Launching a new feature is always a risk – especially if you haven’t confirmed whether users will notice it, understand it, or know how to use it. This is where early-stage UX research becomes essential, particularly around two often-overlooked questions: Can users find the feature? And when they do, do they understand what it’s for?

What Is Feature Discoverability in UX Research?

Feature discoverability is the ability for users to notice and locate a feature within your product’s interface without any help. Poor discoverability can lead to unused features, wasted development effort, and user frustration. You might design a powerful tool – but if it’s hidden behind a confusing navigation path or placed where users don’t expect it – they may never engage with it.

Why Comprehension Matters Just as Much

Even if a feature is easy to find, users need to quickly understand what it does and why they should use it. This is where feature comprehension becomes critical. Misunderstood features can lead to task failures, increased support tickets, or users abandoning the experience altogether.

Testing Early Helps You:

• Confirm whether users notice new features during natural usage
• Uncover flaws in labeling, icons, or UI placement
• Verify if user expectations align with how the feature works
• Reduce rework by identifying issues before launch

Mental Models and User Expectations

A common reason new features fail is a misalignment with user mental models – the assumptions users bring into a system. For example, if a file-sharing feature is labeled "Send" instead of "Share," users might interpret it as emailing a document rather than granting access. Early usability testing allows teams to catch these mismatches and update language or design before launch.

The Right Time to Do It

Feature discovery testing doesn’t require a live product. You can begin with prototypes, wireframes, or even click-through mockups. Early testing saves time and surfaces insights before technical investments are fully baked.

Effective research planning prioritizes these areas – especially when working on fast timelines or limited resources. Partnering with On Demand Talent can help product teams design quick, targeted studies to assess discoverability and comprehension early, ensuring your features are meaningful from day one.

The rise of DIY research tools has unlocked new ways for UX and product teams to test ideas quickly. Whether you're using remote testing platforms, AI survey builders, or automated usability sessions, DIY tools promise fast insights at a lower cost. But as with any tool, their effectiveness depends on how they're used – and many teams run into the same challenges when planning UX research for new features.

Top Pitfalls of DIY UX Research

1. Misaligned Study Objectives

One of the most common problems is rushing into a test without a clear purpose. Teams often run sessions that generate lots of data – but not the insights they actually need. Without proper planning, DIY tools can lead to unfocused questions or tasks that don’t tie back to key usability goals.

2. Misinterpretation of Mental Models

DIY platforms might help you observe user behavior, but interpreting that behavior requires experience. For example, if users skip over a feature, is it because they didn’t find it – or because their expectations led them elsewhere? Identifying mental model mismatches requires context and analysis that tools alone can’t provide.

3. Over-Reliance on Quantitative Metrics

Many DIY tools emphasize surface-level data like click counts, time on task, and success rates. While useful, these metrics don’t always explain why users struggle. Qualitative feedback is often limited or poorly synthesized without expert moderation or analysis.

4. Bias in Task Design or Surveys

New researchers may unintentionally write leading tasks or biased survey questions, skewing results. Without objective input, internal teams can reinforce their own assumptions instead of uncovering user needs.

5. Bottlenecks in Execution

Even with great tools, running a quality study takes time and skill. Designing tasks, screening participants, analyzing open-text feedback – all of these steps can slow down already-stretched teams.

How On Demand Talent Solves These Issues

Rather than abandoning DIY tools, many leading brands are pairing them with On Demand Talent – experienced consumer insights professionals who bring human expertise to digital research environments.

These experts can help:

• Clarify study goals and align them to product KPIs
• Design unbiased tasks and valid participant flows
• Interpret insights through the lens of user expectations
• Ensure findings speak to stakeholders and drive action

Think of it this way: DIY tools are like a high-end camera, but without a trained photographer, you're unlikely to get the shot you need. On Demand Talent acts as that photographer – turning a tool into a powerful storytelling machine.

Scaling Research Without Compromising Quality

As teams adopt faster workflows and AI-enabled platforms, flexible expert support becomes even more valuable. With flexible access to professionals – often within days – SIVO’s On Demand Talent model allows you to scale research capacity without hiring full-time, train junior employees through mentorship, and protect quality even as timelines shrink.

DIY research isn’t wrong – it just works best when balanced with the right expertise. Understanding your tools, knowing your gaps, and having experienced professionals support your team can be the difference between feature success and failure in rapid product cycles.

When testing new features, product teams often overlook one of the most influential factors: a user's mental model. A mental model is how a person believes a system or feature should work, based on their past experiences with similar products. If your new feature doesn't align with a user's expectations, even the most innovative design can fall flat during usability testing.

For example, if your team introduces a swipe gesture to perform a task typically done with a tap, users may completely miss it—not because it's a bad feature, but because it clashes with how they expect mobile apps to behave. This disconnect can lead to false assumptions that the feature itself is flawed, when in reality, it's an issue of discoverability and comprehension rooted in mental models.

Understanding mental models helps UX researchers evaluate not just how a user behaves, but why. Here’s how they affect testing outcomes:

User expectations influence discoverability

Many failed features during early testing are not inherently unusable—they’re simply unexpected. If users can’t immediately recognize the purpose or function of a new feature, they may ignore it or assume it’s broken.

Context matters more than instructions

Even when a new feature is explained in task instructions during testing, users will still act based on how they expect a digital product to work. If your tool relies on behavior that feels unnatural to them, the feature may underperform in testing.

Mental models differ across user groups

What feels intuitive to power users might baffle newcomers. A fictional startup developing an AI-based sorting tool discovered in early research that their target audience expected “drag-and-drop” functionality, but the product supported voice commands. The core feature wasn't being used—not because it didn’t work, but because users weren’t thinking along those lines.

To effectively test new features, researchers should explore mental models before usability tests begin. Simple methods like open-card sorting or short interviews about similar tools can uncover hidden expectations. This insight gives context to testing results and helps teams refine designs that better align with what users already know or expect.

Ultimately, aligning with mental models leads to more accurate feature feedback and clearer insights for product development. And when DIY research tools are in use, this step is easy to miss—but it’s one that can dramatically improve research quality.

As UX research teams increasingly adopt DIY research tools, many quickly discover a common challenge: having the tools is one thing, knowing how to use them effectively is another. That’s where On Demand Talent makes a measurable difference. These are experienced consumer insights professionals who can step in quickly to guide planning, conduct studies, and translate data into clear, actionable insights—without the overhead of hiring full-time team members or managing freelancers.

Too often, teams using platforms like UserTesting or Maze find themselves going through the motions—selecting tasks, uploading mockups, and sending out tests—but still walking away with confusing or inconclusive results. This usually happens not because the tools are flawed, but because the strategy behind the tests is underdeveloped, especially when teams are under pressure to move fast.

Here’s how On Demand Talent enhances feature-focused UX research:

• Brings immediate expertise on how to plan UX research – Professionals know how to design studies that target discoverability, comprehension, and expectation gaps. They help you ask the right questions and frame tasks clearly, avoiding invalid or biased feedback.
• Solves common mistakes in DIY UX research tools – Misinterpreting early user reactions, skipping mental model checks, or testing without defined success criteria are all preventable errors that seasoned talent can help avoid.
• Teaches your team how to get more from existing tools – On Demand Talent supports team capability-building by showing how to use your DIY platform effectively. Over time, your internal staff becomes more confident and independent, boosting long-term value from your research tech investments.

Unlike freelancers or consultants, On Demand Talent from SIVO are vetted insights professionals who specialize in real-world research for product development and usability testing. Many have worked across diverse industries—from healthcare tech to CPG—and hit the ground running.

One fictional example: a mid-sized e-commerce company rolled out a “Save for Later” feature they believed was intuitive. But early DIY testing showed mixed results. An On Demand Talent professional joined the team for three weeks, redesigned the user tasks, added mental model probing questions, and identified that users thought the heart icon meant “favorite,” not “save.” A quick UI shift later, the same feature received strong feedback in testing. That minor change unlocked the feature’s true value—without lengthy delays or budget overruns.

When digital teams are stretched thin, On Demand Talent isn’t just helpful—it’s strategic. They reduce risk, fill skill gaps, and ensure that research reflects real user experience, not assumptions.

Planning effective UX research for new features doesn’t require huge budgets or long timelines—it requires clarity, user empathy, and thoughtful structure. Whether you’re using DIY tools or working with internal researchers, a strong research foundation ensures you avoid common problems during early testing and get results you can trust.

Start with clear research questions

Before building a test, define exactly what you want to learn. Are you trying to measure if users notice a new feature (discoverability)? If they understand what it does (comprehension)? Or how it fits into their current workflows (integration)? Clear objectives guide everything from participant selection to task design.

Test early and iteratively

Studies focused on feature discovery should happen early in the product development process—ideally before launch. Even low-fidelity prototypes or clickable wireframes can uncover key usability insights long before resources are committed to final builds. Planning smaller, faster research loops can lead to quicker, risk-reducing iterations.

Balance tasks with natural behavior

When using DIY usability testing tools, it’s easy to over-engineer test tasks. But scripted instructions can skew results, leading to artificial behavior. Ask users to explore or complete a goal, rather than following step-by-step prompts. This helps reveal if the feature is truly discoverable in a real-world context.

Recruit participants that match target users

New features shouldn’t just work—they should work for the right users. Make sure the people testing your product reflect the behaviors, goals, and digital literacy of your actual audience. If you're testing a B2B dashboard, casual app users may not provide relevant feedback. DIY recruiting tools can help, but expert guidance ensures the sample is well-matched.

Document both what users do—and what they believe

Behavioral metrics (like if they clicked the button) matter. But so does user perception. Include short interviews, think-aloud protocols, or post-task surveys. This combination helps reveal how closely the feature aligns with user mental models—and where it may fall short.

Planning UX studies with these principles will help you avoid vague feedback, misinterpreted test results, and unnecessary design churn. And if your team’s bandwidth is limited, adding On Demand Talent ensures you have the right strategy and skillset in place, right when you need them.