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Most companies make decisions based on assumptions and gut feeling. But with A/B test optimisation, you gain tangible data that comes from your users themselves. This method enables you to systematically improve your website and thus be more successful in the long term.[1] A/B test optimisation is no longer a trend. It is a necessary strategy for anyone who wants to improve their online performance.
Why A/B test optimisation is essential for your company
Every day without data-supported optimisation costs you sales. Companies that use A/B test optimisation achieve measurably better results than their competitors. The reasons are varied and convincing.
Firstly, you avoid making expensive mistakes. Instead of relying on what one person thinks, you use real behaviour patterns from hundreds or thousands of users.[2] Secondly, you reduce the risk of campaigns. Every change is tested beforehand. This means you know exactly what works and what doesn't.[3] Thirdly, you save time and resources. Instead of carrying out many tests in succession, you can test several hypotheses in parallel.
Teams that work with analyses perform 32 per cent better per test than teams without analyses.[3] Adding heat maps increases success by a further 16 per cent.[3] These figures show: A/B test optimisation is an investment that pays off.
Understanding the basics of A/B test optimisation
A/B test optimisation works according to a simple principle: You divide your users into two groups.[2] Group A sees the original version. Group B sees a modified version. You then measure which version achieves better results.
The goal is clear: to find out which variant performs better[4]. But there is more to it than just comparing. It's about systematic learning from your customers.
How A/B test optimisation influences your conversion rate
The conversion rate is at the heart of every A/B test optimisation. It measures how many visitors perform a desired action. This can be a purchase. It can also be a newsletter subscription. Or the completion of a form.
With A/B test optimisation, you can specifically test which elements increase the conversion rate. Changing the button text could result in more clicks. A different colour of the call-to-action button could generate more purchases.[2] Small changes often lead to big results.
BEST PRACTICE with a customer (name hidden due to NDA contract): An e-commerce company tested the placement of its shopping basket button. Instead of top right, it was positioned top left. The new variant increased conversions by 8 per cent within two weeks. This small change led to several thousand euros in additional sales per month.
The right hypothesis: the beginning of successful A/B test optimisation
Before you test anything, you need to formulate a hypothesis[1], which is your specific assumption about what you want to change and why.
A good hypothesis follows a simple pattern: If I make change XY, then the following metric will change because the benefit for the user is YZ.[5] This pattern ensures that you cover all relevant building blocks. You address the problem. You define the solution. You describe the benefit for the customer.
Collect test ideas and prioritise them with A/B test optimisation
To find good test ideas, you first need to analyse your website. Where do users leave your site? Where do they click most often? Which forms do they not fill out?[1]
There are qualitative and quantitative methods for collecting test ideas.[1] Qualitative methods include usability tests or surveys. Quantitative methods are web analytics data or heat maps. Save all test ideas in a central document[1]. A Google Sheet or a Kanban board work perfectly for this.
Not all ideas are of equal value. That's why you need to evaluate them using a simple formula: priority equals impact divided by effort.[1] The impact describes how much a test variant could improve the conversion rate. The effort describes how long it takes to test this variant.
BEST PRACTICE with a customer (name hidden due to NDA contract): A SaaS company collected 47 different test ideas for its login page. Using the prioritisation formula, it reduced the list to the top 10 ideas. This resulted in the best options being tested first. The result was a 23 per cent increase in sign-ups within two months.
Practical implementation of A/B test optimisation
The different types of A/B test optimisation
There are several ways in which you can implement A/B test optimisation. The most popular is the classic split test[4], where you only test one element at a time against the original. This could be a button colour. It could be a different wording. It could be a new headline.
The advantage is clear: you know exactly which element is responsible for better results. You can directly attribute the change to success. This is crucial for meaningful results.
Then there are multivariate tests[4], where you test several changed variables at the same time. This could be the combination of button colour and text variant. These tests require more traffic and more time. In return, they provide deeper insights into combination effects.
A third method is sequential testing. This is particularly helpful if you have a limited budget. You can carry out tests one after the other and save resources in the process.
The four steps to successful A/B test optimisation
The process of A/B test optimisation is systematic and comprehensible. Step one is to identify problems on your website[5]. Where do users fail? Which pages have a high bounce rate? Which elements are ignored?
Step two is to define an appropriate hypothesis[5], which we have already discussed above. Your hypothesis must be precise. It must be testable.
Step three is to consider what goals the A/B test optimisation should pursue:[5] Do you want more clicks? Do you want higher sales? Do you want lower bounce rates? Each test must be linked to a clear business objective.
Step four is the creation of the variant to be tested[5], which can be realised by a web designer or a web developer. The important thing is: Only one element should be changed. Everything else must remain identical.
BEST PRACTICE with a customer (name hidden due to NDA contract): One online shop carried out these four steps systematically. In the first step, they identified that users were leaving the product page without seeing the price. In the second step, they formulated the hypothesis that a more prominent price display would increase conversions. In the third step, they defined the target: 5 per cent more sales. In the fourth step, they created a variant with a larger price display. The result was a 7 per cent increase in sales.
What you should test: Practical examples
There are countless elements that you can test. The choice depends on your objectives. Here are practical examples from various industries:
You can test button colours in e-commerce. You can vary product descriptions. You can reduce the length of the checkout process. You can also test images. Which product photo leads to more purchases?
In the SaaS sector, many companies are testing their login pages. Do you really need three forms or is one enough? Which headline generates more registrations? What effect does the wording of the call-to-action button have?
You can test subject lines in email marketing. You can test different sending times. You can vary the design of emails. You can also optimise the length of texts.
In the content area, many blogs test their headlines. A different headline could generate more clicks. The length of content can also be tested. Do your users prefer short or long articles?
The most important rules for successful A/B test optimisation
There are basic rules that you must follow. Rule one: Only test one variable at a time[6]. This is essential for clear findings. If you change several elements at the same time, you cannot know which element is responsible for better results[2].
Rule two: The test group must be large enough[4] If the traffic is too low, it will take longer to obtain relevant results. This is particularly important in multivariate testing.
Rule three: Randomise user assignment[8] Users are randomly assigned to either version A or version B. This eliminates bias. This eliminates bias.
Rule four: Observe statistical significance[8] A/B tests use statistical analyses to determine whether the differences between the variants are significant. Or are they just due to chance?
Rule five: Set a macro target for each project[2]. This marks the end of the tests. Without a goal, A/B test optimisation runs the risk of becoming endless.
How artificial intelligence is revolutionising A/B test optimisation
Artificial intelligence is changing the rules of the game in A/B test optimisation. Modern tools store historical data, live data and best practices[2] and make recommendations on this basis.
Algorithms recognise recurring patterns. They derive recommendations from this. They can even implement measures independently. This is particularly valuable for repetitive tests.
The advantage of AI-based tools is their ability to learn[2]. The programme improves during a running test. It constantly optimises the informative value of the results. This saves time and increases the quality of the results.
Mastering typical challenges in A/B test optimisation
Many companies fail in A/B test optimisation not because of the methodology. They fail because of typical challenges.
Challenge one: Too little traffic. Some websites do not have enough visitors. Then it takes a very long time to reach statistical significance. Solution: Focus on sites with a lot of traffic. Or carry out longer tests.
Challenge two: False hypotheses. Sometimes hypotheses are formulated that cannot be tested. Solution: Use the if-then-else pattern[5], which forces you to think precisely.
Challenge three: Too many parallel tests. Sometimes companies try to test everything at the same time. This leads to confusion and ambiguous results. Solution: Prioritise your tests with the priority formula.
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