What is A/B Testing?
A/B testing is like giving chocolate A to one child and chocolate B to another child and seeing which one is liked better. Technically also known as split testing and bucket testing, it is an experiment of testing which one of the two versions performs better. They could be two creatives for an ad, two product descriptions, two headlines, two call-to-action buttons, and so on.
How does A/B Testing Work?
Your team designs two headlines for an email marketing campaign, for example, and split tests them by randomly showing one headline to half of your audience and the second headline to the rest of your audience. If you can’t decide on two headlines, you can also run the A/B test, where you can test more than 2 variations.
Then, following your objective, which could be the open rate in this case, for example, you can track by your key performance indicator which version performed better.
A/B testing should be continuously practiced since it helps improve the overall user experience and marketing efforts. Websites and apps need to be tested for optimization to get higher traffic and conversion rate increases.
Why do we need to do A/B Test?
A/B testing is one tool that is easy to implement with a higher return for the organization by improving the user experience for the customers. While the correlation is proven, A/B testing helps explain the causation, the cause of the effect change. Here are some ways A/B testing is beneficial:
Increase User Engagement
During the split test, if a green-colored call-to-action button works better than a white call-to-action button with green borders, it improves user engagement. Similarly, creatives on social media result in likes, shares, and comments and increase the post’s engagement. As a result, your SEO or social media strategy starts using the better-performed version.
Decrease Bounce Rates
Improved pages can result in lower exit rates and bounce rates from where people exit the website or the application. Higher engagement leads to lower bounce rates, and people stay longer on your website, increasing the probability of conversions
Higher engagement rates and lesser bounce rates result in increased click-throughs at the call-to-action buttons. Improved user experience also plays a vital role and leads to, eventually, a conversion.
Designing Effecting Content
A/B testing helps you understand the kind of content your audience prefers, which helps you plan your products’ copywriting, SEO strategy, and social media strategy.
Launching a campaign or a product, only to later realize that it could significantly be better and result in higher conversions, wastes time and resources. A/B Testing reduces the risk of low conversions and launches a significantly better version of your product or campaign.
What are the different kinds of A/B Tests?
Depending on the kind of website and your objective, there can be three different kinds of A/B tests:
- A Classic A/B test: This is the kind of test where users are directed to the same URL with two variations of the same element.
- Redirect Tests: This test directs your traffic to different URLs if you are hosting new pages on your server
- Multivariate Test: A multivariate test measures the impact of multiple variables and changes on the same page, such as the text, color, banner all change, etc.
What is the Process of A/B Testing?
The first step of A/B testing is to decide which variable to test. You can change the creative for a social media post and keep the caption the same, or the color of the call-to-action button different but the font and text similar, and so on.
Data from other sources must complement A/B Tests to understand the conversion problems. As a result, to create an understanding of your user behavior, you run A/B tests. The analysis phase is critical because it helps you create strong hypotheses.
The key performance indicator that you will choose will be chosen before the test and will be your dependent variable. It changes how you manipulate the independent variable. If you identify goals at the end of the process, the test might not be as effective.
Hypotheses must be linked clearly to a problem that has identifiable causes. They should mention a possible solution to the problem and indicate an expected result, which a key performance indicator can measure.
For example, if the checkout process has a high abandonment rate, then a hypothesis can be “Deleting the optional fields will increase the checkouts”.
A/B testing can help you statistically validate your hypothesis; however, it cannot explain user behavior alone. Therefore, gather information provided by other research methods such as usability testing, heatmap recordings, and interviews to find out the context and the reasoning behind the behavior.
Create the desired variations to your website, like changing the call-to-action text or its color, swapping the sections, etc, on testing software.
After the variations are created, the traffic must be evenly divided into both variations. The audience should be randomly selected and tested simultaneously within the same context. A change in the time, such as time or the day or day of the week, can significantly impact the results since the time and the day are also variables.
Another reason why A/B Testing is beneficial is that it is quite straightforward in results and does not need detailed analysis to understand. A good test analysis report includes the conversion rate by each variable, the percentage of improvement and segments the data by traffic sources, geographical location of the traffic, and so on.
Why is A/B testing important in SEO?
Google encourages A/B testing, and performing one doesn’t impact search engine ranking negatively unless it is used for cloaking. If you use multiple URLs for a split test, don’t forget to use the rel=” canonical” to prevent the Google bot from getting confused.
Justifying Further Investment in Promising Area
A/B tests determine which variables work better and should be invested in more, whether it is attractive creatives, good product descriptions, catchy headlines, etc.
Avoiding Disastrous Decisions
If a page has a high conversion rate, then there is no point in A/B testing it and trying to improve it. You could use that time and effort on a page that improves the conversion rate from 20% to 47%.
A/B tests sometimes also fail where the variation is not significantly noticeable or different. Changing the font size from 12 to 14 is highly unlikely to be noticed by the users. Instead, a font change could be a good variable.
A/B Testing Check List before Starting
Before starting the test:
- Pick one variable to test: the independent variable
- Identify the goal: the dependent variable
- Create a control variable and a challenger variable: two versions of the same page, content, etc
- Determine the sample size of both the sample groups
- Choose a testing tool and reporting software that statistically analyses the results.
Frequently Asked Questions
How significant do your results need to be?
The higher the confidence level, the surer you can be of your results. Aim for a confidence interval of 95% – 98%. The more radical the change is, the less scientific the process needs to be. The more specific the change is, the more scientific the change should be.
Which elements to test in an A/B Test?
Start with the easiest elements to change and can have big impacts. It would be difficult to change your entire website rather than just changing the positioning of the sections or the colors of your call-to-action buttons. Another way to start is to find your funnel dropouts and then make improvements in those pages or product features.
How much time does A/B testing take?
It is advised to run A/B tests to run for around 2 weeks to capture enough accurate information to design the strategy.
Can I Test More Than One Thing at a Time?
Testing for two or more variables is a very common mistake that digital marketers make. Just like in a science experiment, one variable changes, and everything else is kept constant; similarly, in an A/B test, all other variables are kept constant to measure the impact of the changing variable effectively. It helps pinpoint the behavioral impact caused by which particular variation.
How to Analyze A/B Testing Data?
Identify the key performance indicators (KPIs) before the test and the metrics that will help you evaluate the results of the A/B Test. Determine how the test impacted those metrics and segment your audience into different variables such as:
- visitor type: new or repeated visitor
- device type: mobile vs desktop
- traffic source: direct, social media, or referral
After analyzing, implement the winning variation further into your strategy.
Remember that the best time to A/B test is always! There is always something you can improve, you can innovate, and something you can introduce. Try testing your emails, webpages, Facebook and Instagram ads, social media posts, blog titles, call-to-action buttons, and so on.