How to perform A/B testing in user interface design

When designing an interface, first impressions matter. A well-crafted UI can determine whether a user stays engaged or exits the website or app. But how can we be sure that our UI is optimized for the best user experience? The answer lies in A/B testing, a powerful method for making data-driven design decisions.

A/B testing, or split testing, is a technique that involves comparing two or more variations of a design to determine which one performs better. It’s essentially a head-to-head competition between different designs to see which one wins in terms of user engagement and performance.

Performing A/B testing

Let’s see how we can conduct A/B testing as part of user testing for a product and gain meaningful insights. There are five main steps for performing successful A/B testing:

Setting objectives
Creating test variants
Collecting data
Analyzing results
Iteration and improvement

Let’s look at an example of a multi-step form. Suppose the product team wishes to roll out this form for a specific target audience. As designers, we are tasked to create a design for this form that will be easy to navigate. We are confused about where to place the “Next” button in the form.

Setting clear objectives

The first step in A/B testing is to set clear objectives. What do we want to achieve with our UI? Do we want to increase user engagement, drive more sign-ups, or reduce bounce rates? Defining our goals is like setting the North Star for our A/B testing journey. It gives us a direction and helps measure our success.

In our multi-step form, our goal is to measure how the completion time for filling the form differs with the placement of the “Next” button.

Creating test variants

The next step is to create different test variants for our design. As the name suggests, one experiment contains two test variants, A and B. These variants are exactly the same except for one key feature that we wish to measure.

For example, in the multi-step form, we want to compare the performance of the two different placements of the “Next” button.

Test variant A has the button placed on the sticky header in the top-right corner. It will always be visible to the user. In test variant B, the button is placed at the end of the page. The user must scroll to the end to see the button if the page is longer than the screen size. There is another scenario where there are more input fields, and the user presses the “Next” button while there are more fields to be filled; then, there are chances that the user will always press the “Next” button in test variant A.

Testing tools for data collection

To conduct A/B tests effectively, we must choose the right tools or platforms. Several A/B testing tools are available, such as Optimizely, Google Optimize, and Visual Website Optimizer (VWO).

Optimizely: Optimizely provides a comprehensive experimentation platform that includes multivariate testing, personalization, and feature management. It has historically targeted enterprise-level clients, offering extensive features and scalability for large organizations with complex testing needs. Optimizely also integrates well with various third-party tools and platforms, allowing users to connect their experiments with other parts of their tech stack.
Google Optimize: Google Optimize is a robust A/B testing platform that seamlessly integrates with Google Analytics, providing users with a familiar environment for experiment setup and analysis. This integration is often a strong point for organizations already using Google Analytics. Google Optimize supports responsive design testing, ensuring experiments are optimized for different device types and screen sizes.
Visual Website Optimizer (VWO): VWO is known for its user-friendly interface, making it accessible to marketers and designers. The drag-and-drop editor allows for easy experiment setup without extensive technical skills. It also offers robust conversion tracking and goal setup features, making it easy for users to define and measure the success of their experiments based on specific metrics. VWO includes personalization features, allowing users to deliver tailored experiences to different audience segments.

Next, we need to determine a large enough sample size to detect meaningful patterns. It is also crucial to randomize the allocation of users to different variations. This helps ensure that any external factors or biases do not skew the results. Randomization distributes users evenly between the variations, creating a level playing field for the test.

During the test, we need to collect data on key metrics like click-through rates, conversion rates, time on page, etc. In our scenario, we need to record the time a user spends to complete one step of the form. Using this metric, we will then determine the significance of the design.

Analyzing results

Here’s a sample Python code for conducting an A/B test to compare the time taken to complete a form between two variants (A and B).

import numpy as np
import scipy.stats as stats
# Sample data for completion times (in seconds) for variant A and B
variant_A_times = np.array([45, 37, 52, 60, 55, 48, 50, 42, 38, 58])
variant_B_times = np.array([39, 41, 37, 44, 40, 43, 42, 36, 45, 38])
# Perform a two-sample t-test
t_stat, p_value = stats.ttest_ind(variant_A_times, variant_B_times)
# Set a significance level (alpha)
alpha = 0.05
# Check if the p-value is less than alpha to determine significance
if p_value < alpha:
    print("The difference is statistically significant.")
    if np.mean(variant_A_times) < np.mean(variant_B_times):
        print("Variant A is faster to complete the form.")
    else:
        print("Variant B is faster to complete the form.")
else:
    print("There is no statistically significant difference in completion times between the variants.")

Lines 1–2: We have two arrays, variant_A_times and variant_B_times, containing completion times for the form in seconds for each user in variants A and B, respectively.

Line 9: We use a two-sample t-test from the scipy.stats library to compare the means of the completion times in the two groups.

Line 12: We set a significance level (alpha) of 0.05, which is a common threshold for statistical significance.

Line 15: We compare the p_value to alpha to determine whether the difference in completion times is statistically significant. If the p_value is less than alpha, we conclude that there is a statistically significant difference.

Lines 20 & 22: Depending on the results, we print which variant is faster to complete the form.

According to the analysis, variant B is a better design choice. It allows for a seamless transition as the user sequentially fills in the form and presses “Next” instead of finding it in some other part of the screen.

Iteration and improvement

We can iterate over the design and make the necessary changes based on the experiment results. If we are unsure, we can conduct another A/B test to confirm and solidify our understanding. It is a continuous journey to find the most optimal design, where we have to design, redesign, test, and retest.

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