form, half see the \u201cversion A\u201d and half see the \u201cversion B\u201d.<\/p>\n\n\n\n

The code can either be placed server-side or client-side, but the majority is done client-side via Javascript using a testing tool like VWO,<\/a> Optimizely<\/a>, or one of the dozens of testing tools on the market today.<\/p>\n\n\n\n

Both perform exactly the same: some visitors see version A, the others see version B. Here\u2019s what the process looks like:<\/p>\n\n\n\n

  1. You add a snippet of Javascript code (in your website header like Google Analytics) for the platform you are running.<\/strong><\/li>
  2. You specify the URL that you will be running the A\/B test on.<\/strong> This will tell the tool where to cookie the visitors who are being opted into your experiment.<\/li>
  3. Once you\u2019ve entered the URL, you build out the experiment variation.<\/strong> This is where you would add\/remove\/move elements from your original design (control).<\/li>
  4. After you\u2019ve created the variant, you can then specify the goals you want to measure.<\/strong> What metrics will tell you whether your new variant was a success?<\/li>
  5. Next, when you begin the campaign, the tool will begin diverting traffic to variant B<\/strong> (or others if you\u2019ve decided to run a multivariate test).<\/li>
  6. Depending on the variant the visitor is opted into, the web page code is altered by the testing tool\u2019s Javascript code before it is rendered in the visitor\u2019s browser.<\/strong><\/li>
  7. Finally, the testing tool\u2019s cookie tracks which variant the visitor was opted into<\/strong> and whether they take specific actions to be counted against the test goal.<\/li>
  8. From this point on, you then need to wait for your test to reach enough results<\/strong> to declare an objective winning variant.<\/li><\/ol>\n\n\n\n

    The ultimate goal behind these A\/B tests is to run multiple subsequent tests built on the learnings from the previous experiment and use those insights to make incremental improvements in the pages being tested.<\/p>\n\n\n\n

    Whereas user testing measures how easily a user can achieve their own goals (find a product, select a color, check out and arrange shipping, etc.), A\/B testing focuses on getting visitors to take actions you want them to take.<\/p>\n\n\n\n

    That\u2019s a significant difference.<\/p>\n\n\n\n

    They are both useful for conversion rate optimization, but it\u2019s important not to mistake one for the other. The effectiveness of your testing will hinge on your wise use of strategy.<\/p>\n\n\n\n

    \"a\/b<\/figure><\/div>\n\n\n\n

    Why is A\/B testing important for conversion rate optimization?<\/h2>\n\n\n\n

    It\u2019s tempting for ecommerce managers to rely solely on personal experience and input from the marketing team when considering web page design choices. You call a meeting, consider the options, then choose the winner by either group consensus or by making a gut-level management decision.<\/p>\n\n\n\n

    That\u2019s fine for getting started, but the real question is will it work with real customers? What happens when the web page goes live? Will visitors to your site understand what to do next \u2014 and will they do it?<\/p>\n\n\n\n

    That\u2019s where A\/B testing comes in. You let the users show you which variation is more effective. They don\u2019t tell you, they show you with their actions.<\/p>\n\n\n\n

    Here are three primary reasons why A\/B testing tools are vital:<\/p>\n\n\n\n

    1. A\/B testing tools bring objectivity to your marketing decisions.<\/strong> What you think will be best may not be the best. A\/B testing will uncover the truth.<\/li>
    2. A\/B testing tools allow you to make one small change after another, seeking to get better results with each variation.<\/strong> Before long, you may look back to see those little incremental wins added together have produced a big win.<\/li>
    3. A\/B testing tools make it possible for you to perform an ecommerce website redesign one small chunk at a time.<\/strong> This reduces (maybe even eliminates) the chance you\u2019ll end up with something your users don\u2019t like.<\/li><\/ol>\n\n\n\n

      A\/B split testing provides data directly from your visitors. Unlike surveys, where you might ask prospects about their preferences, A\/B testing tools make it possible for you to see what they do when given two options.<\/p>\n\n\n\n

      Does one illustration work better than another?<\/p>\n\n\n\n

      Could your navigation be organized better?<\/p>\n\n\n\n

      Should that call-to-action button be big or small, red or blue, round or rectangular?<\/p>\n\n\n\n

      Do your visitors buy more when given one product description style over another?<\/p>\n\n\n\n

      A\/B testing will tell you. A\/B testing is for those who prefer objective data over theory, and sales over discussions about sales.<\/p>\n\n\n\n

      \"a\/b<\/figure><\/div>\n\n\n\n

      TreeRing increases revenue by 53 percent: a case study<\/h2>\n\n\n\n

      To get at the A\/B testing process, let\u2019s use a real-life example:<\/p>\n\n\n\n

      TreeRing is in the school yearbook business. The company\u2019s unique social-first approach to capturing school memories gets TreeRing plenty of notice from students, parents, and school staff members. Despite all the attention, though, TreeRing\u2019s conversion rate was not as high as they would have liked it to be. Consequently, revenue goals weren\u2019t being met.<\/p>\n\n\n\n

      After trying on their own to turn sales around, TreeRing management called The Good for help.<\/p>\n\n\n\n

      By using a conversion audit to gain insight on where stuck points and bottlenecks existed along the path to sales, the conversion optimization team from The Good identified areas of opportunity, developed a detailed testing roadmap, and launched a robust regimen of A\/B testing aimed at converting more of the existing visitors to customers.<\/p>\n\n\n\n

      In one A\/B test series, for instance, the team experimented with the location of the \u201cRequest Free Sample\u201d link. Those tests alone resulted in a 42 percent increase in visits to the Free Sample page and boosted conversions by 12 percent.<\/p>\n\n\n\n

      Following the initial round of A\/B tests \u2014 just three months after beginning the process \u2014 year to year revenue for the quarter was up by 53 percent!<\/p>\n\n\n\n

      Here are observations made and the steps taken to ignite TreeRing revenue:<\/p>\n\n\n\n