What is A/B testing?
Let’s understand what is A/B testing and its future in the software market with some A/B testing examples.
A/B tests are randomly applied to a given audience: “layout A” and “layout B” are displayed until you have a statistical number relevant to the survey. At the end of the test, it is possible to identify which layout performed best.
A/B testing and it’s future
As there is a lack of good advice, practice and answer to the question- what is A/B testing, we decided to write some tips that could help in performing this test. Have a glance at these tips to learn how to perform the test.
Focus first on the big changes
If you’re thinking of testing a home or landing page, start with two radically different versions.
Home and landing page
Some people argue that by testing completely different pages you won’t identify which elements are driving conversions. This is true, but for the first phase, choose the main approach or structure. Minor adjustments will not bring big changes, if before you have not tested completely different formats of pages.
Improving Bounce Rate does not improve conversion
Bounce Rate is the percentage of visitors who have accessed your site’s homepage and left without clicking any internal links. For example, if 100 people visited your blog and 75 of them only read the main page, your Bounce Rate is 75%. So, the Bounce Rate is an important component of a site’s homepage.
We have seen countless times that improvements in Bounce Rate that did not result in an improvement in conversion or, at worst, conversions even dropped. The obvious explanation is that the user was taken to a path he didn’t want or we were wrong to think that this path would improve conversion.
For example, adding a huge button to a page with the words “Learn More” induces people to click on it. But so what? You could have provided users with this information without a click. That could improve the bounce rate, but you didn’t make it a conversion.
You can trick users into clicking by adding more content on subpages or tabs. But if the content isn’t as relevant to the user, you’re hurting your chance of conversion with these distractions.
Another useful point about the Bounce Rate
Even if improvements in the bounce rate result in conversion improvements, you can’t compare these improvements to each other. They’re not equal.
Let’s say you have an online business selling toys for cats. You make some design changes and your Bounce Rate drops from 20% to 10%. Legal, a 50% improvement in bounce rate. You mean you’ll get a 50% improvement in conversion?
No. The Bounce Rate shows the percentage of people leaving your site, not the percentage of those who stay. To be equal to equal, you need to think about your retention rate. What you got was an improvement in the retention rate from 80% to 90%, i.e. your improvement will be 12.5%.
They say a basic understanding of economics is worse than none. Learning about the implications of supply/demand without understanding the nuances and underlying assumptions can lead to confidence in misguided conclusions.
The same goes for statistics. That’s why it’s so important that A/B test practitioners understand that they are involved in a purely statistical exercise. So some basic rules:
The smaller the difference in the result between your tests, the greater the number of samples will be needed to conclude;
Unless the results are overwhelmingly extreme, each test requires at least 1000 (and probably much more) unique visitors to conclude worth adopting.
User needs time to adapt
If your site already has a good number of loyal users, then you need a trial time to be able to count on the results.
Because users have already learned to interact with your site the way it is, introducing new features can reduce performance and/or induce a “curiosity interaction” that will not be representative of long-term usage.
There is no rule of how long testing is enough to reach stabilization. You may need to keep the test for at least a month until you begin to understand and measure long-term impacts.
So, you should have knowledge about AB testing in marketing.
Guide to A/B testing
How and when to use AB testing?
- Start by testing completely different pages. Smaller tests will be more efficient once you’re sure you’re working with the right approach.
- Reducing the bounce rate is great, but it shouldn’t be seen as the salvation of a campaign. Retention Rate is more linked to conversions.
- Understand the nuances of statistics. There are margins of error that make a pre-job not worth it.
Future of A/B testing
From the ashes of the AB test, a new way of testing websites emerges optimization with AI. From Facebook ads to calculating your route on Google Maps, AI-based optimization is everywhere, positively impacting the user experience at all times, like never before.
A/B testing and it’s future
When you did an AB test, you were testing which version brought the most results in general. Today’s AB test took shape under the name of real-time personalization, after all each user reacts better in a certain way and it wouldn’t be smart to say that there is one better version than the other for a particular audience.
Real-time personalization enables you to understand your users individually so that each page, every layout, and each site element adapts to your preferences. What will increase your CTR is not a particular product or banner in the right corner of the screen. You need to understand which banner that specific user would like to see, according to their needs. And all this is only possible because of artificial intelligence.
We hope this article helped you. It delineated in details about the tools for AB testing. If you still have a query, leave it on the comments below. We will try to address it. Cheers!