Smart A/B testing ideas for better digital experiences

At its core, A/B testing, also known as split testing or bucket testing, is the process of splitting traffic between two or more variations of a digital experience to determine which performs best.

Whether you're tweaking in-product notifications, refining onboarding flows, or streamlining form fields, A/B testing helps validate assumptions and guide optimisation efforts based on analytics, not guesswork.

For more complex scenarios, multivariate testing allows teams to test multiple elements simultaneously. Such as, headlines, imagery, and CTAs, to understand which combination drives the strongest impact.

By forming clear goals and hypotheses, digital product teams in pharma and healthcare can test changes designed to enhance user experience, improve engagement, and drive better business outcomes.

To ensure your A/B tests are truly actionable, you need more than a control (A) and a variation (B). You need a clear framework for measurement, with proper tracking, a reliable dashboard, and an understanding of what your data is actually telling you.

Here are the key A/B testing metrics you should be paying attention to, and what they really mean:

Conversion rate

The percentage of users who complete your goal (e.g. sign-up, click, purchase). Higher isn’t always better. Context matters.

User engagement

Looks at how users — whether HCPs, patients and consumers — interact (clicks, time on page, scroll depth). Useful for understanding behaviour beyond just conversions.

Bounce rate

Shows how many users leave after one interaction. A high bounce rate can flag poor relevance or user confusion.

Statistical significance

Tells you if a result is likely real or just random. Usually, p < 0.05 means the difference is worth paying attention to.

Confidence level

The probability your result is accurate. A 95% confidence level means you’re 95% sure the variation made a real impact.

Lift

The percentage difference between the control and variation. A 10% lift is great, but only if the result is statistically sound.

Sample size & duration

Too few users or too short a test? Your data could mislead. Wait for enough volume to make a confident call.

A rigorous testing methodology ensures reliable outcomes. Here’s how it breaks down:

When done well, this process feeds into continuous website optimisation and product experience improvements.

Segmentation in A/B testing means dividing your audience into distinct groups. See new vs return visitors, mobile vs desktop users, or even by geography, traffic source, or user cohort. Rather than measuring results in aggregate, you’re breaking them down to see how different segments respond to each variation.

Why does this matter? Because one size doesn’t fit all situations. A specific version of a product experience might perform brilliantly with one group and fall flat with another. A first-time user might need a different onboarding flow than a loyal customer. A campaign landing page that converts well on desktop might underperform on mobile. Without segmentation, these signals get averaged out and the real story gets lost.

Segmenting A/B tests helps you uncover those hidden insights. It reveals where your product is overperforming, underperforming, or needs more personalisation. It’s how you move from blanket decisions to relevant, data-driven optimisation at scale.

A/B testing in digital product design

For those responsible for designing digital products in healthcare and pharma, A/B testing is more than just a numbers game, it’s a creative tool for shaping better user experiences. Whether you’re testing microinteractions, tweaking navigation patterns, or validating layout changes, experimentation helps de-risk bold design moves.

Design-led experiments empower teams to iterate with confidence, using real user data to inform everything from wireframes to final UI polish.

The power of A/B testing lies in its ability to validate assumptions, drive statistically significant improvements, and fine-tune existing test strategies to meet business outcomes. Done well, it enhances the customer experience, optimises CTAs, improves landing pages, and helps product teams understand how different cohort analyses respond to changes.

For example, you might:

• Test two headline variations on an HCP landing page to see which drives more registrations.
• Compare a short vs long dosage explanation to improve patient comprehension and adherence.
• Experiment with CTA placement on a condition awareness page to increase downloads of educational material.
• Segment returning HCP visitors to trial a simplified navigation flow that reduces friction.

In short, it turns opinion into evidence, and evidence into action.

To make the most of the benefits of A/B testing, you need more than just tools. You need to build a culture of experimentation. Creating a thriving experimentation programme means embedding testing into your development workflow, getting department involvement, and aligning on testing protocols. It also means documenting learnings and celebrating both wins and failures as steps toward better product decisions.

This mindset is especially important in pharma, where a natural tendency toward risk aversion can sometimes limit digital innovation. But digital products should not be treated as finished the moment they launch. To stay relevant and impactful, they need to evolve, continually shaped by real user data and changing needs. When testing becomes part of your team’s DNA, you enable faster innovation, smarter decision making, and more resilient products.

As part of our work with a leading global pharmaceutical company we built a continuous improvement process focused on real user behaviour. By partnering closely with content and data teams, we analysed site interactions, identified friction points, and designed A/B tests grounded in UX heuristics.

All experiments were fully approved by regulatory teams and implemented rapidly, using existing assets to move fast.

The result? A 25% increase in HCP registration success—and a far richer understanding of what HCPs actually want.

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