By comparing two or more variants of a website’s layout, content, or functionality, A/B testing can help you learn which one is preferred by your target audience. It allows you to see how various changes to your page or its components effect your consumers’ actions.
Undoubtedly, you are familiar with some of the more elementary uses of A/B testing, such as experimenting with button colours and CTA copy. It’s unfortunate that many online stores don’t understand the fundamentals of A/B testing.
The Value of A and B Testing and Why You Should Do It
When done correctly, ecommerce AB testing (also called split testing) gives you the power to significantly improve your customers’ shopping experience, which in turn can lead to greater click-through rates, conversions, and customer loyalty.
Consider how Amazon has improved upon the online shopping experience overall:
- One-click ordering is now possible.
- The ability to swiftly and efficiently replenish supplies by pressing a button on Dash.
- Obtain your order in just two days with no additional shipping costs.
- Personalized suggestions that are perfect for you.
These advances, however, are not the result of random chance, but rather the implementation of judgements based on the trustworthiness of the results of extensive multivariate testing.
Changes to your e-commerce site’s messaging, search engine optimization tactics, or checkout process are unavoidable at some point in the future. If you want to increase your conversion rate from the traffic you already have, A/B testing is the best way to learn new information and get pointed in the right path.
In other words, how does one go about conducting an A/B test?
There are two primary types of A/B testing: client-side testing and server-side testing. The testing of services on the client side is more widespread.
Server-side testing is when your website’s server shows visitors different versions of a page and makes changes to those versions before sending them to the visitor’s browser. Nothing is being done to the page in the browser at the moment.
For instance, you may split your traffic in half, sending 50% to variant A and 50% to variant B.
The next step is to compare the two versions of the page’s performance to decide which one, A or B, is more effective in generating the desired outcome (conversion), which might be an increase in sales, email subscriptions, click-through rates, or any other statistic.
However, the sample size must reach a certain threshold before any inferences can be formed about the results with confidence that they are accurate and not the outcome of an aberration. If this condition isn’t met, the validity of your multivariate test results is called into question, which could lead you to make decisions that are bad for your website.
If you’re concerned that your website isn’t getting enough visitors, investing some money into paid advertising or search engine optimization (SEO) could be a good idea. After you’ve amassed the necessary number of traffic, you can start A/B testing whenever you choose and start seeing a huge return on investment almost immediately.