Traditional user testing relies on small, overused panels and hard-to-use tools. TheySaid's AI moderator lets you recruit anyone, guides them live (voice optional), probes for "why," and auto-analyzes every session into decisions.
Why Traditional User Testing Falls Short
Traditional user testing creates systemic problems that corrupt research quality and delay product decisions, forcing teams to make choices based on flawed insights.
1. Panel Bias and Pro-Tester Effect
A handful of "professional testers" dominate results across multiple studies, developing testing habits that don't reflect real buying behavior. These repeat participants create unreliable signals for purchase intent and user preferences. You end up with insights from people who test products for a living rather than people who actually buy and use them daily.
2. Hard to Source Niche Participants
Business owners, doctors, lawyers, and enterprise buyers rarely appear in standard panel databases. When you need feedback from specific industries or roles, traditional systems force teams to accept whoever shows up. This compromises insight quality exactly when specialized expertise matters most for product decisions.
3. Clunky Setup and Weak Feedback
Screen recorders combined with "think aloud" instructions create setup barriers that frustrate participants and researchers alike. First-time users freeze when asked to narrate their thoughts while completing unfamiliar tasks. Technical problems consume valuable session time that should focus on gathering insights instead of troubleshooting software.
These fundamental limitations explain why many product teams struggle to trust their user research and why competitive advantages slip away while waiting for reliable feedback.
Meet TheySaid AI User Testing (What It Is)
TheySaid solves traditional user testing problems through intelligent automation that makes research faster, easier, and more insightful.
AI as your live moderator transforms the experience. The system guides tasks step-by-step, keeps participants talking naturally, and adapts to what's happening on screen. No scheduling human moderators or managing complex logistics.
Frictionless participation removes barriers. Users join from a link, QR code, email, social media, or website popup. They share screen and mic in-browser without downloads, account creation, or technical setup headaches.
Real-time depth uncovers what surveys miss. AI follows up with probing questions to uncover motivations, confusion points, and intent. When users hesitate, rage-click, or show frustration, the system immediately explores why.
Why This Changes the Game (Core Advantages)
Traditional user testing versus AI user testing shows stark differences in speed, quality, and cost:
Recruit anyone, anywhere breaks panel limitations. Test with your actual customers and prospects instead of a tiny group of professional testers. Geographic and demographic diversity improve insight quality and business relevance.
Voice that nudges depth creates natural conversation. AI can speak out loud and participants can talk back. This natural, human interaction feels comfortable while generating richer feedback than typed responses or silent screen recordings.
Automatic "why" discovery scales qualitative research. Dynamic probing kicks in when users hesitate, rage-click, or bounce from pages. You capture the emotional and logical reasoning behind user behavior without manual intervention.
From raw footage to decisions happens instantly. AI flags key moments, summarizes patterns across sessions, and recommends next actions. Skip weeks of manual analysis and transcription work that delays product decisions.
How It Works: Set Up in Minutes
The entire process from research idea to actionable insights happens faster than traditional methods take just to recruit participants.
- Describe your goal and tell AI what you want to learn. Get suggested tasks and questions tied to decisions like "Ship v1," "Change checkout," or "Kill feature X." The system understands business context and creates relevant research frameworks.
- Tweak the flow by editing tasks and adding success criteria. Set branching logic, add must-ask probes, and customize the experience for your specific user types and research objectives.
- Publish and recruit via multiple channels. Distribute through shareable links, social media, email campaigns, website popups, or QR codes. Reach your actual users wherever they spend time.
- Participants join through in-browser screen and mic sharing with no installs required. AI welcomes users and explains the test clearly, reducing anxiety and technical friction.
- Guided session begins with AI moderating task-by-task. The system asks follow-ups, timestamps issues, and captures all user actions while maintaining natural conversation flow.
- Auto-analysis generates highlight reels instantly. See "moments that matter," task success and failure points, time-on-task measurements, hesitations, and drop-offs without manual review.
- Roll-up insights emerge as cross-session themes develop. Patterns by segment, outliers, and confidence notes help prioritize which findings deserve immediate attention.
- Action recommendations provide prioritized fixes ready for implementation. Get test-again suggestions and experiment ideas that connect directly to product roadmap decisions.
What You Can Test (High-Impact Use Cases)
AI user testing works across every stage of product development, from early concepts to post-launch optimization.
1. User Needs Discovery
Before investing in development, validate early problem and solution fit with real users. Upload wireframes, concepts, or value proposition statements to test whether users actually grasp your core benefits. Test messaging clarity, feature prioritization, and market positioning with people who match your ideal customer profile. Discover the gap between what you think users need and what they actually struggle with daily.
2. Pre-Build Feature Tests
Turn Figma prototypes and mockups into validation opportunities before engineering begins. Navigation flows, button placement, information architecture, and interaction patterns all need real user input. Why? Because catching usability issues at this stage costs hours instead of weeks later. You'll quickly learn whether new features solve genuine problems or just add complexity users don't want.
3. Why A/B Lost Analysis
When split tests show variant B underperforming, numbers tell you what happened—not why. AI user testing fills that gap by revealing the specific reasons users preferred the control experience. Emotional reactions, trust factors, and cognitive load differences become clear through voice feedback and behavioral observation. Failed experiments transform into learning opportunities for smarter optimization.
4. Checkout and Funnel Friction
Cart abandonment data shows where users leave, but not why they get stuck. Watch people encounter barriers during critical conversion moments while hearing their actual thought process. Payment flows, shipping options, form fields, trust signals, and error messaging all impact buying decisions. Real-time user feedback reveals friction points that analytics miss completely.
5. Competitor Teardowns
Understanding your competitive position requires seeing through users' eyes, not just feature comparisons. Have participants complete identical tasks in your product and competitors' tools to capture authentic switching considerations. You'll identify competitor advantages you missed, validate your unique value propositions, and spot market opportunities others ignore.
Data You Get Back (Deliverables and Metrics)
TheySaid’s AI user testing transforms raw session footage into structured insights and actionable recommendations that drive immediate product decisions.
Per-session package includes video with clickable timeline of key moments, transcript with sentiment analysis, annotated events showing user actions, task outcomes with success measurements, and AI Q&A summaries.
Study-level dashboard aggregates task success rates, time-on-task measurements, confusion points by frequency, sentiment shifts throughout sessions, and completion rates by segment.
Cross-study learning identifies themes that persist across releases, provides regression alerts when changes hurt user experience, and builds institutional knowledge about user preferences.
Executive summary delivers one-page brief covering what changed, what we learned, confidence level in findings, and next actions ready for implementation.
Recruiting Without Panels (Distribution Playbook)
Skip traditional panel limitations by reaching users through channels they already trust and frequent. This approach delivers higher response rates and more authentic feedback than generic recruitment methods.
Owned channels tap into existing user relationships where engagement runs deepest. In-app prompts target specific user behaviors like feature confusion or checkout hesitation. Lifecycle emails trigger automatically based on usage patterns, catching users when feedback motivation peaks. Community posts gather insights from your most engaged customers, while help center integration captures support context that reveals real pain points. Product update announcements naturally include research requests that feel relevant rather than intrusive.
On-site intercepts capture visitors exactly when decision-making happens. Exit intent popups on checkout pages catch abandonment in real-time. Repeat visits to pricing information signal buying consideration that makes feedback timing perfect. Time-based triggers identify engaged visitors who've spent enough time to form genuine opinions about your product experience.
Offline triggers bridge digital and physical touchpoints for comprehensive feedback collection. QR codes at events connect face-to-face interactions with digital research. Product packaging creates feedback opportunities when customers experience your solution in context. Print materials in retail locations capture reactions when motivation and memory remain fresh from actual usage.
B2B targeting leverages existing customer relationships for precise participant selection. Send research invitations directly to segmented lists in CRM systems like HubSpot or Salesforce. Auto-invite users matching specific criteria such as "Finance admins at customers over 500 employees" to gather role-specific insights. Company characteristics enable research segmentation that reveals how business size, industry, or maturity affects user experience preferences.
Example Study Blueprints (Copy-and-Tweak)
These proven templates help you launch AI user testing projects quickly with frameworks that generate actionable insights:
ROI and Proof Points (What to Measure)
Smart teams measure AI user testing success through four key metrics that demonstrate clear business impact beyond just faster research.
Time saved versus manual studies compounds across setup, moderation, and analysis phases. While traditional user testing requires weeks for recruiting, scheduling, conducting, and analyzing sessions, AI user testing delivers comparable insights in days. This time compression lets product teams complete multiple research cycles within a single traditional study timeline.
Decision latency tracks how quickly teams move from initial idea to validated decision. Faster feedback cycles reduce time to market and increase iteration velocity before competitors respond to changing user needs. Teams using AI user testing often complete validation sprints in the time traditional methods take just for participant recruitment.
Fix rate and impact measures shipped product changes tied directly to test insights. Higher confidence in user feedback leads to more decisive product improvements and smarter resource allocation. When teams trust their research quality, they implement changes faster and with greater conviction.
Conversion and UX lifts capture the business results that matter most. Checkout completion rates, user activation percentages, and feature adoption levels typically improve after implementing AI user testing insights. These metrics prove research investment delivers measurable returns beyond just faster processes.
Frequently Asked Questions
How does AI moderation compare to human researchers?
AI moderators provide consistent questioning across all sessions and scale to handle multiple participants simultaneously. They capture emotional tone through voice analysis while maintaining neutral interactions that encourage honest feedback.
What technical setup do participants need?
Participants only need a web browser and internet connection. No software downloads or account creation required. The system works on phones, tablets, laptops, and desktop computers.
Can this work with our existing design tools?
Yes, any interactive content accessible through a web browser works effectively. This includes Figma prototypes, live websites, staging environments, and mobile web apps.
How quickly do we get actionable insights?
Initial insights appear during live sessions through real-time flagging. Comprehensive analysis with themes, sentiment scores, and recommendations typically completes within hours after sessions end.
Do users provide honest feedback when talking to AI?
Most participants find AI moderation less intimidating than human observation. They express candid opinions without worrying about offending researchers and often share more honest feedback about frustrations.
What sample sizes provide reliable results?
AI user testing identifies major usability issues with 5-8 participants for similar user groups. Complex products serving diverse audiences benefit from 12-15 participants across different segments.
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