Introducing Live Site Testing: Real Insights from Real Interactions

Figma has long been the go-to tool for UI/UX designers, known for its intuitive interface and real-time collaboration. In fact, over 95% of Fortune 500 companies rely on Figma, and 13 million monthly active users trust it to design and prototype digital experiences.

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If you’re already designing in Figma, integrating with Optimal can help to validate your ideas early, reduce costly mistakes, and deliver experiences users actually want.

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The Hidden Cost of Skipping Design Validation

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Validating designs before development and catching usability issues early has a measurable impact on both users and the business. Research consistently shows that:

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• Early usability testing and design validation can reduce later redesign or bug-fix costs by a significant margin up to 40-50%.
• Integrating customer insights into design decisions can increase user satisfaction by up to 70%.
• Companies that prioritize UX design experience a 50% faster time-to-market for their products.

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Figma + Optimal: Prototype Testing and Design Validation

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Instead of waiting for post-launch analytics or expensive redesigns, you can test your Figma prototypes with real users in hours, not weeks with Optimal. Get quantitative data, watch recordings, analyze heatmaps, and actually see where users struggle, all before a single line of code is written.

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Here’s a look into 4 practical ways teams use Figma and Optimal together.

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4 Ways to Test Figma Designs with Optimal

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1. Preference Testing: Let Users Pick the Winner

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Ever had a debate with your team about which design direction to take? Let data decide.

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Here's how:

• Create a Figma frame with two designs side-by-side (think: two homepage variations, competing button styles, different navigation approaches)
• Copy your Figma link and drop it into an Optimal first-click test
• Ask participants: "Which design do you prefer?"
• Watch the results roll in with heatmaps showing exactly where users clicked

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2. Concept Testing: Does Your Idea Actually Make Sense?

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You've got a bold new concept. It makes perfect sense to you. But will users get it?

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The process:

• Build wireframes or mockups in Figma (they don't need to be pixel-perfect)
• Import your Figma link into an Optimal first-click or prototype test
• Create tasks like “Click the option that best matches what you’re trying to do.” or “Click where you would sign up.”
• Analyze whether users successfully understand and navigate your concept

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3. Prototype Testing: Find the Friction Before Development

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You've built a clickable prototype with multiple screens and interactions. It looks polished. But does it actually work for users?

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Step-by-step:

• Build a complete interactive prototype in Figma
• Ensure all frames and flows are complete in Figma before importing into Optimal.
• Copy your Figma prototype URL (works even with password-protected links)
• Paste it into an Optimal prototype test
• Define realistic tasks: "You want to buy running shoes under $100. Complete the purchase."
• Watch video recordings and analyze usability metrics, clickmaps, misclicks, successes/failures, and heatmaps

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What you'll discover might surprise you. Users will:

• Click on things you never intended to be clickable
• Miss obvious CTAs you thought were perfectly placed
• Get lost in navigation that seemed intuitive to your team
• Abandon tasks at friction points you didn't know existed

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4. AI Prototype Testing: Validate AI-Generated Designs

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The rise of AI design tools like Figma Make has changed the game. You can now generate a functional prototype from a text prompt in minutes. But just because AI can create it doesn't mean users can use it.

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Quick workflow:

• Generate a prototype using Figma Make
• Copy the URL and drop it into an Optimal live site test
• Add your testing tasks
• Review recordings to spot usability issues

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This is perfect for rapid experimentation. 

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Getting Started Is Simple

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1. Prep your Figma file - Have a prototype or design ready
2. Copy the link - Grab your Figma share URL
3. Create your test - Choose first-click, prototype test, or live site test in Optimal
4. Paste and configure - Add your Figma URL and write your test tasks
5. Launch - Use your own participants or tap into Optimal's panel or Managed Recruitment services
6. Analyze - Review results and iterate

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Launch Designs Users Love

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Figma gives you the power to design and prototype rapidly, while Optimal gives you the insights to make sure those designs actually work for real users. Together, they create a workflow built on real insights, not guesswork.

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By testing early and often, teams can reduce risk, build confidence in their designs, and move into development knowing their work has already been validated by users. Gather insights quickly, collaborate more effectively, and keep projects moving forward with evidence-backed decisions.

Ready to validate your next Figma prototype? Use Optimal as part of your workflow and start testing with real users today.

User interviews have always been one of the most trusted and powerful UX research methods. They give you something beyond dashboards or written surveys: real, in-depth conversations and context.

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But they’ve historically come with a cost – time, coordination, and a heavy lift to review recordings and turn videos into insights. Sometimes insights get buried. Recordings sit unused and research becomes challenging to revisit.

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In our recent webinar, we explored how that’s changing and how you can reduce the heavy lift of interview review, while building a research repository. 

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What is a research repository?

A research repository is a centralized system for storing, analyzing, and reusing research data, especially qualitative data like user interviews. It helps teams answer questions like what users said, what patterns emerged, and how past research can inform future decisions.
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For interviews, this means:

• Storing recordings and transcripts
• Organizing insights and themes
• Making research searchable
• Enabling teams to revisit past findings
• Supporting continuous discovery
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Optimal Interviews brings this to life by automatically capturing recordings, generating transcripts, structuring insights from the start, and making everything searchable so teams can easily revisit, build on, and continuously learn from their research.
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So what did we learn? Here are some key takeaways from this webinar, plus answers to the most common questions we heard.
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1. The biggest bottleneck isn’t conducting interviews. It’s everything surrounding it.

Running interviews isn’t just about talking to users. It’s everything before and after:

• Recruiting participants
• Coordinating calendars
• Managing reschedules and no-shows
• Setting up emails and reminders
• Transcribing, organizing, and synthesizing findings
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That overhead adds up quickly. There’s opportunity in automating these workflows and removing the friction around them. Optimal Interviews solves this by:
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• Creating a central calendar
• Emailing participants with confirmations and session reminders
• Automatically capturing recordings
• Generating transcripts
• Uploading and generating summaries and insights
• Structuring insights from the start
• Allowing you to explore instantly with AI Chat
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2. Speed matters more than ever (and it’s finally achievable)

Research isn’t slowing down. Product cycles are getting faster, and teams expect insights just as quickly.

What stood out most:

• Interviews can now go from recording → transcript → insights in minutes
• Teams can share highlight reels, clips and findings almost immediately
• Analysis can start while context is still fresh
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One team told us that a few years ago it took them three weeks to analyze user interviews for an initiative. When they replicated the same study in Optimal Interviews, they were able to generate usable insights in about five minutes.
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That shift from lagging insight to near real-time understanding is where the real impact lies.

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3. Scheduling should feel effortless

Interview scheduling sounds simple, but it’s often where things break down.
You can use Optimal Interviews to ensure:
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• Availability blocks with buffers
• Controlled rescheduling and cancellations
• Video conferencing integrations
• Support for collaborators
• Built-in, secure participant communication & messaging (coming soon)
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When done right, scheduling fades into the background so teams can focus on conversations, not coordination.
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4. AI is reshaping analysis but humans stay in control

AI is already proving its value in the analysis phase:

• Automatic transcription across multiple languages
• Theme and insight extraction across interviews
• Highlight reels and supporting evidence
• Natural language queries over your research
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But one point came through clearly: AI accelerates analysis but it doesn’t replace human judgment and sensitivity.
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Researchers still play a critical role in validating insights, interpreting nuance, and deciding what matters for the business. Think of AI as getting you to 80% faster, while you own the final 20%.

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5. The real unlock is continuous, reusable research

Here’s what you can achieve with Optimal Interviews:

• You can ask questions of past interviews using natural language
• Create new custom themes or topics on demand for AI to add new insights into
• Re-analyze old research with fresh context
• Add new interviews to your existing Optimal Interviews study and refresh the insights
• Identify gaps and spin up new studies faster
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This turns research from static storage into something dynamic, something you can continuously mine and build on.
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FAQs from the Webinar

Does the platform synthesize insights or just aggregate data?

Both. You can extract insights from individual interviews, but the real value often comes from patterns across multiple sessions. Aggregation helps surface stronger, more reliable themes, while still preserving standout moments from single participants.
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How is sensitive data handled?

Privacy is a core focus and consideration with Optimal Interviews. Some of the key protections include automatic redaction of personally identifiable information (PII) and enterprise-grade AI infrastructure with strict data isolation. We're also looking to expand Optimal Interviews anonymized scheduling and communication and manual redaction controls before analysis.
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What if I can’t connect my video conferencing tools?

Integrations are available for Google Meet, Microsoft Teams, and Zoom. 

You can still run everything without integrations:

• Set availability without integrations
• Add conferencing links yourself
• Manage sessions independently
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Integrations are helpful but not required.
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Can I search across multiple studies?

Today, teams often bring relevant interviews into one project for analysis. Looking ahead, the goal is broader. Optimal’s looking into how the platform can search and query across all research, use AI chat to explore insights across studies, and surface insights at a Workspace level.
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Can I query transcripts or AI summaries?

Yes. You can search transcripts directly and use AI-powered chat to explore themes, generate summaries, or even turn findings into shareable outputs like Slack posts or reports.

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Final thought

Interviews aren’t new. But the way we run them and what we can get out of them is changing fast.

By removing operational overhead and reducing time to insight, teams can talk to users more often, share insights faster, and build a research repository that becomes part of everyday product decision-making.
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If you want to experience the full walkthrough, demo, and Q&A from the session, we encourage you to watch the full webinar.
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👉 You can watch the full training webinar here.

Collaboration tools like Mural, FigJam, and Miro are staples of how modern teams can brainstorm, map ideas, align on plans, and build together. But a canvas alone can't tell you if you're on the right track or guide you to what comes next when progress stalls. That's where Optimal AI Chat and user insights come in.

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By starting or bringing real user insights into the boards your team already works in, you can reduce ambiguity, ground discussions in real research, and accelerate decision-making. 

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Here are 7 ways to use AI Chat alongside your collaboration boards.

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1. Align on key objectives

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Before your next planning session, use Optimal AI Chat to surface relevant insights from your interview recordings. Add a summary directly into your Mural, Miro, or FigJam board so everyone comes in with the same context and understanding of the objectives. Instead of starting with assumptions, your team can start with real user insights and clear trade-offs to discuss.
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Try this prompt: "Summarize the key considerations for [decision topic] and flag any trade-offs we should discuss as a team."
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2. Create a user journey map

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AI Chat can analyze interview transcripts and video recordings and highlight common jobs to be done, behaviors, and friction points. You can then map those steps visually on your board and identify where the experience breaks down.
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Try these prompts: “Summarize the typical jobs to be done for the people we interviewed.”
“For this job you identified [paste job details], detail the journey steps.” 

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3. Turn pain points into design and product decisions

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AI Chat can analyze recurring themes from your interview recordings and convert them into concrete opportunities your team can explore next. Adding these to your board gives the team a clear starting point rather than a vague list of problems.
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Try this prompt:  "Based on these pain points [paste notes or themes], suggest three product improvements we could explore."

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4. Sharpen your marketing messaging

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Interview insights aren’t just valuable for product, research, and design teams. Marketing teams can also use AI Chat to quickly evaluate messaging, positioning, and customer perception.

When running preference or concept testing interviews, AI Chat can quickly analyze the feedback and suggest positioning directions you can workshop on your board.
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Try this prompt: “Suggest positioning options based on the interview feedback.”

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5. Facilitate workshops

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Running workshops and brainstorming sessions with cross-functional teams can be challenging. Conversations drift, discussions stall, and teams sometimes struggle to focus on the most important issues. 
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AI Chat can help you structure the conversation before the workshop even begins by generating discussion guides based on user insights from your interviews. Add the chat outputs directly to your board to guide the session.
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Try this prompt: “Generate a structured discussion guide based on the pain points of the interviewees.”


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6. Make brainstorming more focused

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Open brainstorming can be valuable. It can also be chaotic without clear direction. By leveraging AI Chat, you can guide your brainstorming sessions with intelligent suggestions, topic generation, and idea organization.
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Try this prompt: “Generate 10 brainstorm ideas based on these user insights and group them into themes we could explore.”

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7. Map complex processes

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Visualizing complex processes and systems is easier with tools like Miro, FigJam, and Mural. AI Chat can help you map out each step. AI Chat can help break down a process step-by-step, highlighting decisions, dependencies, and potential friction points based on your interviews. Your team can then map these steps visually and identify opportunities for improvement.
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Try this prompt: “Create a step-by-step process map for how users complete [task], including key decisions and potential friction points.”

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Using Optimal AI Chat for seamless collaboration

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The best collaboration happens when teams have the right information at the right time. 

Optimal AI Chat gives your team a jumpstart for your interview analysis: clearer inputs, faster synthesis, and smarter outputs that translate directly into what you're building on your boards.

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Whether you're running a workshop, mapping a user journey, or planning a product launch, AI Chat helps you spend less time getting oriented and more time making decisions.
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Ready to see what your team can do with it?
Learn more about best practices for AI Chat or book a demo.