How would you feel if you could no longer use this product?
L
Product-Market Fit Survey
Evaluator agent · 1.2K uses
Evaluator Agent
Product Testing Survey Template
Product forms miss why users care. Stop losing valuable product insights to generic feedback forms. This template intelligently explores user workflows, feature adoption blockers, and improvement suggestions based on actual usage data and user roles.
Real motivations
Feature priorities
Confident decisions
Used 945+ times
What's inside this template
Start from this conversation and adapt it to your team — change any question, add your own logic, and connect the tools you already use.
Questions it always asks
The core fields every response captures.
Which core features do you use most frequently?
What's the biggest obstacle preventing you from achieving your goals?
How it adapts
Follow-ups that change based on what people say.
If user reports low feature adoption, ask about onboarding experience
If user mentions competitor comparison, explore specific feature gaps
Where it routes people
Different paths for different answers.
Route power users to advanced feature feedback team
Send bug reports with reproduction steps to engineering queue
Automations it can trigger
Actions that fire the moment a response comes in.
Create Jira tickets for reported bugs with user context
Add feature requests to product backlog with priority scores
Send satisfaction scores to analytics dashboard with user segments
SOC 2 Type II and ISO 27001:2022 certified. Responses are encrypted in transit and at rest, and you own your data. View our Trust Center.
How this AI template works
The AI starts by understanding the user's role and product usage patterns, then guides them through targeted questions about specific features they've used. It automatically probes deeper into pain points and follows up on feature requests with context-gathering questions.
Getting started
- 1
Define your product areas and features to test
- 2
Set user segmentation rules based on usage data
- 3
Configure follow-up triggers for specific feedback types
- 4
Connect feedback pipeline to your product management tools
Template Details
- Agent Type
- Evaluator
- Category
- Product & research
- Industry
- SaaS / Tech
- Business outcome
- Build the right product
- Job to be done
- Collect customer feedbackTest concepts & creative
- Journey stage
- Feedback & evaluation
- Replaces
- Survey tools
- Integrations
- Slack, Notion, Webhook
- Times Used
- 945+
Forms collect fields. Conversations capture context.
Static forms force complex situations into rigid dropdowns. Perspective captures structured data and the reasoning behind it — so your team makes better decisions, faster.
The static form
yoursite.com/intake
Category *
Select...
Details
Describe your situation...
Submit
Result:Category: "Other"|Details: "It's complicated"
No context. No follow-up. No next step.
- Static product testing forms force users to rate features without understanding their actual workflow context. Product managers get numerical scores but miss the reasoning behind user preferences, making it impossible to prioritize development efforts effectively.
- Fixed checkbox questions can't explore why users get excited about certain concepts or concerned about others. Teams lose critical insights about adoption barriers, competitive advantages, and unexpected use cases that could reshape product strategy.
- Multiple choice testing creates false constraints that don't reflect real user decision-making. Users abandon lengthy feature evaluation forms, leaving product teams with incomplete data about market demand and willingness to pay for new capabilities.
The AI conversation
"Tell me more about the timeline — when did this start, and is there a deadline your team is working against?"
Extracted & structured automatically
Category
High-priority
Urgency
Deadline: 2 weeks
Sentiment
Frustrated but hopeful
Next step
Route to senior team
Triggered: Slack alert sent| CRM updated
Right team. Full context. Instant action.
- AI conversations explore user workflows before presenting concepts, revealing how new features would fit their actual business processes. Product teams understand not just what users want, but exactly how they would adopt and integrate new capabilities into existing systems.
- Adaptive questioning probes deeper when users express strong reactions to specific features or pricing models. Product managers discover the emotional and rational drivers behind preferences, plus competitive alternatives users actually consider during evaluation.
- Conversational testing encourages users to share detailed thoughts about prototypes and positioning without the fatigue of rating dozens of features. Teams collect richer qualitative insights from each participant for more confident go-to-market decisions.
What should product concept testing conversations include?
Start by understanding current user workflows and pain points before presenting your concept. Ask about their existing solutions, decision-making process, and recent frustrations with current tools. Then present your concept and explore natural reactions, concerns, and excitement. Include questions about feature prioritization, implementation timeline, and budget authority. Probe competitive alternatives and positioning by asking how they would explain your concept to colleagues. Focus on validating core assumptions about problem severity, solution fit, and adoption likelihood rather than collecting feature ratings.
FAQ
Frequently Asked Questions
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Replace drop-off, poor qualification, and missing context with AI conversations that capture structured data and real understanding. Set up in minutes.
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