Q01
Message
All templates
Product Feature Discoverability Assessment
Measures how users locate, recognize, and evaluate in-product features. Designed for product and UX research teams to diagnose navigation friction, benchmark discoverability, and prioritize information-architecture improvements.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
21 questions · ~10 min
Q02
Multiple Choice
In the last 30 days, approximately how often did you use this product?
- Daily
- 3–6 times per week
- 1–2 times per week
- Less than weekly
Q03
Long Text
Please name one feature you recently used or tried to find in this product (within the past 14 days).
Q04
Multiple Choice
Thinking about the feature you named, how did you first find (or try to find) it?
- Browsing the navigation or menus
- Home or dashboard area
- Button or icon near the related task
- Search within the product
- Onboarding checklist or tutorial
- In-app tip, coach mark, or walkthrough
- Notification or What's New panel
- Help center or documentation
- A colleague or friend told me
- External website, blog, or video
- I had used it before and remembered where it was
- I couldn't find it at first
- Other
Q05
Ranking
Rank where you would most expect to find this type of feature (most to least likely).
- Top navigation bar
- Profile or account settings
- Contextual menu near the related action
- Search results page
- Home or dashboard shortcuts
Drag to rank
Q06
Long Text
In your own words, what helped or hindered you when finding that feature?
Q07
Multiple Choice
Which best describes your primary role when using this product?
- Individual contributor
- Manager or team lead
- Executive or owner
- Student or educator
- Personal use
- Other
- Prefer not to say
Q08
Message
Thank you for your insights — your feedback will directly help us improve feature discoverability. You're all set!
Q09
Multiple Choice
Which of the following devices do you use for this product? Select all that apply.
- Desktop or laptop
- Mobile phone
- Tablet
- Other
Q10
Long Text
If you used in-product search, what keyword(s) did you try?
Q11
Multiple Choice
Which of the following discovery aids have you noticed or used in this product? Select all that apply.
- Onboarding tour or checklist
- Tooltips or coach marks
- In-app search
- What's New or changelog panel
- Help center or knowledge base
- Contextual prompts or suggestions
- None of these
- Not sure
Q12
Long Text
What is one change that would make this feature easier to find next time?
Q13
Multiple Choice
How long have you been using this product?
- Under 3 months
- 3–12 months
- 1–2 years
- 3+ years
- Not sure
- Prefer not to say
Q14
Opinion Scale
Overall, how easy or difficult was it to find that feature?
Scale: 1 – 7
Min:Very difficultMax:Very easy
Q15
Multiple Choice
If you had difficulty finding the feature, where did the process stall most?
- Couldn't guess the feature name
- Unsure which menu or section to check
- Too many similar-sounding options
- Search results were irrelevant
- Feature hidden behind upgrade or paywall
- Needed permissions or access I didn't have
- Connection or setup issue blocked me
- I stopped trying
- Not applicable — I found it without difficulty
Q16
AI Interview
Based on your responses, we'd like to explore your feature-discovery experience a bit further.
Q17
Multiple Choice
Which region are you in?
- North America
- Latin America
- Europe
- Middle East or Africa
- Asia
- Oceania
- Prefer not to say
Q18
Opinion Scale
Once you located the feature, how clear was its name or label?
Scale: 1 – 7
Min:Not at all clearMax:Extremely clear
Q19
Long Text
Based on your responses throughout this survey, is there anything else you'd like to share about finding or discovering features in this product?
Q20
Multiple Choice
What is your age range?
- Under 18
- 18–24
- 25–34
- 35–44
- 45–54
- 55–64
- 65+
- Prefer not to say
Q21
Multiple Choice
Do you use any assistive technologies (e.g., screen reader, voice control) when using digital products?
- Yes
- No
- Prefer not to say
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- AI follow-ups automatically explore the 'why' behind concept reactions, replacing shallow rating-only templates
- Academic-grade methodology with proper scale construction—no leading questions or attention checks that bias results
- Full reproducibility: every AI prompt, model parameter, and logic branch is logged and visible for replication studies
SurveyMonkey
Product Testing Survey TemplateA well-established template used over 57,000 times, focused on gathering feedback on product concepts, messaging, and pricing. Expert-certified and customizable, but fundamentally a static questionnaire with no adaptive follow-up capabilities.
What it does well
- Used 57,000+ times with high social proof and trust
- Expert-certified questions covering credibility, innovation, value, and quality dimensions
- Supports AI-generated word clouds and basic sentiment analysis on results
Where it falls short
- No AI-powered follow-up questions to explore why respondents rate concepts the way they do
- AI features limited to post-hoc analysis (word clouds, segments)—not real-time interview adaptation
- Black-box AI insights with no transparency into models or prompts used
- Expensive Audience panel upsell for reaching target respondents
Qualtrics
Product Research Survey TemplateA Ph.D.-designed concept testing template with sophisticated methodology including monadic and sequential monadic designs. Enterprise-grade but expensive and complex, with no AI interview follow-ups during data collection.
What it does well
- Ph.D.-designed methodology with monadic and sequential monadic testing options
- Advanced analytics including conjoint analysis and Stats iQ for deeper data exploration
- Pre-built dashboards and automatic reporting for concept comparison
Where it falls short
- Enterprise pricing makes it inaccessible for academic researchers and small teams
- No AI-driven conversational follow-ups during the survey itself
- High learning curve due to feature complexity
- No transparency into how AI-powered analytics arrive at their conclusions
Typeform
New Product Survey TemplateA visually appealing, conversational-style template that asks one question at a time. Great UX for respondents but limited to static branching logic—no AI that adapts questions based on prior answers in real time.
What it does well
- Elegant one-question-at-a-time design that boosts completion rates
- Highly customizable branding with 300+ integrations
- Logic Jumps allow basic conditional branching
Where it falls short
- No AI follow-up capability—branching is pre-defined, not adaptive
- Limited analytics; designed more for data collection than deep research analysis
- Advanced logic jumps restricted to paid plans
- No built-in methodology guidance for proper concept testing scale construction
Jotform
New Product Survey Form TemplateA free, drag-and-drop template focused on collecting basic customer feedback before product release. Easy to set up but methodology is generic and lacks any AI or adaptive research capabilities.
What it does well
- Completely free to use with drag-and-drop customization
- 100+ third-party integrations including Google Sheets and CRM systems
- QR code sharing and multi-device support
Where it falls short
- No AI-powered follow-up questions or conversational capabilities
- Generic question design with no survey methodology guidance
- No concept testing methodology (monadic/sequential) built in
- Basic analytics with no advanced statistical tools
SurveySparrow
Product Feedback Survey TemplateA conversational-style product feedback template with multi-language support and in-app survey embedding. Focuses on general product feedback rather than structured concept evaluation methodology.
What it does well
- Conversational UI with 130+ language support via Google Translate
- Skip/display logic for personalized survey paths
- In-app and offline survey collection capabilities
Where it falls short
- No AI-driven adaptive follow-up during the survey experience
- General product feedback focus—not purpose-built for concept evaluation research
- AI features limited to survey creation assistance, not real-time interview probing
- No transparency into AI model or prompt decisions
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.