One of the most overlooked – yet critical – factors in a successful UX test is the experience level of your users. Whether you're testing a product with lifelong experts or people who are seeing the interface for the first time, their familiarity greatly impacts how they interpret your tasks, how they respond, and what sort of insights you can reliably gather.

What experience level means in UX tests

Experience level refers to how familiar a user is with the product, category, or interface they're being asked to interact with during a UX research session. For example, an expert user might be someone who uses accounting software daily, while a beginner might be a brand-new small business owner seeing financial software for the first time. In user testing, those distinctions matter more than many teams realize.

Why mismatched testing leads to poor data

If your tasks are written from the point of view of an advanced user – using product jargon or assuming previous knowledge – novice users may feel lost. They'll either struggle and fail the task (which may mimic a real-world experience, but not tell you why), or provide limited feedback because they didn’t understand the test itself. On the flip side, if you oversimplify tasks for experts, you risk missing out on the deeper usability insights experienced users can provide. You also might appear to be "testing the obvious," which can skew perception of your research quality.

Examples where experience impacts test results

• A beginner using a banking app might need guided instructions through a mobile deposit task. An expert can probably complete it in seconds and comment on interface friction or errors.
• Testing onboarding flows? Novice feedback helps you fine-tune first impressions, while expert feedback might skip to account management and retention pain points.
• Comparing both users in the same task without adjusting difficulty or expectations could result in confusing, inconsistent data.

What happens when you ignore experience level?

Here are some common issues that emerge when experience levels aren't considered during user testing task design:

• Misleading conclusions – such as thinking a task is "too hard" when only novice users failed to understand it
• Lack of actionable insights – because users aren't challenged enough or too overwhelmed to give useful feedback
• Difficulty comparing results – which defeats the point of running a mixed-audience study

Accounting for user experience levels isn’t just a best practice in research planning – it’s essential for getting results that reflect your real user base. That’s why more companies are turning to seasoned professionals to help set up their tests for accurate interpretations. At SIVO, our On Demand Talent experts help teams analyze their target users and build test structures that work across experience levels – ensuring clarity, consistency, and smarter recommendations.

DIY research tools like UserTesting have opened the door for faster, more cost-effective UX research – but they come with their own set of risks. While these platforms make it easy for teams to launch tests, many beginner and even intermediate researchers struggle with designing tasks that deliver usable insights. Task design isn't just about writing instructions; it's about creating a structured pathway for your users that mirrors real behaviors and surfaces useful feedback.

Mistakes researchers often make in DIY UX testing

Here are a few of the most common problems we see when teams use platforms like UserTesting without formal research training or guidance:

• Tasks are too vague: Instead of a clear action, testers are given general prompts like “Explore the site” or “Tell us what you think.” These often lead to surface-level input.
• Too complex for beginners: Novice users are asked to complete multi-step flows with minimal guidance, creating confusion or drop-off.
• Too simple for experts: Power users breeze through tasks they’ve completed hundreds of times before – offering little feedback beyond the obvious.
• Stacked tasks or leading instructions: When tasks give away the answer or assume prior knowledge, the data becomes biased or invalid.

Consequences of poor task structure

When UX testing tasks are poorly structured, your study results can suffer in several ways:

• Unclear feedback: Users may not understand what they're supposed to do, and their feedback becomes general or contradictory.
• Non-comparable results: If different tasks are used for novices vs. experts without a calibration plan, it's impossible to spot meaningful patterns.
• Wasted research time: Poorly structured studies often have to be repeated – increasing costs, delaying decisions, and eroding team confidence in DIY tools.

How to improve task design in DIY platforms

You don’t need to abandon self-service platforms – you just need better research planning and thoughtful task design. Key tips for improving your user testing structure:

• Use task breakdowns: Divide longer journeys into small, clearly defined steps.
• Tailor language to the audience: Avoid jargon with novices, and challenge experts with real-world scenarios.
• Test for understanding: Run pilot tests to ensure your tasks make sense to every user, not just insiders on your team.
• Balance coverage: If testing both experience groups, include shared tasks with neutral language for comparability.

If tackling all this on your own is overwhelming, many teams bring in experienced help to structure and guide their DIY research effectively. SIVO’s On Demand Talent can provide temporary support or think of them as a strategic partner who understands how to make your testing platform investment work smarter. These professionals not only calibrate your tasks for better accuracy – they also build your team’s own capabilities to design better studies in the future.

Ultimately, successful DIY UX research doesn’t come from the tools alone – it comes from the expertise behind how those tools are used.

One of the most common challenges in UX research is ensuring that task results are comparable across different user experience levels. If your test includes both beginners and experts, and you’re seeing drastically different outcomes between the two, the issue may stem from how the tasks are structured, not from the users themselves.

Many teams using DIY research tools struggle with designing user testing tasks that are equally effective for users at both ends of the experience spectrum. The result? Data that is hard to interpret, misleading insights, and flawed decision-making.

Why You Can't Use One-Size-Fits-All Tasks

Experts move quickly and rely on intuition, while beginners may need more guidance and context. Presenting both groups with identical tasks might skew your results. For instance, a beginner may take five minutes navigating a new app feature, while an expert completes it in 30 seconds – but does that mean it’s intuitive? Not necessarily.

Strategies to Bridge the Experience Gap

The goal isn’t to make the test easier or harder for one group but to make tasks equally diagnostic. Here are a few approaches to consider:

• Calibrate instructions: Offer slightly different framing while keeping the task objective the same. For beginners, include more context or examples. For advanced users, jump straight to the challenge.
• Use scaffolding: Allow beginners to complete a warm-up task before attempting more complex flows. Experts can skip these or be given deeper contextual layers to simulate real-world use.

Keep Outcome Comparability Top of Mind

Well-calibrated task design focuses on what you want to learn, not just what you want users to do. Consider using benchmark questions (e.g., “What did you expect to happen next?”) that help uncover thought process differences without changing the core task.

For example, in a fictional consumer e-commerce test, both beginners and experts might be asked to find a specific product. Beginners may need guidance on filtering options, while an expert might be challenged more by comparing features across similar SKUs. If both are being asked to choose based on specific benefits – you're still testing the same outcome, just tailoring the route to get there.

When testing across levels, it’s especially important to document your task adjustments. This ensures results stay rooted in the same research objectives and allows for cleaner cross-analysis later.

When testing with both newcomers and experienced users, it’s easy to unintentionally introduce bias or frustration – especially in unmoderated user testing sessions where there’s no real-time facilitation. Poorly structured tasks often lead to premature drop-offs, participant confusion, or skewed feedback from both ends of the spectrum.

Common Pitfalls that Lead to Incomplete or Misleading Data

Here are a few mistakes seen in DIY UX research platforms when designing for mixed-experience audiences:

• Overloaded instructions: Beginners may get overwhelmed by jargon or multi-step instructions, while experts may skip over them without fully absorbing the task.
• Assumptions of background knowledge: Tasks that assume familiarity with UI conventions or system logic will alienate novices, causing higher disengagement or inaccurate responses.
• Task fatigue: Experienced users may check out if tasks feel too simplistic, while beginners may burn out with overly complex flows.

These issues often result in skewed feedback or task abandonment, especially when no moderator is present to adjust in real-time. It leads to lost data – and lost time.

How to Reduce Risk in Multi-Level Testing

To prevent test failure, prioritize the following:

Create leveled task flows: Design optional branches within task flows depending on the user’s performance. For example, if a beginner struggles with an onboarding screen, offer the option to try again or skip with explanation – ensuring you still get valuable feedback.

Clarify expectations at each step: Set clear, concise objectives for each task, like “Tell us what you expect to see when you click here.” This encourages thoughtful engagement without being intimidating.

Use calibration questions: Start your test with situational questions that help segment users by digital experience. This helps you tailor the task logic and their journey, even in tools like the UserTesting platform where personalization options may be limited.

Proper planning in your early research setup plays a big role in reducing both unconscious bias and unnecessary friction during testing. Balanced task design ensures that all users – regardless of experience level – feel comfortable engaging with the test, leading to cleaner, more accurate insights.

Whether you're just starting with DIY research tools or running UX tests regularly, one of the hardest things to optimize is your test setup – especially when designing tasks for multiple experience levels. This is where SIVO’s On Demand Talent solution can make a meaningful difference.

Expertise Where It Matters Most

Our On Demand Talent are not freelancers or generalists. They are seasoned insights professionals with the experience to know what makes a user test succeed – or fail. They understand how to:

• Structure user testing tasks that uncover true behavior
• Account for subtle user differences across groups
• Balance qualitative and quantitative needs within a test

This means they can jump in quickly to elevate your research quality without needing weeks of onboarding or training.

Fixing Fail Points in DIY Testing

DIY platforms like the UserTesting platform are powerful, but only when task design is aligned with your research planning goals. Common mistakes we see include misaligned objectives, vague or biased prompts, and under-preparation for mixed-audience reactions. Our experts help you avoid those traps – leading to stronger, more actionable results.

Whether it’s cleaning up protocols before launch, diagnosing performance issues mid-test, or debriefing with your team after results come in, On Demand Talent professionals act as embedded team members who ensure you get the most from your tools and your respondents.

Build Capabilities While Closing Gaps

Unlike outsourced consultants, On Demand Talent doesn’t just deliver a research report and walk away. These professionals become internal champions – helping your team learn by doing, improve your confidence with UX research tools, and build internal capability for the long term.

So whether you need temporary bandwidth to hit deadlines or want ongoing support to manage study design complexity, our On Demand Talent gives you the flexibility to adapt and scale – without sacrificing quality or insight depth.