A/B testing is one of the core methods used in Conversion Rate Optimization. It allows us to compare different versions of a webpage and measure which version performs best based on real user behavior. And with AI making it easier than ever to generate ideas and variations, validating what actually works has become even more important.
Every experiment starts with a hypothesis based on research and data. We identify an opportunity for improvement, create one or more variations and define what we want to measure.
For example, imagine above checkout where the promo code field is immediately visible in Version A, while in Version B it is hidden behind a “Have a promo code?” option. The hypothesis could be that a prominent promo code field distracts customers while completing their purchase, potentially causing them not to finish the checkout. By testing both versions, we can measure whether making the field less prominent actually leads to more completed orders.
Once the experiment is live, visitors are randomly divided between the original version, known as the control, and one or more variations. Each visitor sees only one version, allowing us to compare performance as fairly as possible.
Once a test is running, we track a (number of) predefined primary metric(s). Depending on the experiment, this could be conversion rate, add-to-basket rate, average order value, sign-ups or another metric that best reflects the goal of the test.
The variation with the highest result is not automatically the winner. A temporary uplift can be caused by chance, so we need enough traffic and data to determine whether the difference is statistically reliable.
AI can help us work faster. It can support research, generate new hypotheses, create variations and help identify patterns in large amounts of data. This means experimentation teams can explore more ideas in less time.
But AI cannot tell us with certainty how real customers will respond to a change. A variation may sound convincing or look promising, but until it is tested with actual users, it remains a hypothesis. That makes experimentation just as relevant in an AI-driven world, if not more so.
Ultimately, A/B testing helps us move away from assumptions about what we think will perform better. AI can help us generate and explore ideas faster, while experimentation shows us what actually works.
By combining both, we can make better data-driven decisions and continuously improve the user experience and business performance.
Want to know how A/B testing could work for your business, or how AI can support your experimentation process? Give us a shout!