How to Construct A Simple A-B Test?

January 18, 2022

In this post, we will focus on one of the basic methods of measuring digital efforts; A-B testing. One of the most significant differences between digital and traditional businesses is that practically every factor can be examined, tested, and optimized in a short period of time using simple methods with low-budget. All you have to do to benefit from this digital advantage is set up properly structured tests and experiments.

From defining the key elements of your e-commerce website to deciding on ad copy, from changing your Instagram captions to deciding whether to develop a product, A-B tests (also known as split tests) may help you make data-driven business decisions instead of your senses. Understanding A-B tests is also important to be able to build multivariate tests. Multivariate tests are practically a few A-B tests that run together.  

Just use the tools you have for a well-structured A-B test that can enable you to take less risk, increase your return on investment, increase your conversion rates, and get to know your customers and their expectations better. By following this article, you will have a beginner guide for running meaningful A-B testing. 

Is A-B Testing a New Method?

Although the term “A-B testing” became popular in the 1990s as digital businesses grew rapidly, it does not refer to a new process. In digital, we can refer to a method, commonly known as the split test or two-sample hypothesis test. It is, in reality, an application of methods established by statistician Ronald Risher in the 1920s to digital systems. In the 1960s and 1970s, these strategies, which were first used in clinical medicine as randomized control experiments in the 1950s, became more widely used in marketing.

What is A-B Testing? 

A-B testing is an easy-to-apply performance test in which a second version of one of the variables is created to test a hypothesis while other conditions are kept constant. The version A where the variable remains constant is called the control, and the version B where the variable is changed is called the variation. Control and variation can be separated only by the way a header is aligned, or they can be two completely different landing pages.

For example, when reviewing your user records, you thought that the call-to-action (cta) button on your ad landing page was not noticed because it was light gray, so it might not be clicked, and you think that the red button will increase click-through conversion. In this case, your hypothesis is that the red call-to-action button will result in higher conversion rates. To test this hypothesis, you configure a simple A-B test that will compare your page A with gray buttons (control) to page B with red buttons (variation).

You show half of the traffic coming to your website through your ad, version A and the other half version B until you have meaningful data, and you determine which version the button performs better. Changing this experience may have no effect on users’ behavior, and having a single button red instead of gray can significantly increase your conversion rate. 

With a properly structured A-B test, you can test your e-commerce website, your e-mail text, the titles of your ads, the size of your product package, the color of your logo, the size of your CTA button, your discount hours, in short, every variable you can generate hypotheses; You can determine which version converts better. 

Why is A-B Testing Necessary?

Even in a digital product that complies with all UI and UX principles, is based on detailed market research, and has been developed based on the best practices in the industry, users may not behave as you imagined. A-B testing is one of the easiest and most cost-effective ways to identify changes that need to be made for conversion optimization and see if their hypotheses will work.

You can choose to apply the A-B tests to a completely new audience through advertisements with a test budget, or you can choose to show it to half of your existing audience with the development required for the change you want to make, without incurring any extra cost. With the data and insights you will obtain, you can continue with the variation you decide to have better results. Even when all goes well, you should test your hypotheses to see if your business’s growth potential is greater than you think. 

A-B Testing for Conversion Optimization in 4 Steps

🔘 Clearly Define Your Goals

Set specific, measurable, reasonable, time-limited goals.

You can set a goal such as targeting a 20% increase in call conversion in the next quarter.

You should also determine the KPIs that will measure this goal. Clicks, increase in visitors, increase in sales, increase in cart volume? What are the lenses that will measure your target correctly, what is the current status of these lenses, and how much increase do you expect at the end of your test?

You should not set a vague and immeasurable goal like we want our brand awareness to increase, as well as online visibility.

Optimal targets for optimization are usually aimed at eliminating negativities. Setting goals for your current problems, such as increasing the average time spent on your site, providing more than two page visits, will allow you to get results as soon as possible.

The goal you set should be related to your hypothesis and as closely related as possible. If you are testing the sentence on the call-to-action button of the form for which you want contact information, it makes more sense to measure its performance from button clicks, not increased sales. Of course, when you optimize the elements, your sales will increase indirectly, but it would be more reasonable to measure click performance rather than continuing your A/B test until you see the effects on sales.

🔘 Structure Your Hypotheses Well

A successful A/B test that provides meaningful data means the hypothesis is well developed. The hypothesis you will test should be based on field insights and/or available data. You can share the hypothesis with others and gauge their response before investing in testing to see if it’s a logical and commonplace assumption. 

For example, if you want to determine the reasons for your visitors to leave your site, you can start hypothesizing with the aim of optimizing the most popular exit page in the last 3 months. 

By prioritizing your hypotheses, you should determine what you need to test first. Starting at the weakest point in your sales funnel can be a good solution for quick results. 

You have to make sure your KPIs are in line with your hypothesis. 

Testing one change at a time will ensure you get the clearest possible results, so base your hypotheses simple and based on one variable. Simplify your complex hypotheses.

🔘 Create the Variation That Fits Your Hypothesis

In this step, construct the variation with the change on which you base your hypothesis. Make sure you have two versions of the button color, the location of the form, the url structure, whatever you are going to test.

Make the change in the easiest way. Your variation doesn’t have to be perfect, it doesn’t have to be complete, you don’t have to wait until everything is perfect. Remember, you just need to make the change you want to measure performance as soon as possible and at the least cost and start the test. 

🔘 Start The Test

To test a change you’ve made to your landing page or to measure other changes to your e-commerce site, ads are an easy way to get results in a short time. Facebook has a simple and directive A/B testing option in its ad panel. You can start your test by making any changes you want right after you set up your Facebook, Instagram or Audience Network ads. You can also benefit from the internal A/B testing option on platforms such as Google and Yandex. If you wish, you can also configure your test yourself by creating a different ad group. 

You can also perform A/B testing by directing your existing traffic (for example, by creating different pages for different language browsers or by meeting a randomly selected half of your members with a version) without incurring any advertising budget costs. 

You can use free tools to calculate the time you need to run the test to get meaningful data. 

A-B Testing Tools 

A-B testing, also known as split testing, is one of the most powerful ways to improve your conversion rates, revenue and ultimately, profit. But finding the right tool for your business is tough.

We believe that the best A-B testing tools are definitely an imperative for increasing conversions and improving your customer experience. There are a lot of A-B testing tools you can choose from. 

Here is a list of  A-B Testing Tools you can choose from:

  1. Google Optimize
  2. Amplitude Experiment
  3. Optimizely
  4. VWO
  5. Adobe Target
  6. Omniconvert
  7. AB Tasty
  8. Kameleoon
  9. Convertize
  10. Apptimize
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