Optimal Blog

Optimal gets used for a lot of research that nobody outside the team ever sees. A card sort that reshapes a navigation menu. A week of usability sessions that kills a feature before it ships. We started the Optimal Experience Awards to put some of that work in front of an audience for once, and this year's entries made the judges' job hard in the best way.

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This year the panel scored hundreds of entries across six categories, with 14 judges reading every submission against the same rubric. Judges weighed the method behind the work as much as what came of it.

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Why we do this

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Good research tends to disappear into the decision it supported. That's sort of the point of it. A good outcome looks obvious once it ships, and nobody outside the room remembers the months of interviews and testing that got the team there. It makes the work hard to point to later. A new researcher can't easily learn from something they never got to see.

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The Optimal Experience Awards is one way to work against that. We wanted the researcher who spent a year winning back a skeptical stakeholder to get the same spotlight as whatever they eventually helped ship. Reading the studies in the open, including the parts that didn't work, makes everyone doing this work sharper.

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How judging worked

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Every entry got scored by more than one judge, working independently, against six criteria specific to its category. Research for Social Good weighed participant protection and replicability heavily. In Outstanding User Research Project, rigor carried more weight, along with how well the method fit the problem. Those scores got combined into a per-category ranking.

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Now, the results.

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Outstanding User Research Project

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This category comes down to one project, how it was built and whether anyone downstream could use what it found.

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Winner:  EE

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Finalists: Southern New Hampshire University, Google, Contentsquare

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Research-Driven Impact

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Here the panel looked past the study design and asked what changed because the research happened.

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Winner:  Paper Leaf

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Finalists: Joruney Digital, Drexel University

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UX Research Leader of the Year

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This category looks at one person's body of work, the practice they built and the people they brought up behind them.

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Winner:  Dawn Ta, Director of Research & Design at Scientella

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Finalists: Dr. Meg Kurdziolek, Sarah Geden, Aline Lin

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Collaborative Research Excellence

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This category looks for collaboration that shaped how the research got built, across teams or organizations, from the ground up.

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Winner: Oxford University

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Finalists: Thailand Development Research Institute (TDRI), London Borough of Camden, Miro

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Research for Social Good

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Judges paid close attention to how well participants were protected here, and to how much the findings ended up helping the people the study was about.

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Winner: Base22 LLC

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Finalists: Analog Devices, Minnesota IT Services

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Best Use of Optimal

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This category rewards how well the tools fit the study, and what came out of that choice.

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Winner: NHS England

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Finalists: American Electric Power, Cengage, Eaton

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Thank You

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Thanks to everyone who put a project in front of a panel of people they don't know. That's not a small thing to do.

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Thanks to the judges too, for the time they spent going through every entry closely enough to argue about a few of them: Graham Gardner (U.S. Bank), Addison English, Harry Parkes (Hargreaves Lansdowne), Erietta Sapounakis (Stan.), Tradd Salvo (Huge), John Rainey (Gallos Technologies), David Vuu (Telstra Health), Joann Wu (Uber), Alexander Wilson (T. Rowe Price), Kate Towsey (The ResearchOps Review), Christina Goldschmidt (Warner Music Group).

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If your team did work this year that belongs on this list next time, we want to see it.

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Self-serve recruitment made participant recruitment faster and more affordable, but it didn’t completely eliminate the challenge of finding targeted, engaged, and honest participants. In our recent webinar, we called out what a hundred research teams said about how they recruit, and used what we learned to build the next evolution of Optimal Recruitment.

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Summary

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• 75% of research teams say finding the right participants affects study quality or timelines.
• Self-serve tools made recruitment faster and cheaper, but shifted the work of screening, verification, chasing, and replacing participants
• 41% of researchers encounter fraudulent participants, while others struggle with participants who match criteria but provide poor-quality responses.
• Niche audiences are often difficult because multiple common criteria become rare when combined.
• The fix is a recruitment approach that combines multiple sources with automated checks and human in the loop to find reliable, high-quality participants.

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The 4 biggest challenges of participant recruitment

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1. You can't find the right people

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75% of teams say that not finding the right people impacts either their study quality or their timelines, and two in five have research that simply doesn't happen because they can't source the right participants.

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2. Some participants aren't who they say they are

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41% of researchers run into fraudulent participants, often using VPNs to present as multiple people, and knowledgeable enough to talk vaguely around a topic and slide past quality checks without saying anything specific or true.

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3. The right participant on paper isn't always the right participant in practice

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You can nail the segment, role, and behavior criteria, and still end up with someone who can't articulate a clear thought. That's pushed teams toward workarounds like video screeners to gauge whether someone is introspective and genuinely who they claim to be. Automation is good at matching criteria, but it can’t judge whether someone is introspective and would provide quality data.

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4. Stacked criteria make audiences hard to find

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Many audiences we call niche aren't actually made up of individually rare criteria. It's the combination of requirements that can make someone difficult to find. A specific role, at a certain company size, working in a specific function, using a specific tool are not criteria especially rare on their own. But layered together, the pool can get very small. 

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Solving these challenges takes a platform built to actually find the right people and verify them once they're found. 

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What is self-serve recruitment?

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Self-serve recruitment was created to make participant recruitment faster and easier. Instead of working with an agency, researchers can order participants directly based on the demographics and criteria they need for their study. But while this makes recruitment more accessible, it doesn’t eliminate the work behind the scenes. Teams still have to screen, chase, verify, and replace participants, and four out of five researchers still say the core problem is open.

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Part of the problem is the way self-serve recruitment is typically built. Most tools rely on a single panel and try to make every recruitment request fit within it. Instead, Optimal starts with who you actually need to recruit, then determines where those participants are most likely to be found. We call this a multi-panel model. Some panels are stronger for moderated B2B recruitment, others for general population studies, and others for regional coverage. By combining them, Optimal Recruitment gives you access to 20M+ verified participants across 150+ countries.

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And the data suggests that this broader access matters more than speed alone. In a study of 100 people, only 9% chose faster turnaround as the most important thing to improve. When asked to pick just one thing to fix, they prioritized access to hard-to-reach people, reliability, and better targeting.

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That’s why our focus isn’t just on getting participants into a study quickly. It’s on getting the right participants, from the right sources, with quality built into the recruitment process from start to finish.

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Ensuring participant quality

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Quality checks used to mean if they answered the screener correctly, but increasingly, it's about whether someone understood the task, engaged with it properly, and gave you something useful. Optimal Recruitment has layers of checks so you can find quality participants in every study:

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• Screeners to filter candidates before they begin your user research study
• Automated checks to flag straight-lining, repeated answers, and suspicious behavior patterns at scale
• Human in the loop catches what automation still can't, which matters more as research shifts toward formats like recorded usability tests

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Some teams want to submit an order and get straight into research. Others want more support: screener help, sourcing across panels, manual review, or a fully managed recruit. Neither is the "better" way to recruit. They're solving different problems.

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What we're doing is bringing those two worlds closer together.

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How to make a strong participant screener

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A better screener doesn't necessarily mean a longer screener It's more about being clever with the questions you're asking.

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Add a plausible but wrong option

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Rather than putting an answer or a red herring in that's really obviously fake and easy to spot, we like to include something that sounds like it could be right, but someone with genuine experience should know it isn't.

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Ask the same thing twice but in slightly different ways

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You don't obviously want to literally repeat the question, but you might validate something they told you earlier in the screener from a different angle later. If their experience is genuine, those answers should match up and make sense together. And if they don't, it's a really useful signal to take a closer look at the participant.

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Move more of your instructions into the screener itself, not just in the study

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Clear instructions are a huge contributor to participant quality, but sometimes what looks like a quality issue, it did actually start much earlier where the participant just didn't understand what they were signing up for. For example, in a recorded prototype test, state “this study requires you to speak your thoughts aloud as you complete the tasks, are you comfortable with that and in a quiet space?”; then, people who aren't comfortable can opt out before they ever get into the study.

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FAQ about Optimal Recruitment

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What are participant profiles?

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You can build recruitment profiles which group targeting criteria, like demographics, location, device, job title, seniority, company size, industry, and B2B, as well as screener questions to recruit for your studies. You can also  exclude participants you’ve already spoken to for new perspectives. 

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How many participants is enough?

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It depends on what you’re trying to learn. For usability testing, they tend to surface quickly, so around five participants per user group will often reveal almost all the problems. Concept testing may span a wider context, so you need a larger sample. 

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How do you ensure you are not hearing from the same people?

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We recently have added exclusion controls to participant profiles that lets you automatically exclude participants who've taken part in your studies within a set period; for example, the last three months. This ensures you aren't hearing from the same pool of people.

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How do you work to avoid participant fraud?

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Optimal ensures a 100% quality guarantee for participant responses by utilizing a multi-layered fraud prevention strategy that combines advanced technology with human oversight. Automated systems monitor participant behavior in real-time, detecting "speeders" by monitoring completion times, identifying skipping patterns or "straight-liners," and flagging any inconsistent responses. 

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Our award-winning panel partners maintain a fraud rate of less than 0.06% through advanced machine learning scoring systems (like PureScore) and AI-driven fraud detection, continuously screening participants. For any B2B studies, we will also cross check against LinkedIn profiles.

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Can Optimal help me recruit participants for my next study?

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Yes, Optimal can help you recruit the right participants for your study. You'll see a time estimate for when participants should start coming through after submitting a request.

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Recruitment doesn’t become a bottleneck. Your research can finally start moving at the speed of the questions you're actually trying to answer. 

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👉 If you want to experience the full walkthrough and demo of Optimal Recruitment, watch the full training webinar here.

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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.

Imagine you've just joined a new research team. There is a vast amount of research in the repository. Hundreds of interviews, usability tests, surveys, and notes. Everyone tells you, "We've probably researched that already," but nobody knows when or where.

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Instead of manually searching projects or asking around, Model Context Protocol (MCP) lets you ask questions about studies conducted in Optimal and instantly surface the evidence you need.

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Here are practical ways UX researchers can use MCP with Optimal to understand past research, accelerate new studies, and uncover insights across their repository.

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1. Get up to speed on past and current research

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One of the best ways to use MCP is understanding what's already known. Instead of combing through different studies, ask MCP to summarize existing knowledge before planning your next study.

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Try asking:

• Based on the research I’ve run in Optimal, what are the biggest UX opportunities for our product?
• Summarize the key findings from checkout research over the past year.
• What usability issues have been identified most frequently?
• What research should I read first to understand this project?

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2. Define your next research study

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Before creating your next study, writing discussion guides or recruiting participants, check what questions have already been answered and which gaps remain.

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MCP can help identify opportunities for follow-up research and prevent unnecessary duplication.

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Try asking:

• What questions about account creation are still unanswered?
• What themes need further investigation?
• Based on previous studies, what should our next usability test focus on?
• What hypotheses should we validate next?

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3. Find supporting evidence faster

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Whether you're preparing a presentation or writing a report, MCP can help to surface quotes, observations, participant metadata, and findings in seconds.

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Try asking:

• Find participant quotes describing frustration during onboarding.
• Show examples of navigation issues from recent usability tests.
• How many participants completed this study on mobile?
• Which sessions mentioned difficulty finding pricing information?
• Pull task completion rates from all prototype tests in the Dashboard project as a CSV.

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4. Discover research before starting from scratch

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One of the easiest ways to waste research effort is repeating work that's already been done. Use MCP to explore what's already in your repository before creating a new study.

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Try asking:

• What research already exists about navigation?
• Have we previously tested this feature?
• What have we already learned about search?
• Which studies relate to account settings?

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5. Identify patterns across multiple studies

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The biggest insights often emerge when you zoom out. Instead of reviewing studies individually, MCP can synthesize findings across projects to reveal recurring themes, behaviours, and pain points.

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Try asking:

• What pain points appear consistently across checkout studies?
• Compare findings from our last five usability tests.
• What themes have become more common over the past six months?
• Which usability issues keep appearing regardless of product area?

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6. Create stakeholder-ready summaries

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Research is most valuable when it's easy to share. Use MCP to turn large volumes of research into concise summaries or visualizations tailored to your audience.

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Try asking:

• Summarize this quarter's most important customer insights.
• Create an executive summary for leadership.
• Create a pie chart with a breakdown of onboarding studies by study method. 
• What are the three biggest opportunities we should prioritize?
• Write a summary suitable for our product team.

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7. Bring research into your existing workflows

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Connect it with the AI tools and platforms your team already uses so research becomes part of everyday decision-making.

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Examples include:

• Ask research questions and post insights directly into Slack.
• Create a new page in Notion summarizing research findings.
• Draft insight summaries for Google Docs and Confluence.

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Best practices for getting the best answers from MCP

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Like any AI assistant, the quality of the output depends on the context you provide. A few simple habits can make a big difference.

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Start with a clear goal

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Rather than asking broad questions, explain what you're trying to achieve. Instead of Tell me about onboarding.

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Try: I'm planning a usability study on onboarding. What problems have previous research uncovered that we should investigate further?

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Narrow your search when appropriate

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Large repositories can contain a wealth of research.

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Specify:

• Study tool and/or project
• Research method
• Time period
• Team
• Participant segment

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For example: Summarize usability studies about checkout conducted during the past 12 months.

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Decide whether you need one study or many

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Sometimes you need detailed findings from a single study. Other times you're looking for patterns across dozens of studies. Tell MCP which perspective you want.

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Ask follow-up questions

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Treat MCP like a research partner rather than a search engine. For example:

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• Can you show supporting participant quotes?
• Which studies contributed to this finding?
• Are there conflicting findings?
• What evidence supports this recommendation?

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Tell MCP how you want the answer

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Different audiences need different outputs.

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Ask for:

• Bullet-point summaries
• Executive briefings
• Presentation-ready insights
• Charts
• Tables
• Research reports
• Action items
• Product recommendations

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MCP helps researchers spend less time hunting for information and more time generating insights that move products forward. 

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How will you use MCP with your Optimal data? Whether you're uncovering past insights, planning new studies, or connecting research with the rest of your tools, we'd love to hear how you're putting it to work.