What is A/B testing and how can I use it in my funnels? 🙌

How to optimize the home page in your funnels using A/B testing

What is A/B testing and how can I use it in my funnels? 🙌

What is A/B Testing and what is there to consider?

While creating landing pages, websites or funnels, you have surely heard about A/B testing. A/B testing is a well-known analysis method in marketing where 2 variants of an element are tested against each other over a selected period of time to find out which variant of the element leads to better results.

If you want to know whether your target group is more willing to take a certain action when your funnel has a red or a white background, then you pit the red variant against the white variant. Over a selected period of time, 50% of the traffic is sent to the red variant and the other 50% to the white variant. Since your funnel differs only in the test point "background color", you can determine in the later analysis of your A/B test which background color brings better results.

What are the advantages of A/B testing?

A/B testing can be used in a variety of ways and there are no limits to your creativity when it comes to what you can test. No matter if ad, ad copy, ad creative, landing page, funnel, funnel pages or even form modules, you can (and should) test everything.

A/B tests are particularly exciting for you as a marketer because they do not rely on your subjective views - leaving your gut feeling out of it - and you have statistically relevant data that validates your test results. This way, your results are unambiguous and you can perform optimization after optimization without just going by your gut feeling.

How does A/B Testing work with Perspective?

You open your Perspective Funnel in your account and create a second start page for your funnel in the editor. There you make one specific change. That can be a head live, an image, the background color or anything else. After publishing, traffic is automatically distributed to one of the two start pages (50-50). Over time, you can see in the analysis area which variant leads to a higher conversion. This way you can always try out new theories to continuously optimize the performance of your funnel.

💙 How do I know what I should test?

For example, the conversion rate of your funnel is 2%. You want to increase this percentage significantly, but you are not sure what changes are needed to generate the increase. You start an A/B test and change the value proposition on the variant. So now you have 2 different value propositions that you test against each other. After 200 visitors on original and variant over the period of 2 weeks you see that the second value proposition has convinced many more visitors and the conversion rate on this page has grown from 2 to 6%. You end the test by choosing the variant as the winner and from now on you continue to work with the version that led to the increase in conversion rate.

How do I evaluate my A/B test in Perspective?

Once you have created a variant and published the funnel, you can see in the analysis section of your funnel not only the page views and conversion rate of the funnel as a whole, but also a separate evaluation for the original and a separate evaluation for the variant.

You will quickly see what difference each version has on page views and conversion rate. Once you are satisfied with the results and a clear winner emerges, you can end the test and keep the winning version.

Pro tip before you get started:

It is important that you do not change the variant too much from the original so that you get a meaningful test result. It is sufficient if you change the button color, for example, and test which headline version leads to better results. If you change the variant too much, possibly replacing several blocks, changing the content, the design, etc., you will not be able to understand what change caused the results when you evaluate the test. It is also a question of the significance of your test.

What is significance of A/B tests?

In the context of A/B testing, statistical significance indicates how likely it is that the difference between the original version and the variant of your experiment is not due to error or chance. For example, if you run a test with a significance level of 95%, you can be 95% sure that the differences are real.

This is especially important because it is easy to jump to conclusions when testing, a good example of such a misinterpretation is an A/A test where no changes were made between the original and variant, yet the conversion rate is significantly different between the two versions. Not least because besides the changes on the pages many other factors play a role, e.g. the persona types of visitors, the source of the visitors, the duration of the test, the total number of visitors and much more. You can find a worth reading article on this topic here by Tomi Mester.

Start A/B testing today!

With A/B testing, you can find out what content and what elements drive your target persona to action. Have fun!

Share this post

Bring your best ideas to life

Subscribe to our newsletter and receive the best templates, worksheets & checklists conveniently in your inbox every time.

Start advertising more effectively in the mobile age.

Cancel anytime
Access to all content
Expert support