Automated A/B Testing: 5 Ways To Use ML To Improve Your UX Design

The UX design scene is quickly becoming the newest arena for AI-powered innovation. As an industry all about creating a positive experience for all consumers, UX designers, marketers, and site builders have worked together for years to craft brilliant user experiences manually.

However, thanks to the rapid development of machine learning technology, modern-day UX teams can also prioritize speed as well as precision when crafting online experiences.

According to research by Forrester, a positive UX design can increase conversions by more than 400%. When integrating AI and machine learning into your strategy, you ensure that every base is covered.

In fact, a staggering 62% of UX designers now claim to already use machine learning to increase their productivity, personalize their design and automate tasks such as A/B and accessibility testing.

With this in mind, we’re exploring the potential benefits of introducing machine learning to your UX strategy in 2024.

What is Machine Learning (ML)?

Many of us confuse the term machine learning with artificial intelligence. However, these are both different practices.

Machine learning is just one field of artificial intelligence. It focuses on the development of models that can learn from data to enhance predictions and automate decisions.

One example of a machine learning model is the famous ChatGPT.

As a generative AI tool, ChatGPT's responses have appeared more humanized as it learns more about our language structure from millions of prompts worldwide.

The best thing about ML is its ability to improve over time. As the model receives more data, it continues to learn and improve its performance.

Here are some of the ways ML could be applied to your UX strategy:

• Improving user experience using predictive analytics
• Customizing the user journey
• A/B and accessibility testing
• SEO optimization
• Detecting potential flaws in your current UX design

Now, let’s examine these in more detail and learn how you can seamlessly integrate ML into your current UX strategy.

Predictive analytics

It’s no secret that ML thrives on new data. With vast amounts of user behavior data to analyze, machine learning models can access your current site interaction and make accurate predictions based on behaviors, user preferences, and gratifications. This is why ML has become the cornerstone of offering the perfect personalized UX design.

With ML at hand, you can easily customize content based on each user. Take brands like Spotify and Netflix, for example.

They use ML to help adapt their user interfaces. Machine learning helps them create personalized playlists, predict a unique set of shows to watch, and even enhance color schemes to suit individual needs.

In fact, according to McKinsey, 71% of consumers expect brands to automatically personalize content to their individual needs in 2024, rendering ML and AI essential in the design process if you’re to see continued engagement on site.

A/B testing

Next, let’s discuss A/B testing. While this is a staple of UX design practices, it can be a long and tedious process, especially when designers need to move quickly.

While ML cannot replace human input, it can take your A/B strategy to another level. It can automatically and autonomously analyze test results before making real-time adjustments to your website in response.

Machine learning programs can also analyze user data on the go, such as a user’s session time, location, categories and products viewed, and even an individual page’s bounce rate.

Websites managed by ML are always in ‘testing mode,’ meaning that designers can highlight flaws in real-time and adjust without the need to perform regular A/B-style checks.

SEO optimisation

There are plenty of AI SEO tools on the market, but machine learning takes SEO optimization one step further.

With ML tools like ChatGPT on the market, automated content generation is one key reason why ML is reshaping SEO content creation. ML algorithms can generate high-quality content based on in-depth topic modeling and keyword research.

This means that your content will be professional as well as highly searchable.

Better still, ML algorithms can also help UX designers to generate searchable meta tags and optimised site descriptions. Using data based on user queries, ML can suggest tags and description phrases that are likely to be searched by your target demographic.

You can also add an ML-powered competitor analysis to your SEO strategy. By analyzing competitor content, ML models can identify opportunities within the SERPs to place content exactly where it is most likely to be seen.

Design accessibility

In a study of over a million websites, experts at WebAim found an average of 51.4 accessibility errors per web page.

In an era where people of all abilities can access the internet, accessible and inclusive UX design is crucial if you want to appeal to a wide convertible audience.

Machine learning has become an essential tool in the creation of accessible online content. ML models can analyze a webpage's content, visual appearance, and CTAs.

When programmed to see and think like a user with certain accessibility issues, ML models can immediately highlight areas for site improvement to create the best possible experience for all consumers.

A great example of this in action comes from Microsoft’s Seeing AI app.

Using computer vision and ML to help identify areas of a website that may be inaccessible to the visually impaired, Seeing AI automatically analyzes the real-time environment and can provide audio descriptions of elements some users are unable to interact with, such as colors and visuals.

Detect potential issues with your UX design

Machine learning models are not only excellent at gathering data from user-generated content, but also AI-powered content too.

ML models may be brilliant content generators, but they can also be used to detect AI writing.

If you’re using AI content generators such as ChatGPT, it’s important to make sure that you’re using ML prompts to inspire your own site content rather than simply copying and pasting it. This is because Google itself can detect the use of AI-generated content and, in turn, grant your website a lower authority score.

Machine learning models can also help you identify issues with your UX design in real time. By working their way through your site navigation in the same way a human user would, ML models can highlight areas of the consumer journey that may encourage bounce.

The benefits of operating in real-time also mean that ML models can detect sudden behavior changes surrounding trends and seasonal browsing patterns, giving you time to adapt your navigation map ahead of the curve.

Will AI Replace UX Designers?

The rise of AI within the UX industry has significantly reshaped the job market. Machine learning alone has transformed content creation and accessibility testing and helped brands create personalized experiences for their customers.

However, could it one day replace UX designers themselves? The short answer is no.

While we’re sure Ml will continue to amplify designer capabilities, human UX designers' skills are simply irreplaceable.