What is A/B testing?
A/B testing compares two versions of the same digital asset, such as a landing page, online advertisement, against each other to determine which variation performs better for your conversion goal. This method is also known as bucket testing, split testing and statistical hypothesis testing.
As you focus on growing your business through marketing strategies, you will want to take out the guesswork and make data-informed decisions about your campaigns. By conducting A/B testing, you can easily know if users like version A or B better and make optimized changes to improve the effectiveness of your digital marketing assets.
How do you perform an A/B test?
To perform an A/B test, you should split the audience into control and experimental groups. The control group sees the original version and the experimental group sees a modified version of the digital asset. Ideally, both versions are identical, with a slight change that can affect a user's behavior and impact your KPIs.
Some of the most common elements to change with A/B testing include:
Headlines and subject lines
Call to actions
Types of Images and quantity of images
Once you've collected enough data, use analytics and statistics to measure the difference in behavior between version A and B. Was there a fluctuation in the click-through rate, number of clicks, transactions or purchases? If so, were these changes positive or negative? Based on the results, you’ll be able to implement the winning variations moving forward.
You may also be interested in:
Customer lifetime value
Unique selling proposition
The importance of A/B testing
By making positive changes to the user experience you can get more leads and sales. Therefore, A/B testing is an effective lead generation strategy. Assuming you've already worked hard to drive traffic to your website, with A/B testing, you can increase your site's conversion rate and improve ROI.
All these changes allow you to make big business improvements with low risk. After all, the more conversions your current digital assets receive, the less time and money you need to spend on developing new marketing campaigns.
How to perform an A/B test
The process of A/B testing is not as complicated as it may seem. There are 7 key steps you need to take to perform a successful experiment.
Identify a problem: Collect data to understand which of your assets can see better performance and conversion rates. This can be improving your email campaign's CTR or an advertisement’s form fill rate.
Set improvement goals: Choose which metrics you want to improve, whether it's the number of purchases, sign-ups or clicks. This will allow you to better assess the results of the A/B test and determine the winning version.
Develop a hypothesis: Like in middle school science class, predict a hypothesis that “if you change X then Y will happen.” For example, if the CTA button is at the top of your landing page, you can assume that conversions will grow by a percentage.
Create variations: Make the changes to the control version A which turns it into your experimental version B. If it’s an email campaign with different images, then create new visual content for it.
Run the experiment: Test your hypothesis by splitting your traffic between the two versions. Run the experiment for a long enough period to yield enough data to draw reliable conclusions.
Analyze your results: Through in-depth reports and analysis, dissect the data you collected from the time of the experiment. Use the KPI benchmarks from step 2 to compare the two variations.
Make the changes: Based on your results, update your website, emails or ads to serve the most optimized best-performing versions.