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A/B testing in E-commerce is one of the most popular ways to test a theory and predict results. That’s why one of the most common questions we get at Depict is whether we can run an A/B test to get a better understanding of the correct choice between two (or more) options.
In theory, nearly everything can be A/B tested. However, this doesn’t mean that it’s always the best idea.
Below, we run through the basics of A/B testing, including some common E-commerce A/B testing examples, and the pros and cons of using this testing method.
First, let’s align on what A/B testing actually is.
As a fashion & lifestyle E-commerce retailer, it's important to continually optimize your website and marketing campaigns to drive conversions and sales. One effective way to do this is through A/B testing, also known as split testing.
In an A/B test, you create two versions of a webpage or marketing campaign and show them to two separate groups of visitors. You can then compare the performance of each version to determine which one is more effective.
When asked to explain A/B testing to a 5-year-old ChatGPT gives this answer:
“A/B testing is a way for us to try out different versions of things to see which one works better. For example, if we have a website where we sell clothes, we might create two different versions of the website. One version might have blue buttons and the other version might have red buttons. We would show the blue button version to some people and the red button version to other people. Then we would see which version of the website helped more people buy clothes. The version that helped more people buy clothes is the better one, and we can use it to help more people buy clothes in the future.”
There are several pros to using A/B testing for fashion E-commerce retailers.
Of course, for different types of sites and specialized niches, there will be other benefits. But, generally, these are some of the benefits you can expect:
By testing different versions of your website or marketing campaigns, you can identify elements that are more likely to drive conversions. Without A/B testing, E-commerce stores would be stuck with keeping the same layout season after season, or blindly making decisions without having the experience to base them on.
In E-commerce some common A/B testing examples involving conversion improvement can include changes to the layout, color scheme, product images, or messaging on a site.
A/B testing also allows you to isolate specific elements on your site or in your marketing campaigns and see how they perform.
This can help you determine which of your elements, such as call-to-actions or product descriptions are most effective in driving conversions and sales. You can then use this data to properly tailor pages and fuel future campaigns.
A/B testing is a data-driven approach to optimization, allowing you to make confident decisions based on real-time data rather than assumptions or guesses.
Having the ability to make decisions based on concrete data ultimately saves time and stress. Knowing that changing your home page to look a certain way, recommending certain products to certain audiences, or upselling in certain areas of your site, means your team can quickly make decisions and then spend time on the things they like to do. And, the things that boost your profits.
Most of the time A/B testing tools are widely available and easy to use, making it simple to set up and run tests on your site or campaigns.
This is beneficial as you can quickly collect the data you need, then implement changes and begin feeling the impact of your changes as soon as possible.
One of the best benefits of A/B testing in E-commerce is its clarity and ease of interpreting the results. A/B testing works perfectly when there is only one, or very few components changed between versions of the test. Therefore, you can be clear about what it is that is impacting your results.
The benefits of A/B testing may now have you hyped and ready to start testing. However, there are also some potential cons to consider when using A/B testing for fashion E-commerce retailers.
While they greatly cut down the time spent actually making a decision, this doesn’t mean A/B testing itself doesn't take time.
Setting up and running A/B tests can be time-consuming, as you often need to create multiple versions of your website or marketing campaigns to prepare for testing. Then, you’ll actually need to set a time period for gathering data on their performance and analyzing this data.
That means that when considering an A/B test, its worth taking this into account.
A/B testing only allows you to test a few variables at a time. So, it may not provide a comprehensive view of how different elements on your site or in your campaigns impact performance.
In essence - the more complex your test is and the more components there are in the variants - the harder it is to A/B test. So, A/B testing in E-commerce often works best when
It's important to ensure that you have a large enough sample size and a statistically significant result before making decisions based on A/B test results.
If your sample size is too small or the result is not statistically significant, you may end up making changes based on false positives. Again, this means an A/B test works best when well thought out and planned.
Even though it’s rather easy to set up a test - small errors in tracking and measurements can have big impacts on the A/B test outcome.
A/B tests are best run by those who are experienced in testing, to avoid any wasted time or effort.
A/B tests do not always take into account the long- term effects of certain changes and the overall customer experience. This last point is arguably the most important to acknowledge. While A/B testing can be an effective way to optimize your website for conversions and sales, it's important to keep in mind that it's only one aspect of customer experience.
By focusing solely on short-term revenue-generating wins, you may be ignoring other important aspects of customer experience such as usability, ease of navigation, and overall brand aesthetic.
For example, an A/B test may show that a more cluttered layout with more calls to action drives more conversions in the short term. However, this may not necessarily provide the best overall customer experience, as it may be overwhelming or confusing for visitors.
Additionally, A/B testing is only effective for testing a few variables at a time. This means that you may not be able to fully understand the impact of a larger design or layout change, and how this impacts the customer experience.
In short, A/B testing is certainly a worthwhile tool to use, when fully thought out and planned.
Rather than relying solely on A/B tests to make your decisions, and performing them whenever you get the urge, it's important to consider the long-term impact of your optimization efforts on customer experience.
This means that rather than solely focusing on short-term revenue gains, you may require a more holistic approach to optimization, such as user testing or focus groups, to fully understand the needs and preferences of your customers.
A great example of this comes from Meta, which experimented and tested how the volume of notifications would impact the retention of its users. The short-term effects of sending fewer notifications showed that retention went down, but in the long-term, it had a positive impact due to an increased customer experience.
Overall, while A/B testing can be a valuable tool for optimization, it's important to consider the broader impact on customer experience and take a more holistic approach to optimization.
If you’re looking for a foolproof way to improve your online fashion store experience, with Visual-AI-led product recommendations and merchandising tools, why not check out our product offering at Depict?
Of course, we’re willing to help you A/B test the results too, so get in touch with us to find out more.
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