This Is How You Implement Your A/B Tests Correctly
We explain to you what A/B tests are and how you can use them effectively for conversion optimization.
Better conversions and more sales with the same amount of traffic on your website? How is that supposed to work? The simple solution: A/B tests. They help you to make step-by-step data-based changes to your website without losing sight of your target group. This way, you can optimize your website in the long term and get the most out of it.
What you can use A/B tests for and how to conduct them, you can read in this article. In addition, we give you some pros and cons of A/B tests and useful tools that can assist you in website optimization.
1. What is A/B testing?
Basically, A/B testing (also called split testing) refers to the comparison of at least two variants of a system (such as a website). Here, you can test two or more versions against each other that slightly differ from each other. The versions are then served to your target group at random, and their behavior and reactions are recorded in an A/B testing tool.
A/B tests are commonly used in online marketing to measure and compare the performance of a page. This allows minimal changes to be observed and analyzed to determine the optimal variant.
For you as a company, it can be a helpful means for the conversion optimization of your online storeor your website, i.e., the conversion of visitors into customers. In addition to websites, you can of course also use A/B tests in native mobile apps for iPhone or Android, or test marketing campaigns via server-based APIs.
A/B tests are differentiated into three different types:
- Classic A/B test: The classic A/B test compares two or more variants of a page under the same URL. Users randomly access one of the variants. It is very important to test only individual elements, no matter how small they appear, otherwise separate tests will have to be conducted additionally.
- Split test (also redirection test): In the split test method, the traffic of your site is redirected to another or several other URLs, such as different landing pages. Split tests are therefore often used for new pages.
- Multivariate test (MVT): This type allows testing multiple combinations of an element on a specific page. This way, for example, different color and text variations of a button can be tested simultaneously.
In addition to the classic tests, a change initiative can also be bridged as a “patch” - a variation is played out to 100% of the traffic.
2. What can A/B tests be used for?
With A/B tests, you can basically test all aspects of your website against each other. Whether it's the color of your call-to-action button, the size of your logo or the display of discounts. Here you find an overview of the different application possibilities:
- Titles, headings, and content
- Call-to-action
- Buttons
- Images
- Forms
- Page structure
- Designations
- User guidance
- Product and price presentations
- Algorithms for further product suggestions
- and many more
Thus, the A/B test is among the measures of conversion optimization. For this we have an article with the seven best conversion optimization tools, if you want to know more about the topic.
3. How do I build an A/B testing strategy?
Depending on the website and company, an A/B test can be carried out individually. However, before you start your first A/B test, you should have the right preparations. The basis of an optimal A/B test is always the right strategy. To build an A/B testing strategy, you should best follow these three steps:
- Continuous analysis and idea backlog: First, you need to find out which aspects should be tested in order to reach which goal. For this, you can use analysis data from your web analytics tools or even usability tests. For example, key metrics such as the click-through rate, the bounce rate, or similar click rate statistics can help you select your object of investigation. Ideally, you have analyzed all possible stages of the funnel for disturbances and also involved other teams in your collection of ideas.
- Prioritization & roadmap creation: Here it is important to prioritize your ideas and investigation objects according to potential, importance & feasibility. You can also plan special campaigns or actions. However, be careful not to do too many tests at the same time, so that the effects remain attributable.
Formulating hypotheses: Once your investigative goals are defined and prioritized, you should formulate appropriate hypotheses for problem-solving. Your identified problem (aspect of investigation) and the probable cause are an important part of your hypothesis. Essentially, you need to question whether the placement, color, size, etc. of your aspect of investigation is effective or more of a hindrance from the user's perspective. Finally, the potential solution and achieving the goal will also be included in the hypothesis. - Awareness creation: With the help of internal newsletters, competitions and much more, you can raise more attention and awareness of your A/B testings. Your ultimate aim should be to establish the culture of experimentation throughout the company.
4. How do I set up an A/B test with AB Tasty?
Once you've finalized your A/B testing strategy and developed hypotheses, you can start conducting the A/B test. This can typically be broken down into seven basic steps:
- Implement your chosen A/B testing tool by embedding tags on all relevant pages.
- Create the Variations of your investigative aspects in your tool, e.g., with the WYSIWYG editor from AB Tasty.
- Set your goals (primary and secondary KPIs), for example, transactions, clicks, or achieving certain pages depending on the hypothesis.
- Find the right Targeting for you and your target audience (Where, Who, When?). Also consider your targeting criteria such as source or device and use, if applicable, templates from AB Tasty.
- Determine your traffic allocation. Normally, you should distribute traffic evenly across the variations. However, it is also possible to do a Dynamic Traffic Allocation.
- Do an initial trial run of the A/B test in QA mode.
- Finally, run your A/B test and evaluate it afterwards.
In the WYSIWYG editor of AB Tasty you will be guided securely through all important steps to create your test.
Which tool you should use best for your A/B tests depends, among other things, on the size of your company and your required functionality. Do you already have previous knowledge of A/B testing? Or rather not? There are different A/B testing tools, from easy applications for small business owners to complex enterprise solutions with all-round service. At the end of this post, we'll share with you a few of the most relevant A/B testing tools on OMR Reviews.
5. How do I correctly evaluate a test?
After successfully carrying out your A/B tests, you can evaluate the collected data and use the results obtained to derive actions for your marketing goals. When your A/B test is done can usually be seen directly in the A/B testing tool. AB Tasty, for instance, shows you when a statistical significance has been reached and you have obtained valid results. The probability of winning should be at least 95%, as a reference value AB Tasty uses the Bayesian statistics.
This uses a probability index, which is directly connected to user behavior and thinking. On the other hand, Bayesian statistics provide confidence intervals for substantiated business decisions, so you can better determine the relevance of the tested versions.
A good reporting shows you when your A/B test has reached statistical significance.
Our recommendation for optimal evaluation of your results: Let at least two business cycles run, which are usually carried out for 2 weeks each. You should expect at least 5000 visitors per variation and at least 300-500 conversions per variation on your primary target.
The results of your A/B test can now give you clues whether your hypotheses have been confirmed and how much better the other variant has performed. Furthermore, you can read off the performance in certain target group segments from further insights. Pay attention here also to prevent possible pitfalls of your result evaluation. For instance, these could be neglecting your micro-conversion (which is directly connected with the test), or using unclear hypotheses and primary goals. Your next steps of the A/B test could finally be implementing it in the hard code, starting further follow-up tests, or derive personalizations.
Reporting with AB Tasty: With insights into data like probability of winning, confidence interval and much more, you can make quick and safe decisions.
6. Conclusion on A/B testing
Advantages:
- No technical knowledge is necessary to carry out the A/B test. With the right testing tools, the effort is also correspondingly low.
- You focus on your target group and can take their perspective. This directly helps determine which elements work well and which don't.
- The collected data provides you with clear results, how you can improve the experience of your website for your target group and have a direct influence on your ROI.
A/B testing not only helps you improve user experiences, but also directly influences the ROI.
Disadvantages:
- In A/B tests, it is very important that you carry out an individual test for each change, otherwise your evaluation will not be clear.
- If you make too many changes on the website in too short intervals, it can happen that you cause confusion and deterrence among your existing customers. Therefore, plan in your roadmap which users run into which tests and possibly exclude certain segments or allow some time between tests on a certain page.
7. Useful tools for A/B tests on OMR Reviews
On OMR Reviews you can find an overview of A/B testing tools and conversion optimization software and can decide on the software best suited for you with the help of verified user experiences and reviews. We have already put together a small selection of the most popular tools for you:
