A/B testing of a website

Contents

• What is A/B testing?
• Why is A/B testing necessary?
• How does A/B testing work?
• What elements can be tested?
• How to conduct split testing?
• What six A/B tests showed for an online store

What is A/B testing?

A/B testing of a website, also known as split testing, is a method widely used to evaluate the effectiveness of web pages. This approach can significantly increase the conversion of landing pages, which in turn contributes to the profitability of online projects. A/B testing compares several variations of the same page, with each shown to an equal number of visitors. After a certain period, the test results are summarized, and the variant that demonstrated the best results, usually with a higher conversion rate, is selected.

Why is A/B testing necessary?

Suppose you launched an educational project, and the conversion of your landing page is only 3%. You want to increase this figure to 6%. To achieve this, you need to formulate a hypothesis about how to reach the desired result. For example, you might hypothesize that a shorter application form will be filled out more easily than a longer one and decide to remove several fields.

If you have a small startup and are just starting out, you might manage without testing. However, as traffic increases, so does the cost of mistakes. What if the number of leads does not increase, but you lose important information about your potential customers? And what if the number of leads does increase, but the final conversion does not change? To avoid such mistakes, A/B testing is applied.

When formulating hypotheses, it is important to evaluate them both in terms of the expected result (for example, you are confident that hypothesis #24 will increase conversion by 10%) and in terms of development costs. This will help prioritize hypotheses more effectively.

How does A/B testing work?

The mechanism of A/B testing is quite simple: you divide all visitors to the site into two groups and direct them to different pages. Half of the users see the control page A, while the other half sees the modified page B. This 50/50 split is not the only option; you can also use a 70/30 or 20/80 division depending on your needs.

To obtain statistically significant results, it is important that each user is shown only one variant of the page. This is usually done using a special parameter that is recorded in the browser's cookies. It is also necessary to consider all traffic channels (social media, search engines, advertising, email) and conduct measurements simultaneously. It is advisable to minimize the influence of internal factors, such as the actions of call center operators or editorial staff.

What elements can be tested?

The selection of components for testing depends on your goals and objectives. Practically every element of a web page that can convert visitors into customers is subject to testing. In the classic variant, changes to one component are tested, but there is also multivariate A/B testing, which includes several modified elements.

Some of the most commonly tested elements include:

• Headlines and subheadings;
• Texts and product descriptions;
• The amount of content on the page;
• Images;
• The appearance and text of call-to-action (CTA) buttons;
• Customer reviews;
• Registration forms;
• Page design and layout;
• Prices, promotions, and delivery conditions.

How to conduct split testing?

When conducting A/B testing, it is important not to rely solely on intuition or experience, as this may not lead to the expected results and waste time and resources. You should adhere to a specific methodology that includes several key stages:

1. Define the goal of the A/B testing and the metrics you want to improve.
2. Analyze the website using Google Analytics tools and determine how users interact with it.
3. Formulate optimization hypotheses. For example, you might hypothesize that changing the location of the subscription form will increase the site's conversion.
4. Prioritize elements whose changes will yield the maximum result.
5. Calculate the required sample size using statistical formulas or specialized services.
6. Conduct A/B testing of important hypotheses and analyze the results.
7. Share the results of the split testing with all project participants.

What six A/B tests showed for an online store

In the process of A/B testing, it is important not to make hasty conclusions. When you receive the first data that satisfies you, there may be a temptation to stop the experiment. However, it is important to remember that key metrics may fluctuate over several days. For accurate comparison of random parameters, average indicators should be evaluated, which can take from 7 to 14 days to accumulate sufficient data.

It is recommended to launch A/B tests through specialized services that will help correctly segment the audience and collect statistical data. One of the most popular tools for A/B testing is Google Optimize. This service is free, easy to set up, and integrates with Google Analytics, allowing for the testing of various page elements and automating the experiment process.