How to use A/B Testing to boost your results

What is A/B Testing?

This is a statistical way of differentiating two or more versions, such as version A or Version B. To not only understand which version performs better but also to understand if a difference between two versions is statistically significant.

Why do businesses conduct A/B tests?

This is one of the ways businesses are run today and have to take a data-driven approach. A common difficulty that companies face is that they think they understand the customer, but in reality, customers would behave much differently than you would think. Now, let’s take a look at the nitty-gritty, and find in detail how to use A/B testing to boost your SEM results.

A/B Testing Process

• 1
  Select Your Variable

  There are many variables you want to test while optimising web pages and emails. So you will have to isolate one “independent variable” to test. That is, A/B enables for testing of more than one variation, as long as they are being tested one at a time. Any design or layout elements about which you’ve been curious are up for testing, from landing page fonts to CTA button placement to email subject lines. Consider creating a hypothesis to estimate using your results. Keep in mind to keep things simple, don’t test multiple variables at once.

• 2
  Choose On Metrics

  The metric will be the “dependent variable” on which you will decide to aim all through the test. Have a decision about where you want this dependent variable to be at the end of the split test. You may also state an official hypothesis and evaluate your results based on this prediction.

  If you take a long time to think about which metrics are important to you, what your goals, and how the changes you’re proposing may upset user behaviour, then you might not be able to set up the test in the most effective way.

• 3
  Split Up Your Groups

  The next thing that needs to be firm about is the control, which will be the unchanged version of the piece that you will be testing. For testing, with control and test developed, you can divide your audience into equally sized, randomised groups. How you do this will vary depending on the A/B testing tool you use.

• 4
  Test both variations simultaneously.

  Whether it’s a time of day, day of the week, or month of the year, timing plays an important role in your marketing campaign’s results. If you had to run Version A during one month and Version B a month later, how can you know the performance change was brought on by the different design or the different month?

  You’ll need to run the two variations at the same time, when you run A/B tests, otherwise you may be left second-guessing your results.

  The only exception here is if you’re testing timing itself, like finding the optimal times for sending out emails. This is an amazing thing to test because relying on what your business provides and who your subscribers are, the optimal time for subscriber engagement can change remarkably by industry and target market.

• 1
  Examine The Results

  It’s time to interpret your findings using your pre-established hypothesis and key metrics. Retaining confidence levels in mind as well, it will be needed to understand statistical significance with the support of your testing tool or another calculator. If one variation shows statistically better than the other, then it’s a success. You can now take action properly to optimise the campaign piece.

  Also keep in mind that statistical significance does not equate to practical significance. You will have to take into consideration the time and effort it will take to execute the change and whether the return is worth it.

• 1
  In Case Of A “Failed’ Test

  If any of the variations developed statistically significant results– that is, the test was invalid, many choices are available. For one, it can be acceptable to simply keep the original variation in place. You can also select to redefine your significance level or re-prioritize several KPIs from the context of the piece being tested. At last, a more powerful or drastically different variation may be in order. Very importantly, don’t be feared to test and test again; after all, repeated attempts can only help to improve optimization.

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