A/B testing is an industry go-to for discovering your customers’ preferences. The usual way to do this is to isolate a specific variable (eg copy, visual, audience, channel, pages) and to see which performs better with everything else being equal.
If you have an underperforming email marketing campaign for instance, you could create two templates to send to customers to see which yields click-throughs.
Small businesses and marketers have experimented with website landing pages, social content and more with A/B testing for years. But how can you automate the process to make it less onerous and laborious?
Enter artificial intelligence (AI)—the powerful tool that can streamline and change how A/B testing is done.
Understanding A/B Testing’s Relationships With AI
Research shows a conventional A/B test only has a 12% success rate for increasing conversions. AI tackles shortcomings in this strategy by promising these advantages:
#1 Automation
Manual A/B testing often requires massive time commitments. AI slashes time by suggesting test variants and sending them on assigned schedules.
Once you’ve finished programming the project using an AI tool, it requires minimal intervention. The model will send new headlines or posts calls-to-action (CTAs) on social media as ordered until there is enough data to see what works best.
#2 Dynamism
AIs have machine learning algorithms analyzing every A/B test. This constant oversight makes the algorithm an expert in your audience and marketing campaigns. Eventually, it can compare the most nuanced details across the different tests for continuous optimization.
It notices what fonts are eye-catching and where on the page a CTA receives the most attention. An AI changes future A/B tests based on this constant stream of incoming data.
#3 Multivariate Testing
You can increase the value of your A/B tests by trying more than two versions at a time. AIs juggle more variables with precision than marketers can, which shaves time, allowing you to refine multiple marketing campaigns at once or move from project to project without compromising quality.
Using Data Collection and Analytics
The usefulness of AI in A/B tests is derived from its data functionalities. It removes human error from the equation by analyzing data objectively.
What abilities do these models use to prove which A/B tests deliver conversion results? Let’s take a closer look below.
#1 Predictive Analytics
With AI-powered capabilities in A/B tests, you can make educated guesses on how your audience will change in the future. You can also do trend research to discover how top-performing competitors adapt their A/B tests to shifts.
An algorithm can do all this for you, predicting customer patterns more accurately and quickly than humans.
#2 Segmentation
Conventional market research like segmentation is time-consuming and costly. It requires you to send surveys, perform interviews or collect focus groups. Countless techniques are out there, but they all demand dedication, which takes away the best minds from other high-value work.
An AI A/B test segments business-critical data for you, including demographics, online behaviors and more. The segments inform subsequent A/B tests, making them more targeted. Personalization boosts the likelihood of conversion because the campaigns are less generic, which is why 38% of marketers rely on AI for it.
#3 Real-Time Insights
Everyone should delegate the full-time job of looking at data and translating numbers into takeaways to an AI. It is more competent at parsing immense volumes of data in no time, delivering instant gratification and feedback for high-performing tests. The immediacy allows your team to make faster decisions on directing future tests and campaigns.
Putting AI-Powered A/B Testing into Practice
How do marketers implement better A/B testing based on AI’s insights?
#1 Iterative Testing
Say your team is testing packaging prototypes that align with branding on the company’s online storefront. An estimated 81% of shoppers are willing to invest in a new product if the packaging is attention-grabbing, and design refreshes also motivate 52% of customers to switch brand loyalties. You can try multiple designs quickly until the algorithm finds what changes cause significant conversion gains.
#2 User Experience (UX) Personalization
UX is about pinpointing an exact customer and tailoring a digital marketing strategy based on them. AI can identify individual experiences with A/B tests, producing better conversions than ever.
For example, it could find the website architecture that gets people to converting links faster. It may also discover if load times for interactive designs discourage users from pursuing the site’s shop. AI A/B tests provide peace of mind by discerning these pain points and automating improvements.
#3 Automated Reporting
Reporting is one of the most tedious tasks a marketer can do, but AI-powered platforms generate A/B test insights with a few clicks. They also often have embedded machine-learning models that suggest future tests based on what they synthesize.
Then, you can work alongside data scientists, IT departments and other teams to design a comprehensive strategy for making conversions the most appealing they have ever been. Tech giants like Google and Microsoft run over 10,000 A/B tests yearly, so you can imagine how much labor AI saves in distilling research into reports.
Examples of AI Powered A/B Testing Tools
Now that you’re all excited about trying these tools, which ones can you choose? Here are some of the possibilities to consider:
- Optimizely: A comprehensive platform offering AI-driven A/B testing, multivariate testing, website personalisation, and feature management.
- ABtesting.ai: Simplifies landing page optimisation with AI-generated text suggestions and automated testing for headlines, copy, and CTAs.
- Keak: Automates website optimisation by continuously generating, testing, and implementing variations to boost conversion rates.
- StrictlyConvert: Focuses on creating and testing text variants for enhanced website conversions, integrating seamlessly with popular website builders.
- Kameleoon: Provides AI-powered real-time experimentation and personalisation to optimise user engagement and customer journeys.
Converting AI into a Conversions Master
AI models give A/B tests the data-parsing capabilities marketers have wanted for years. Because these algorithms make data easier to understand, conversion rates are more likely to rise and discovering the necessary changes to craft the perfect marketing materials for your target audience becomes more straightforward. Switch to AI-enhanced A/B tests to make communications with leads the start of a long-lasting relationship.
Eleanor Hecks is a design and marketing writer and researcher with a particular passion for CX topics. You can find her work as Editor in Chief of Designerly Magazine and as a writer for publications such as Clutch.co, Fast Company and Webdesigner Depot. Connect with her on LinkedIn or X to view her latest work.
