What is an A/B test?

A/B testing is a method of testing in which two versions of the same element (for example, a web page element, a button, etc.) are selected. One version (A) is a control, and the other (B) is experimental (often with some variable). The effectiveness of each version is then compared by analyzing data obtained from users (for example, the rate of sales, time spent on the site, conversions, button clicks, etc.). This allows you to determine the more effective version and use it in the future.

Tools for analysis and data collection - Google Analytics ( including Google Enhanced Ecommerce), GTM, Owox Bi, Google Data Studio, Google BigQuery, Notion.

A/B testing

A/B-testing in e-commerce, gives you the opportunity to conduct experiments to find out which version of the site - version a or version b (the site version) works better and leads to more conversions. To conduct effective A/B testing to increase conversions on your site, follow these steps:


1. define your goals: what metrics you will measure the changes with (increase in the number of sales, ad clicks or average cart value) and what percentage change you consider successful in the experiment.

2. Get the data: from the analysis, select sites with low conversion rates to start making changes and make sure the target (test) version will perform better in the future.

3. Develop hypotheses: what changes you want to make to your site and what you expect them to do.

4. Create control and test groups: create two groups that will receive different versions of the site. The control group - is the baseline, the zero level, and the test group allows you to determine what changes will bring an increase in conversions. Determine the number of tested users.

5. run the test: enter the experiment into your online store and determine how long it should last.

6. analyze the results: collect information from the control and test groups and analyze them to understand which version of the site leads to an increase in conversions, an increase in traffic.

7. Make a decision: based on the result, make a decision on which version of the site changes (or functionality) should remain, and what should be changed in the future.

! It is important to remember that A/B-testing is an ongoing process, and to optimize your online store, it should be done regularly.

Example of A/B test in e-commerce

The following is an example of an A/B test to increase the average order value of an online store:

During testing, you can check:

1. Improving website conversion - this is usually the most important goal of A/B testing. You can try to test for example: different variants of headlines, call-to-action, page layouts, product card design, color change to increase the number of conversions (e.g. sales, registrations).

2. Increasing the efficiency of marketing activities - you are able to test which elements of the campaign are more effective in attracting the attention of the audience and encouraging them to take the action you want (e.g. different variants of mailing, landing page, cta.).

3. Improve website usability - testing can help you assess which user interface variant is more intuitive and useful to your audience.

4. Content optimization - A/B testing can be used to test different variants of text, images, videos and other content to increase their effectiveness in attracting attention and encouraging interaction.

5. Studying audience preferences - the tests conducted can also help you understand user preferences and behavior so that you can tailor your marketing strategy to meet their needs and expectations.

Create control and test groups

To create control and test groups for A/B testing, start by defining the goal. Then select a group of people who will participate in the test, such as website users, newsletter subscribers, etc. Then divide them into two groups at random: a control group and a test group.

The control group is the group that will use the existing version of the website or marketing campaign that is currently being used. That is, they will not be influenced by the changes made in the test group. The test group, on the other hand, will be influenced by the new changes we want to implement. It is important to ensure that both groups are representative of the entire population and contain similar demographic characteristics, such as age, gender, interests, etc. In this way, we will be able to make sure that the results of A/B testing are a true reflection of the behavior of our audience.

A/B-testing results

Analysis of the data will show that the conversion rate on some sites is low, meaning that the percentage of users who perform the action desired by the site owner (such as purchase, registration, file download) is lower than expected. This analysis shows that there are sites where it is worth focusing your efforts to increase conversions. Possible reasons for low conversion rates include interface problems, lack of adequate product/service information, long page load times, flaws in the purchase process or an overly complicated registration procedure. Therefore, site owners should carefully analyze their data and actions to improve the performance of their site and increase conversions, reduce the rejection rate.

A / B tests in e-commerce are very important in business development, because they allow you to evaluate the effectiveness of changes made to your website or application. With A / B tests, you can compare two versions of a website or app, compare the performance of different elements on a page, such as buttons, headers, content, images, and prove what works better for users.

These tests also help you optimize conversions in your online store - if a certain element of your site translates into higher conversions, you can invest in elements or strategies that lead to more transactions.

In addition, A / B testing allows you to continuously improve and refine your website or application, which can lead to increased sales, customer engagement and improved customer satisfaction. As a result, A / B testing is very important in e-commerce business development, as it helps determine what works best for users and what can bring high profits.