It is known as A / B Testing or A / B testing, in digital marketing, the creation of two (or more) versions of web content to determine which of them best meets the objectives that have been set.
How do you do it? Content “A” is randomly shown to half of the users, and content “B” to the other half to measure and analyze the results of each of them based on certain variables, such as CTR, conversion, engagement, and/or rebound.
Some contents on which A / B tests are usually carried out are:
- Lead Magnets
- Email marketing
- Landing Pages
- Product presentation formats in an e-commerce
What are A / B tests for?
A/B testing has the same function in digital marketing as any experimental methodology to improve results from other industries; it just tends to be infinitely easier and cheaper to compare the performance of two versions of a website than it is to compare that of two similar drugs, for example.
The three main functions of A / B testing are:
- Throw invaluable information about what the user prefers. The first goal of A / B testing is to find out what users prefer. That is, what type of elements or messages make them more likely to click on an ad, become leads, make a purchase or stay on a website. This information not only serves to improve the content on which we did the test but also serves as a guide to optimizing all subsequent content.
- Increase the conversion rate. A / B testing is one of the main tools of CRO or Conversion Rate Optimization, which refers to a series of systematic improvements so that content generates more leads, subscriptions, conversions, clicks, etc.
- Improve the user experience. Finally, A / B tests allow us to perfect the content that we offer to our visitors so that their user experience is more and more personalized and according to their expectations and needs.
How to do A / B testing?
The software or applications for A / B testing allow us to create two similar contents, which share the same URL and the same objective (but which are different from each other) and direct the public to one or the other version in a random way.
A / B tests also have different variants, depending on what needs to be measured:
- To compare the performance of two completely different sites or landing pages, it will be more convenient to have two different URLs and measure their performance independently.
- One can also compare the performance of two slightly different content, it is more convenient to A / B test with the same URL.
- To identify the impact of each of the modifications you make to content, it is advisable to do a multivariate or MTV test, which independently displays and measures all the changes you make in a B version, instead of showing them all together. Their configuration is slightly more complex, but they yield much more specific information.
Most of the platforms for creating websites and sales funnels, such as WordPress and ClickFunnels, already integrate among their plugins and functionalities the possibility of A / B testing without dealing with programming codes web design, through very intuitive interfaces.
What are the hypotheses?
It is relatively easy to run A / B tests on a website, what is not so simple is to determine why making certain changes or adding certain elements is going to optimize its performance.
The A / B hypotheses are the possible explanations of why one change or another can help us achieve our goal. In other words, they are the reasons behind, for example, a colour change in a CTA, to increase conversion.
The hypotheses of A / B testing are very important in CRO because otherwise, we would simply do a lot of random tests without ever discovering a systematic pattern that allows us to optimize all our content.
Do you need help with setting up an effective A / B Test?
To carry out effective A / B testing requires the intervention of a specialist with a lot of experience in digital marketing. You can find freelance professionals with this particular profile on all the major website that offer freelance services including Digital Marketing. Alternatively, you can also try using the AB Testing tool from ABTesting.AI