The core idea of UX (user experience) is to understand the desires and needs of users and make improvements that cater to these needs. To understand users' emotions and thoughts, techniques known as UX testing methods are used frequently. Among the most commonly used UX tests are A/B tests. Let's take a detailed look at where and how A/B tests are applied.

What is an A/B Test and Where is it Applied?

A/B Testing prioritizes user experience and helps make improvements by leveraging insights from the UX field. It is contucted to determine which of two different versions of a variable (such as a website, button color, or text style) performs better. An ideal A/B test is performed on a live audience where is the original design is called A, the variant version is called B and generally one element (button, image, or description) differs between the two versions.

To measure the performance of the variable in an A/B test, user traffic is evenly divided between both alternatives. User traffic data is measured based on predefined metrics such as conversion rate, click-through rate or sales. At the end of the test, the element that performed better between the two versions is integrating into the design. The B variant can be changed with various alternatives until the best results are achieved.

A/B tests can be used in different areas to analyze user behaviors and achieve better results.

Examples of A/B Test Applications 

Many features on websites and mobile applications can be improved using A/B tests. Button colors and sizes, text headlines, content layouts, and navigation menus can be optimized to find the best possible version. For instance, the "Buy Now" button and the "Add to Cart" button are tested as A and B versions and changes in the users' purchase rates are measured. If the "Buy Now" label increases sales, the buttons are updated based on this data.

Ads, landing pages, email subject lines, content, and even the sending times of emails can be determined through A/B testing. In addition to two different email formats, emails sent at different times can also be treated as different variables. Based on the test results, the variables that lead to increased click-through rates are preferred.

Data obtained from these tests can be used to increase sales on e-commerce sites/apps, boost social media engagement, and for other purposes. Additionally, newsletters, SMS, ads, and many other digital marketing products can benefit from A/B testing.

7 Steps for Fast and Effective A/B Testing

Certain steps must be followed to achieve efficient results from A/B tests. Here’s what you need to do for a fast and effective A/B test:

1. Set a Goal

The problem needs to be solved or the area to be improved must be clearly defined before starting to an A/B test. The clarity and measurability of the goal are factors that increase the chances of success in the test. For example, goals such as increasing the click-through rate of daily reminders by 25% in health-related mobile apps can contribute to both the product’s value and user loyalty.

In fields like finance and healthcare, where user security and sensitivity are paramount, goals should focus on improving user experience and building trust. Test results will be more meaningful by setting a clear and industry-specific goal.

2. Define the Variables

Once the goal is established, it's time to choose the variables. When defining variables, it’s important to limit the changes to just one element and keep the scope narrow. UX research is also a valuable resource when defining variables. Measuring the impact of changes on the right variable ensures a more effective result. Button color and size, text arrangement, visual usage and design layouts are commonly preferred variables.

3. Create a Hypothesis

At the third step, a hypothesis is created regarding the impact of the variables. Hypotheses formed based on the data obtained in the goal-setting phase help the test yield accurate results. It’s recommended to use simple and easy-to-understand hypotheses rather than complex ones. For example, "Changing the button color to blue will increase the click- through rate by 20%" or "Using large visuals on product pages will increase sales by 5%."

4. Choose the Right Metrics

The metrics need to be clearly defined to measure the impact and success of the change during A/B testing. A/B tests are conducted with two types of metrics. Primary metrics help determine whether the change has a real effect on user behavior, while guardrail metrics determine whether the effect is superficial or genuinely impactful. For instance, the click- through rate of a button is measured using the primary metric while the conversion rate of users clicking on a product page is analyzed with the guardrail metric.

5. Set a Time Frame

The time frame is determined based on the sample size required for the test. Three factors should be considered when setting a time frame: initial value, minimum detectable effect, and statistical confidence threshold. The initial value refers to the current data (e.g., the button's click-through rate). The minimum detectable effect is the smallest change you wish to measure (e.g., a 10% increase). The statistical confidence threshold is set to ensure the reliability of the results (typically 95%).

For example, if you're planning to conduct an A/B test to increase the click-through rate of a "Buy Now" button, you would set the starting value at 5% based on existing data. If your target is a minimum 15% increase in the click-through rate, this would indicate a minimum detectable effect of ±0.75% around the 5% baseline. In other words, a rise to 5.75% would make this change detectable. When aiming for a 95% confidence level, the sample size calculation reveals that 20,000 users would be required for the test. For a website with 1,000 daily users, the test would need to run for at least 20 days.

6. Define Hypothesis-Based Variations

One of the A and B variables is defined as the control and the other as the competing variation. The control refers to the unmodified variation. For instance, the red button color would be the control variation (A), while a blue button is tested in the B variation. To assess the impact of current visuals on product page sales, B would contain larger images as compared to A.

7. Result Analysis

In the final step, the metrics are carefully analyzed. If there’s no significant difference between the variations, the hypothesis and experiment design may be revisited. The strategy is adjusted and the tests continues. It’s crucial to store the data and insights obtained for future improvements. Therefore, findings are analyzed and applied in future tests. This data-driven approach creates a key focus for achieving long-term goals.

Transform the Experience with VOYA

At VOYA, we offer a wide range of services from MVP product design to UX audits and research, development services to SaaS design, tailored to every brand and product's needs. Through our UX audit service, we help create experiences that enhance user loyalty, address their needs and meet their expectations. To better understand your users and create successful digital products, you can schedule a meeting with us today!