6 tips for sig­nif­i­cant A/B tests

If this is your first time carrying out A/B testing, you should start with rel­a­tive­ly simple tests, changing only small elements like graphics or buttons. A simple A/B test with only one variable is im­ple­ment­ed quickly and delivers accurate and reliable results.

Mul­ti­vari­ate tests are often too complex to start with. This is because they have a higher error rate, and they might deliver falsified results. So the key is to start small, as even little changes can offer big results later on. Run multiple tests in a row, and if each causes a small im­prove­ment in the con­ver­sion rate, these positive effects will add up in the end.

Time also factors into A/B testing. It makes sense to run a test for at least a week, so that you can include any weekday fluc­tu­a­tions. In addition, any abrupt positive or negative impacts can be evaluated re­al­is­ti­cal­ly over the course of a week. A typical error when it comes to A/B tests is im­pa­tience. If you come to con­clu­sions too quickly, you may miss some in­ter­est­ing results. For this reason you should evaluate tests above a sta­tis­ti­cal sig­nif­i­cance. In order to check whether results are sig­nif­i­cant, you can use the free A/B Split Test Sig­nif­i­cance Cal­cu­la­tor.

Hy­pothe­ses are the basis of each A/B test. At the same time, it’s chal­leng­ing to formulate these and record them. Efficient A/B testing is based on clear hy­pothe­ses, where you can work out exactly which in­flu­ences have had changes on user behavior. Whether your hy­pothe­ses are confirmed or disproved in the test, you gain more valuable insights into what you are testing if they are well thought out.

A/B testing not only helps to increase profit and con­ver­sions, but it is also a survey of potential customers: it helps you get to know your audience better. Any knowledge gained about the target group is helpful, as it can be used in other steps of the targeting process. As a result, it brings you one step closer towards winning over and retaining customers.

It’s possible for A/B tests to run parallel on different subpages at the same time, but this can lead to erroneous results. It’s rec­om­mend­ed to test one change after the other, since this allows you to optimize the website one step at a time. Multipage ex­per­i­ments can be useful if you want to change numerous elements at once. Visitors will either be shown the new page or the old page. Even if you’re just making really small changes on pages, it’s still important that a similar number of visitors are shown each page. Yet it tends to be more ben­e­fi­cial to your results to modify one element after the other.

A possible scenario: after testing a landing page, you find out that variant B scored better in terms of click and con­ver­sion rate than variant A. The con­clu­sion would be to keep variant B and get rid of variant A, but why not go a step further? If you use ad­di­tion­al web analysis tools (such as Google Analytics) alongside A/B testing, you can segment the test group and dif­fer­en­ti­ate, for example, between new and existing customers. The result could be that regular customers react more pos­i­tive­ly to version B, and that new customers prefer version A, so taking out version A would be a mistake. With added web analysis, you get a better view of the test results and, as a result, can draw more specific con­clu­sions.

Resources are sometimes limited with A/B testing – on one hand, the time you’re investing is limited, and on the other hand, so is the number of visitors accessing the site or in­di­vid­ual subpages. In order not to waste these resources un­nec­es­sar­i­ly, you should pri­or­i­tize what’s most important. It makes sense to decide on concrete goals before per­form­ing each test. Marketers often rely on the SMART principle. With A/B testing, your goals should be specific, measurable, attainable, realistic, and timely. You can read more about this principle here.

As long as you have realistic targets, the ap­pro­pri­ate hy­pothe­ses and the proper testing tools, there are many benefits to suc­cess­ful A/B testing. When im­ple­ment­ed properly, split tests are a great op­por­tu­ni­ty for op­ti­miz­ing any online business. Even if it means you have to invest a lot of time and effort at the beginning, it’s def­i­nite­ly worth it: the result is an optimized and improved site that matches your target group’s needs.

More about A/B testing such as the pros and cons can be found in our guide.