If you are running a marketing campaign and want to find out which of your ads, copywriting or website design works the best – The principle of A/B test in digital marketing is exactly what you need to follow. It is one of the most important things when you’re trying to figure out what your users like or dislike.
Because it will help you make sure that everything on the page works well and fits seamlessly into your website, giving you more information about how people react to different parts of your site.
What exactly is A/B testing?
A/B testing, also known as split testing or multivariate tests, is a method to compare two variants of something to help you determine which performs better. Basically, it’s when different versions of one thing (e.g., advertisements) are shown to users on various devices with minor element changes so that it can be analyzed as per their effectiveness in terms of conversions.
Each variant will differ just enough to give you an indication of whether this variation has any effect on conversion rates and customer satisfaction.
Why is the principle of A/B testing important?
A/B testing is a smart way of determining which audience response works better for you and it doesn’t require too many technical skills. The goal here is to determine which version performs better for the specific visitor that you want to convert into a customer – because if one does, then it will outperform the other. This can be done by employing multiple versions of an ad, varying copy or content on each one, and then measuring the results.
Following are the reasons why it is important:
- Help optimise better conversion rates
- Ensure the right user design and user experience are used to retain longer time on page
- Low-risk modification by minimizing the changes of elements
- Minimize bounce rate on the webpage
- Drive more traffic to the website
Where is the principle of A/B testing applied?
In digital marketing, A/B testing can be applied on many various platforms. For instance, webpage, landing pages, email, ads, and mobile apps. These platforms have their own respective elements to be tested in order to optimize and help deliver the desired result. To be more precise, these are the following example of A/B testing variables:
- Headlines
- Call-to-Action Text
- Page Design
- Images
- Button Colours
- Button Placement
- Visual Copy
- Page Content
- Email Templates
- Email Subjects
- And more
How does the principle of A/B testing work?
A/B testing is a unique marketing strategy that was first developed to test different aspects of landing pages on the internet. By changing one variable at a time, you can see which performs better and adjust your content accordingly.
It usually works by comparing two versions of experiments. Let’s take an example of a Call-to-Action button as our test experiment.
In the above example, we have 2 different CTA buttons, which are Shop Now and Buy Now. We would like to test which CTA buttons could drive the most clicks and conversion rate. Then, we will split two pages between the original page and the variation page to drive equal traffic of 50%-50% to both pages.
After testing for one month, we see “Shop Now” CTA has better clicks and conversions rate (60%) compared to “Buy Now” which we can conclude most of the website visitors are attracted by the “Shop Now” text.
In ads campaigns such as Facebook ads, A/B testing is also recommended conduct to determine which ad creatives perform better. For instance, we conducted split testing on the 2 single images with different creative designs to see which performs better.
Ad A: Non-highlight product features Ad B: Product features highlight
Based on the performance ads result, we noticed Ad A has better clicks and engagement rate compared to Ad B. Hence, we can conclude that the audiences behaviour preferred more on the visual that highlights the product specifications. From here, our strategy was to keep designing other visual ads similarly to Ad A visual concept as we knew the direction had better results.
When to use A/B testing?
When you want to understand more about your audience or visitors’ behaviour that could make them take an action such as purchasing your product or signing up as a lead, then you should do an A/B test. Depending on the goals that you want to achieve, different variations will be tested based on the principle of A/B test in digital marketing.
For example, you own an eCommerce website. The journey of your visitor would be like this:
Homepage > Product page > View product content > Add to Cart > Checkout
At some point, you might notice their journey has stopped at one particular point. Let’s use the Checkout page point as a point of where your customers stopped making purchases even if you received many Adds to Cart. You will need to analyze the page to understand your customer behavior and detect the potential reason in order to solve it.
Other considerations when to do A/B test is when:
- You are doing website revamp
- Any changes on the landing page
- New added or subtracted page on your website
- New identified target segments in the market that you want to target
- Sudden conversions rate dropped
Always remember that A/B test experiment must be conducted with only 1 variable change and keep it as simple as possible. If you are experimenting with more than 1 variable, you might not be able to determine which variable that helps you get high conversion as you have tested 2 or more variables.
Summary
Now that you have understood the fundamentals about the A/B test. The reason why doing these tests is important is so that companies can find ways to make their products or services easier for users to use as well as improve their performance by making sure they have all the necessary features present.
If you are still unsure which elements will work if changes, the best is to apply the principle of A/B test in digital marketing. With this, you are to figure out what works and what doesn’t work.
Key Takeaways
- A/B test can be executed in various platforms and variables
- Always test 1 variable change, don’t test more than 1
- You must identify the key metric that you want to focus when analyzing the result of the test
- The duration of A/B test must be at least 7 days experiment
- Use the data resulted from the test to help optimize your platform
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