Preference testing: Choosing the design users love

You might love a particular logo, layout, or color palette, but that doesn’t mean your audience will.

Preference testing helps bridge this gap. It shows multiple design options to real users and asks which they prefer. This way, teams can validate visual decisions early and avoid costly missteps later.

In this guide, we explain how preference testing works, how it differs from A/B testing, when to use it, and how to run a test with Maze.

Preference testing is a research method that helps user researchers compare two or more design options by asking participants which one they prefer. It’s often used to test visual elements like layouts, logos, images, or copy in the early stages of design.

Preference tests also dig into why users feel that way. This helps you identify perceptions around brand personality, trustworthiness, and visual clarity; insights that can guide meaningful design decisions.

Teams can run preference testing as a qualitative study (through interviews or open-ended responses) or as a quantitative study (surveys with structured questions, rating scales, and larger sample sizes) at scale. For the best results, use it when comparing simple, standalone screens or assets, not multi-step flows or complex interactions.

A/B testing is a quantitative research method that measures user behavior by randomly distributing live traffic between two or more design variants. It compares their performance against predefined success metrics, such as conversion rate, task completion, click-through rate, or time on task. This determines which version produces statistically significant improvements in user actions and business outcomes. It’s typically used post-launch to test what drives more conversions, clicks, signups, or other performance KPIs.

Preference testing, on the other hand, happens in the early stages of the design and development process. It’s used to compare static visuals like mockups, wireframes, or brand assets—asking users which version they prefer and why. Rather than focusing on outcomes, it uncovers user perception, visual appeal, and emotional response. It’s a fast way to gather directional feedback before a design is finalized or developed.

In short: A/B testing shows you which version performs better in the real world. Preference testing shows you which version users like better and why.

Not sure when to use preference testing? Here are a few common scenarios where it can help shape early design decisions:

Preference tests give you an understanding of your users’ preferences, likes, and dislikes, and help you understand how your users feel about different design elements. They allow you to pick your users’ brains early on in the design phase, before you’ve invested too much time and energy into a design that may not appeal to your users.

Unlike concept validation or usability testing, which evaluate functionality and feasibility, preference tests focus specifically on aesthetics and subjective appeal. The insights can guide multiple stages of the design process—from broad decisions like color schemes and layout structures to specific tweaks in typography, icons, and imagery.

Whether you’re validating visual direction or fine-tuning UI details, a well-run preference test can reveal patterns, highlight blind spots, and shape sharper design decisions. Here's how to do it right.

Do you plan to understand which design variations users prefer? Or, do you want to learn how they perceive each design in terms of brand, clarity, or trust? Taking the time to lock in these goals and upfront ensures your test stays focused and your results are actionable.

At this stage, it's also helpful to define how you’ll measure success. Are you tracking preference percentage, emotional sentiment, or alignment with brand values?

Locking in your goals and metrics early gives your test direction and makes it easier to analyze the user feedback later.

Once your objective is locked in, get your design variations ready for testing.

These can be static mockups or lightweight prototypes: for example, screens designed in Figma, or AI-generated prototypes using tools like Figma Make, Bolt, or Loveable. Keep changes focused on a small number of variables, such as layout, imagery, or copy, so you can interpret the results clearly.

Aim for two or three variants with the same level of fidelity and similar context. If one version is a polished prototype and another is a rough sketch, your test participants will react to quality rather than the actual design choices.

As is the user testing rule of thumb, you need to find test participants that reflect your target customers as closely as possible, as well as the frame of mind or context needed to understand the design.

That usually means people who either already use your product and understand the context, or people who fit your ideal customer profile and can offer ‘fresh eyes’ on a new product or redesign. The closer the fit, the more reliable your user feedback.

And if you’re using a research platform like Maze, you can access Maze panel, a global pool with millions of participants, who match specific demographics or behaviors. You can share your study link through your usual channels, send Reach email campaigns to your participant database, or trigger in-product prompts to catch people in the right context.

If you are running multiple tests and want a randomized audience across them, you can also use custom URL tools like Linkly or Nimble Links, or email A/B features, to split people evenly between study links.

With your objectives, design variants, and target audience ready, you can run the test.

Start by giving participants just enough context to understand the scenario you are testing. For example, “Imagine you have just landed on this pricing page while comparing tools for your team.” Then show your design variations in a consistent way, either one at a time or side by side.

Next, ask clear research questions. Combine a direct preference question with a small set of follow-up questions so you collect both quantitative data and qualitative feedback. For example:

This mix lets you quantify test results while still capturing nuance around user behavior, trust, and desirability.

If you are using Maze, you can also enable AI-generated follow-up questions for your open-ended questions. Maze AI uses the participant’s initial answer to ask up to three tailored follow-up questions and then helps you analyze sentiment and themes, which turns short responses into richer qualitative data.

Start by looking at the top-line test results: what percentage of participants preferred each design variation, and how did they rate each option on trust, clarity, or visual appeal?

If your sample size is large enough, use these numbers as directional evidence for which variant is performing better and where there might be gaps in the user experience.

Next, dig into the qualitative feedback. Group open-ended answers into recurring themes based on what users mention. For example, you might notice multiple participants commenting on readability issues, visual clutter, or describing one design as “more modern” or “more trustworthy.” Look for these patterns and turn them into actionable insights: what should change, what should stay, and what needs another round of iterations or additional user testing (like usability testing or card sorting).

Maze automatically generates a report for every live study, pulling key metrics, charts for closed questions, and sentiment and quotes for open questions into one place. You can filter results, customize the report to highlight the most important findings, and share or embed it with stakeholders. This means everyone can see which variant performed best, why users preferred it, and what you’ll optimize next.

While preference testing is valuable for gathering subjective feedback, it has limitations. Since participants evaluate static designs without actually using them, preference testing can't capture how designs perform in real-world scenarios.

This leads to several key concerns:

Preference testing works best when it’s structured, deliberate, and lightweight. Use these practices to keep your studies focused.

Preference testing is most useful when it slots neatly into your existing design and research workflow. Maze is built for exactly that.

With Maze, you can set up a preference test as part of a broader study or as a focused, one-off comparison. Import designs directly from tools like Figma or AI-prototyping tools like Figma Make, Bolt, and Loveable. Then use ‘Variant Comparison’, ‘Multiple Choice,’ ‘Opinion Scale,’ and ‘Open Question’ blocks to structure a clean, repeatable flow for your test.

Plus, Maze automatically generates a live report for every study, so you can see which variant users preferred, how they rated trust, clarity, or appeal, and what themes are emerging in their written feedback. You can customize these reports, filter by segments, and share or embed them in the tools your stakeholders already use.

With Maze, you make preference testing a part of how your team validates visual decisions.