Lyssna for Designers: Quantitative User Research Made Simple

Lyssna is a user research platform built for designers who need quick, quantitative answers about their designs. It’s what teams reach for when they need to validate concepts, compare design options, or test whether users can complete a flow before committing to development.

Key Specs

How Designers Use Lyssna

Lyssna adapts to different stages of the design process. Here’s when designers turn to quantitative research.

For First Impressions

Upload a landing page design, set the test to show it for 5 seconds, then ask “What do you think this product does?” or “Who is this for?” Watch results roll in showing whether your message lands. Designers use this to catch confusing value propositions before launch. Unlike focus groups, participants respond to their immediate gut reaction, not what they think you want to hear.

For Design Validation

Upload two or three design options and ask participants “Which design would you choose?” You can ask follow-up questions about why they chose it. This works for comparing layouts, color schemes, CTAs, or entire page concepts. Get 50-100 responses in an hour instead of debating internally for a week. Use results to defend design decisions with data.

For Prototype Testing

Paste your Figma prototype link, add 3-5 tasks like “Find and add a blue jacket to your cart.” Lyssna tracks completion rates, time on task, and misclicks. Watch heatmaps showing where participants clicked. Use this to identify confusing navigation, unclear CTAs, or flows that don’t match user mental models before developers build it.

For Information Architecture

Test how users would organize your content before building navigation. Card sorting shows you which categories make sense to users, not just stakeholders. Tree testing validates your nav structure by showing where users expect to find specific content. Designers use these to fix findability problems that analytics can’t explain.

Lyssna vs. Alternatives

How does Lyssna compare to other user research platforms?

Choose Lyssna if: You want affordable quantitative research, need first-impression tests and preference testing, or don’t need video feedback.

Choose Maze if: You test Figma prototypes constantly, want advanced analytics (path analysis, rage clicks), or need AI-generated insights.

Choose UserTesting if: You need video feedback showing facial reactions and tone of voice, or require moderated interviews with participants.

Choose Useberry if: You’re on the tightest budget ($40/month) and only need basic prototype testing and preference tests.

Getting Started with Lyssna

A quick start to running your first test:

Step 1: Create your test

Click “New Test” and choose your test type. For your first test, try a preference test: upload two design variations (landing page mockups, logo options, color schemes). Add a question like “Which design would you choose?” and optionally “Why did you choose this design?” to capture reasoning.

Step 2: Set your audience

Choose “Panel” to use Lyssna’s 530K participants or “Share” to send to your own users. For panel tests, set demographic filters (location, age, gender) and add screening questions to narrow your audience. Start with 25-50 participants for preference tests, 10-20 for prototype tests. More participants give you confidence in results, but 25 is enough to spot clear patterns.

Step 3: Launch and analyze

Click “Launch” and wait. Panel tests typically fill within 1-3 hours depending on targeting. Watch results arrive in real time. Lyssna auto-generates charts showing preference percentages, completion rates, and heatmaps for prototype tests. Export results as PDF for stakeholder presentations or CSV for deeper analysis. Share links to live results dashboards with your team.

Lyssna in Your Design Workflow

Lyssna rarely stands alone. Here’s where it fits in relation to other design and research tools.

• Before Lyssna: Create designs in Figma, build prototypes, write research questions in Notion
• During testing: Lyssna for quantitative insights, analytics for behavioral data
• After Lyssna: Update designs in Figma, synthesize findings in Miro or Dovetail

Common tool pairings:

• Lyssna + Figma for testing prototypes before development, then iterating based on completion rates and heatmaps
• Lyssna + Dovetail for combining quantitative test results with qualitative user interviews
• Lyssna + Maze for running different test types (Lyssna for preference tests, Maze for prototype analytics)
• Lyssna + Google Analytics for following up quantitative data with qualitative evidence of why users behave certain ways

Common Problems (and How to Fix Them)

These issues come up regularly when teams use Lyssna.

“My test results are inconclusive”

This happens when design options are too similar or questions are too vague. For preference tests, make sure designs differ meaningfully (not just minor color tweaks). Ask specific questions: “Which design makes the product purpose clearer?” instead of “Which do you prefer?” For prototype tests, write tasks with clear success criteria: “Find and add a red jacket to your cart” not “Explore the site.”

“Participants aren’t completing my prototype test”

Your prototype likely has broken links or unclear navigation. Test your prototype yourself first by clicking every link. Make sure all task paths are actually clickable in your Figma prototype. If completion rates are under 50%, the flow is too confusing or tasks are too hard. Simplify the task or fix the prototype before gathering more data.

“I can’t find my target users in the panel”

Lyssna’s 530K panel is smaller than Maze (3M+) or UserTesting (millions). If you need niche audiences (developers using specific frameworks, healthcare professionals), you’ll struggle. Use the “Share” option to recruit from your own user base, LinkedIn, Reddit communities, or Twitter. Panel testing is best for general consumer demographics, not specialized roles.

“Results don’t match what I expected”

Good. That’s why you test. Designers often fall in love with their own work and miss obvious problems. If 70% of users prefer Option B when you were sure Option A was better, trust the data. Run a follow-up test asking “Why did you choose this option?” to understand the reasoning. Sometimes your hypothesis was wrong, and that’s valuable information.

“Tests are expensive with panel participants”

Panel costs add up at $1-5 per response. To reduce costs, test with smaller samples (25 participants instead of 100), use your own participants when possible (free), or run tests sequentially (test Option A vs B first, then winner vs Option C) instead of testing everything at once. Reserve panel testing for critical decisions, use your own users for iteration.