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When we spoke to study creators about what can cause a study to fail, the answer was rarely a methodology problem. It was recruitment.

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If you've ever watched a study stall because the right participants didn't show up, you already know the problem we're trying to solve. The study is ready. The tasks are written. Stakeholders are waiting. And then recruitment breaks down. 

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Participants trickle in too slowly. The participant quality isn’t quite right. The timeline shifts. Confidence in the findings edges downward.

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Recruitment: a moving target

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Recruitment has always been a moving target. There are endless ways to improve it – through reach, participant quality, fraud prevention, and ease of use. 

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As part of our ongoing work to strengthen our recruitment offering, we’ve reimagined self-service recruitment at Optimal. We’ve taken a closer look at the experience and focused on giving research teams more options, greater control, and confidence in participant quality when recruiting participants.

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What we heard from research teams

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Across the teams we spoke to, several consistent themes emerged. Quality was a persistent concern. The issues were usually about fit. Participants sometimes didn’t meaningfully engage with the tasks or dropped off mid-session. 

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"Niche" participants were referenced often as well. What was usually meant wasn't rare profiles but it was a combination of specific targeting and filters applied at once. Seniority plus industry plus behaviour plus location. Multiple criteria that some existing tools just didn't handle well.

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On the coordination side, we heard a consistent ask: researchers want to know who they've already spoken to. They don't want to contact the same people repeatedly without realizing it. They're tired of managing participant lists manually just to avoid that problem.

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In short: speed matters, but predictability, visibility, and participant quality matter more.

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Built for real-world studies

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We've rebuilt Optimal’s self-service recruitment from the ground up.

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Participant Profiles: more flexibility and control

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The centerpiece of the new experience is Participant Profiles. Build a profile for exactly who you need from demographics, location (down to the city), job title, seniority, company size, device, industry, B2B, language, and more. You’ll also have the ability to:

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• Run multiple profiles in a single study. If you need senior decision-makers from the UK and individual contributors from the US, you can define both profiles with separate quotas and recruit them simultaneously for the same study.
  
  
• Set multiple quotas per profile. Define not just who you need, but how many of each. So if your study needs 30 brand managers and 20 operations managers, you can recruit for both.
  
  
• Set exclusion controls. One of the clearest things we heard: researchers don't want to keep recruiting the same people. It can create a bias in their data, but managing participant history manually can be resource-intensive and tedious. Exclusion controls let you automatically exclude participants who've taken part in your studies within a set period; for example, the last three months.

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More panels, more reach

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To help research teams reach the right participants with greater reliability, Optimal’s self-service recruitment now draws from multiple panels. This increases the pool of available participants across regions and segments, giving research teams more opportunities to find the right fit even for more specific or multi-criteria profiles.

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Access millions of participants across over 150 countries, with expanded targeting options beyond general population samples, including B2B audiences.

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Visibility and predictability, from setup to launch

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Before you launch, you'll see an estimated fill time, credit cost, and a feasibility check based on your profile and quotas. After launch, live recruitment progress sits alongside your study responses. You can see exactly how each profile quota is filling in real time.
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Why this matters now

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The teams we spoke to are running more ambitious studies than ever – recorded sessions, concept tests, and studies that sit between a quick survey and a full moderated interview. These studies demand reliable, targeted, well-matched participants. Recruitment is often the deciding factor in whether a study succeeds.

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Recruitment has always been the part of research that feels the least in your control. We want to change that, not adding more complexity, but by giving you the right tools to recruit exactly who you need.

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You should be able to see what's happening, trust that the right people are coming through, and stay focused on your study. Get the confidence in your participants so you can focus on what comes next: the decisions that matter.

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Profiles are now available in Optimal’s Usability Testing tool and will be rolling out across the rest of the platform.

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Explore the new recruitment experience in your account or book a demo.

Great design decisions are grounded in evidence. But finding the right evidence isn't always easy when it's spread across multiple teams, usability tests, interviews, and surveys.

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Instead of searching through reports or asking teammates if research already exists, Model Context Protocol (MCP) lets you ask questions about your research repository in natural language from AI tools like ChatGPT, Claude, Gemini, and Cursor.

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Whether you're designing a new feature, iterating on a prototype, or preparing for a design review, MCP helps you quickly bring user evidence into your workflow.

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Here are seven ways UX, product, and experience designers can use MCP throughout the design process.

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1. Start every design project with what users already told you

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Before opening Figma, understand what users are trying to accomplish, where they're struggling, and what your team has already learned.

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Try asking:

• What have we already learned about onboarding?
• What usability issues have we identified in checkout?
• What are users trying to achieve when managing their account settings?
• What research should I review before redesigning navigation?

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Designer workflow

Before kicking off a redesign, ask MCP to summarize existing research. Use the findings to define design goals, identify constraints, and prioritize the problems worth solving before creating your first wireframe.

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2. Validate design concepts before investing time in high-fidelity designs

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As ideas begin to take shape, use previous research to pressure-test your thinking. MCP can surface similar studies, recurring usability issues, and participant feedback that helps you refine concepts earlier.

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Try asking:

• Have we tested a similar design before?
• What patterns have users struggled with in previous prototypes?
• What should we avoid repeating?
• Which usability findings should influence this design?

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Designer workflow

While exploring concepts in Figma, keep an AI assistant open alongside your design files. Ask questions as you work so previous research continuously informs design decisions instead of becoming something you review once at the beginning.

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3. Write stronger design rationale

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Design reviews often involve explaining why a particular solution was chosen. Use MCP to find supporting evidence from previous studies.

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Try asking:

• Find participant quotes supporting a simplified navigation.
• What evidence suggests users prefer this workflow?
• Which usability studies identified this problem?
• Show examples of participants struggling with this interaction.

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Designer workflow

Use participant quotes, findings, and usability observations directly in design specs, PRDs, or design review presentations to help stakeholders understand the reasoning behind your decisions.

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4. Spot UX patterns across products and releases

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Looking across multiple studies can reveal broader experience patterns. MCP can identify recurring pain points, emerging behaviours, and themes that may influence future design priorities.

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Try asking:

• What usability issues appear across multiple product areas?
• Compare findings from our last five prototype tests.
• Which friction points have become more common over time?
• What navigation issues keep appearing across studies?

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Designer workflow

Before planning a larger redesign, review patterns across multiple past studies. These recurring themes often highlight systemic UX issues that individual projects miss.

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5. Prepare for design critiques and stakeholder reviews

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Strong design presentations combine visual solutions with user evidence. Use MCP to generate summaries tailored to your audience.

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Try asking:

• Summarize the research supporting this redesign.
• What are the three biggest user pain points?
• Create an executive summary for stakeholders.
• What customer evidence supports prioritizing this work?

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Designer workflow

Generate concise summaries before design critiques, roadmap discussions, or leadership reviews, then pair them with your prototypes to show both the solution and the evidence behind it.

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6. Plan better usability tests

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Designers frequently need to validate prototypes, but not every question requires a brand new study. MCP helps identify what has already been answered and where genuine knowledge gaps remain.

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Try asking:

• What questions about this flow are still unanswered?
• What assumptions should we validate?
• Which participant groups haven't been represented?
• What tasks should we include in our next prototype test?

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Designer workflow

Review previous findings before writing test tasks. Build studies that extend existing knowledge instead of repeating research your team has already completed.

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7. Bring research into the tools you already use

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Research is most valuable when it appears alongside the work you're already doing. With MCP, your repository becomes accessible from AI tools that support everyday design work.

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Potential workflows

• Generate a design brief from previous research before starting a new feature.
• Draft usability findings directly into Confluence or Notion.
• Format ideas into sticky notes and prep for a design sprint with Miro or Mural.
• Create presentation-ready summaries for design reviews.
• Turn research findings into product requirements for engineering.
• Compare proposed designs against historical usability findings.
• Ask follow-up research questions while designing in Figma with an AI assistant open alongside your work.

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Instead of switching between repositories, documents, and reports, research becomes part of your design process.

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Designing with confidence

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The best design decisions aren't based on intuition alone; they're informed by a deep understanding of user behavior.

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MCP makes it easier to bring research into everyday design work, helping you move from evidence to action faster. Whether you're exploring concepts, validating ideas, preparing stakeholder reviews, or planning usability tests, you can use MCP to help your research repository become an active design partner. 

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Book a demo or log into your account to get set up with MCP. 

Walk into any product company and you'll hear the same language about being user-centric. So why does the average person still rage-quit apps weekly?  If everyone's truly putting users at the center, something's not adding up. Most companies aren't lying when they say they're user-centric. They genuinely believe it. Leadership believes it. Product teams believe it. The values are probably painted on the office wall. But there's a massive gap between claiming to be user-centric and actually building products that work for users.

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Where user insights go to die

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Let's trace what actually happens to user research in most organizations. A researcher conducts a brilliant study. They talk to real users, identify genuine pain points, uncover surprising insights about how people actually use the product. The researcher creates a comprehensive report with clear recommendations. They present to stakeholders and then…nothing happens.

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Product moves forward based on roadmap commitments made three months ago. Design tackles the projects already scoped. Engineering builds what's already been spec'd and by the time the next planning cycle rolls around, the research is old news. The context has shifted. New priorities have emerged. The research was great. It just never actually changed anything.

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The issue here isn't that teams don't value research. It's that the systems aren't set up to close the gap between insight and implementation. Research lives in one place and product decisions happen somewhere else. There's a handoff required. Someone needs to take the insights from the research and translate them into the specific contexts where decisions get made. But who owns that handoff? Usually nobody explicitly. The researcher assumes product managers will incorporate the findings. Product managers assume researchers will flag critical issues. Designers are caught in the middle, trying to advocate for users while also hitting delivery dates. So insights leak at every transition point.

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Even when research does make it into decision-making conversations, it's often in the wrong format for action. Researchers are trained to present thorough findings and all of that is valuable. But it's not always actionable. The gap between what users said and what should be done about it, is a lot bigger than most teams realize, but an even bigger problem is that product decisions can’t wait for perfect information. Product decisions need to happen fast, often in real-time, in contexts where pulling up a research report isn't practical. If research insights aren't immediately accessible in the moment decisions are being made, they effectively don't exist.

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So…What actually works?

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The companies that successfully put users at the center do something different. They build research into the decision-making flow rather than treating it as a separate activity that informs decisions. They make sure that research insights live where decisions happen, integrated into project management tools, design files, and product specs.

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Increasingly, they're also using MCP (Model Context Protocol) to connect AI tools like Claude, ChatGPT, and Cursor directly to their research repository – here's how. Instead of searching through reports, teams can ask questions in natural language to surface relevant findings, user insights, and context exactly when they need them, or generate artifacts and workflows grounded in real user research. Insights are accessible in the moment someone needs them.

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At Optimal, our design team embeds research findings directly in their Figma files. Not as links to external reports, but as context right next to the designs. When someone's reviewing navigation options, the tree test results are right there. When they're looking at prototype variations, the usability data is embedded in the file. Decisions and insights exist in the same place.

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Another great step is to make sure that all research insights are framed as recommendations, not just observations. Instead of "Users struggled with the checkout flow," the insight could be "Simplify checkout to 3 steps instead of 5, based on usability testing where users abandoned at step 4." The researcher isn't just reporting what happened. They're translating it into clear next steps.

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We also firmly believe that research should be continuous instead of episodic. Instead of big studies that take weeks and produce comprehensive reports, teams run smaller tests constantly. This doesn't replace deep foundational research. But it supplements it with rapid feedback loops that keep user input flowing into active work.

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And lastly - research should be accessible to all. When only dedicated researchers can conduct studies, research becomes a bottleneck. When product managers and designers can run quick tests themselves, with appropriate guidance and quality standards, research becomes embedded in how the team works rather than something teams have to request and wait for.

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Being genuinely user-centric requires infrastructure, not just values. You need tools that make it easy to share findings where people actually work. You need workflows that integrate research into product development rather than treating it as a separate track. You need the ability to run research at the speed decisions happen.

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Most importantly, you need to make the gap between insight and action as small as possible. The best research in the world doesn't help users if it never influences what gets built.