How you can increase your conversion rate in e-commerce with A/B testing

What is A/B testing?

In A/B testing in e-commerce , you compare two versions of a landing page on your website or an email to find out which version is more popular with your target audience. You show the original version to some visitors and a slightly modified version to others. You then compare which version has the better KPIs. This allows you to continuously optimize landing pages or emails and increase their conversion rate.

Graphical representation of an A/B test including results, in which group B performs significantly better.

Why are A/B tests suitable for the e-commerce industry?

A/B testing is ideal for online shops and should definitely have a permanent place in the marketing team's toolbox. This is because it allows you to significantly increase the return on many online marketing campaigns in e-commerce with relatively little effort. Which wording convinces new customers on the landing page? How should the page be structured? What small changes in design can make the newsletter even more appealing to the target group? These are all questions that A/B testing can provide well-founded answers to.

What advantages do A/B tests offer you?

A/B testing can significantly increase the efficiency of your marketing campaigns. The greatest strength of a well-planned A/B test is its objectivity. You can use data to make arguments and optimize elements. This often allows you to identify and discard false assumptions.

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If you know how to address your target group in such a way that they respond positively, you have a competitive advantage in e-commerce. Your advertising campaigns will pay off faster and you can strengthen customer loyalty. The technical implementation is also easy and inexpensive thanks to various tools such as Optimizely, AB Tasty, or VWO.

What can you check with A/B testing?

A/B testing can, in principle, be carried out on all elements of a website, email, or advertisement. Elements where even small changes can have a significant impact include:

• pictures
• Calls to action
• buttons
• headings
• forms
• user guidance
• product presentations
• Graphical presentation of prices
• product recommendations

In A/B testing, the following variants are often distinguished:

• Classic A/B test (split test): Two versions of the same page on the same URL.
• Multivariate test: Test of more than two changes on the same page.

Which statistical approach is suitable for conducting an A/B test?

In principle, all two-sample hypothesis tests can be considered. Which one is most suitable depends on the specific application. Most tools integrate statistical tests on different bases and evaluate them automatically in a way that even laypeople can understand.

One of the most commonly used tests is the chi-square test. This allows you to check whether the change leads to statistically significant better results. Scientists usually use a significance level of 5%. This prevents you from considering hypotheses to be true even though the change probably has no real impact on your success.

Another option is the Bayesian test, which is deductive and therefore extrapolates the result using probabilities. One advantage is that it allows poor variants to be quickly eliminated. However, this makes false conclusions more likely.

How do you conduct an A/B test?

Follow these five steps to successfully complete your first A/B test:

You should avoid these 4 common mistakes when conducting A/B tests

Be sure to avoid the following mistakes when conducting A/B testing in e-commerce.

What are the challenges of A/B testing?

There are two major challenges when it comes to A/B testing: First, formulating a meaningful hypothesis. It must be verifiable, and the intended change should also have a good chance of being better than the original version. While changing elements in unexpected ways can sometimes yield surprising insights, a targeted approach leads to more efficient optimization.

On the other hand, evaluating tests and interpreting data such as sales figures can sometimes be complicated and can lead to false conclusions. This is also true because individual elements may achieve an improvement in one context, but not necessarily in another. After all, the whole is more than the sum of its parts. A particular headline, in combination with a particular image and a CTA, may appeal particularly well to the target audience. A CTA that has been proven to convert better in a test conducted some time ago will therefore not automatically work better in this context.

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This is exactly what can be tested with multivariate tests: you vary the headline and image respectively and test which combination works best.

Conclusion: A/B testing is the key to efficient marketing in e-commerce.

A/B testing is a relatively easy-to-understand method that, thanks to a wide range of tools, is also easy to implement. It allows you to tailor your landing pages or emails to the preferences of your target group. This makes it an indispensable tool for optimizing your conversion rate in online marketing.

Would you like to learn more about how you can increase the conversion rate in your online shop?

Then schedule a web demo on the topic now!