Quantitative Survey 📋 Prompts

Purpose: Quantitative Surveys are structured research tools used to collect measurable user data at scale, enabling statistically sound UX insights.

Design Thinking Phase: Empathise

Time: 1–2 hours design + 1–2 weeks deployment + 3–5 hours analysis

Difficulty: ⭐⭐

When to use:When validating early hypotheses across large cohortsWhen prioritising features based on measurable demandWhen tracking longitudinal changes in user sentiment or behaviour

What it is

A Quantitative Survey is a standardised questionnaire used to gather numerical data from a statistically significant user pool. It’s a method that captures user behaviours, preferences, and attitudes via closed-ended questions (e.g. Likert scales, multiple choice) to identify design patterns, measure usability, and validate research hypotheses.

📺 Video by NNgroup. Embedded for educational reference.

Why it matters

Quantitative surveys enable product teams to make evidence-based decisions quickly, especially when scaling. They're essential in triangulating findings from qualitative research, operationalising KPIs (like NPS or SUS), and giving a longitudinal or comparative lens to user needs. Executed well, they reduce risk and add strategic direction to roadmap priorities.

When to use

• When you need statistically significant data from diverse users
• When testing assumptions or patterns before prototyping
• When measuring changes in perception or satisfaction over time

Benefits

• Rich Data at Scale: Enables confident decision-making across broad user bases.
• Benchmarking: Useful for tracking usability changes across releases.
• Efficient Validation: Quickly tests hypotheses before design investments.

How to use it

• Define Objective: What user behaviour, attitude, or performance metric are you measuring?
• Choose Question Types: Use scales (Likert, semantic differential), rankings, or binary formats to collect structured data.
• Recruit Participants: Aim for segment diversity (min n=30–50 per key variable).
• Deploy via Tools: Use trusted survey platforms like Typeform, Qualtrics, or Maze.
• Analyse: Use statistical tools or AI to find correlations, clusters, or trends. Visualise results to guide design recommendations.

Example Output

Fictional Study: Feature Preferences for a Finance App

• Sample Size: 212 users, aged 25–45
• Top 3 Most Requested Features (% agreement):
  • Auto-budgeting suggestions (72%)
  • Bank account sync (65%)
  • Spending limits per category (61%)
• User Satisfaction Score (1–7 Likert): Avg. 6.1 (pre-launch baseline: 4.8)
• Feature Adoption Intent: 45% “Extremely Likely” to use goal tracking feature

Common Pitfalls

• Biased Wording: Leading or confusing questions will skew data. Keep language neutral and test your survey internally first.
• Too Many Questions: Long surveys reduce completion rates. Keep them concise (10–15 min max).
• Misinterpreted Correlation: Just because two variables trend together doesn’t mean one causes the other. Supplement analysis with qualitative follow-ups.

10 Design-Ready AI Prompts for Quantitative Survey – UX/UI Edition

How These Prompts Work (C.S.I.R. Framework)

Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.

C.S.I.R. stands for:

• Context: Who you are and the UX situation you're working in
• Specific Info: Key design inputs, tasks, or constraints the AI should consider
• Intent: What you want the AI to help you achieve
• Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)

Level up your career with smarter AI prompts.Get templates used by UX leaders — no guesswork, just results.Design faster, research smarter, and ship with confidence.First one’s free. Unlock all 10 by becoming a member.

Prompt Template 1: “Draft Survey Questions for Feature Validation”

Draft Survey Questions for Feature Validation

Context: You are a Senior UX Designer planning a quant survey for a mobile productivity app.  
Specific Info: You're testing appeal around 3 proposed features: calendar integrations, task suggestions, and time blocking.  
Intent: Generate effective, unbiased questions that measure user interest, usage intent, and current alternatives.  
Response Format: Provide a list of 8–10 questions, grouped by topic. Include Likert and multiple-choice types.

Ask clarifying questions if platform context (iOS/Android/web) would influence question design.  
Then suggest one follow-up qualitative method for deeper insights.

Prompt Template 2: “Evaluate Survey Draft for Usability Issues”

Evaluate Survey Draft for Usability Issues

Context: You are a UX Researcher reviewing a first-draft quant survey to be launched within an e-commerce platform.  
Specific Info: The survey has 15 questions targeting post-purchase satisfaction and cross-sell intent.  
Intent: Identify wording bias, cognitive overload, or irrelevant sequencing that might harm data quality.  
Response Format: Provide a question-by-question review with critique and improvement suggestions.

Ask if the survey tool has mobile constraints that affect usability.  
Suggest a follow-up session to A/B test revised versions.

Prompt Template 3: “Cluster User Segments Based on Survey Data”

Cluster User Segments Based on Survey Data

Context: You are leading UX strategy for a redesign of a health tracking app.  
Specific Info: You’ve collected 800 responses across 20 data points, including age, goal type, tracking frequency, and feature use.  
Intent: Detect natural user groups for persona refinement or targeted design.  
Response Format: Return a description of 3–5 user clusters with tagged attributes and behaviour markers. Suggest how each cluster might influence feature prioritisation.

Ask for clarification on which segments matter most to product strategy.  
Recommend visual formats for presenting these personas to the dev team.

Prompt Template 4: “Generate Benchmark KPIs from Survey Questions”

Generate Benchmark KPIs from Survey Questions

Context: You are a Principal UX Researcher preparing a benchmark study across multiple product areas.  
Specific Info: You’ve finalised 12 quant questions including NPS, task success confidence, and perceived value.  
Intent: Convert these into measurable product KPIs for future team sprints.  
Response Format: Provide a table with KPI Name, Source Question, Measurement Scale, and Suggested Baseline Goal.

Ask if historical benchmarks exist or if this is the first quant tracking study.  
Optionally, propose how to visualise KPI trends in future reports.

Prompt Template 5: “Detect Rating Bias in Quant Survey Results”

Detect Rating Bias in Quant Survey Results

Context: You are analysing a post-launch satisfaction survey from a streaming platform.  
Specific Info: 90% of responses skew positively across all dimensions, but in-app retention has declined.  
Intent: Identify whether your survey design introduced response bias or social desirability effects.  
Response Format: Provide a diagnostic checklist + areas to rework or retest in the next survey.

Ask for access to actual phrasing if possible — or suggest best practices for neutrality.  
Offer ideas for triangulating sentiment beyond surveys.

Prompt Template 6: “Visualise Survey Results for Stakeholder Deck”

Visualise Survey Results for Stakeholder Deck

Context: You are a Design Lead preparing a quarterly showcase for senior stakeholders.  
Specific Info: You have data from a quant survey on onboarding flow satisfaction (300 participants, 18 questions).  
Intent: Turn results into visually compelling, insight-rich slides.  
Response Format: Provide slide outline including headline, visual type (chart/table/diagram), and insight comment.

Ask about preferred tools (e.g. Figma, Google Slides) or visual conventions.  
Suggest assets designers could standardise across quarters.

Prompt Template 7: “Detect Language Gaps in Multilingual Surveys”

Detect Language Gaps in Multilingual Surveys

Context: You are running a localisation audit for survey research in LATAM and Southeast Asia.  
Specific Info: Survey translations are complete, but respondents flagged confusion.  
Intent: Find linguistic or cultural misalignments impacting validity in multi-market research.  
Response Format: Return a table: Question, Original Meaning, Translation Concern, Suggested Fix.

Ask if regional language nuances (e.g., formal vs. informal tone) matter based on brand context.  
Propose language proofing workflows with native speakers.

Prompt Template 8: “Create Follow-Up Interview Guide Based on Survey”

Create Follow-Up Interview Guide Based on Survey

Context: You are a Product Designer planning qualitative research based on quant survey findings.  
Specific Info: Survey showed low adoption of the bookmarking feature despite high satisfaction.  
Intent: Explore reasons behind low usage and identify feature improvements.  
Response Format: Provide a semi-structured 30-min interview guide with themes, sample questions, and sequencing.

Ask for user device context or demographic profiles.  
Suggest how to code responses back to survey hypotheses.

Prompt Template 9: “Compare Two Survey Waves Statistically”

Compare Two Survey Waves Statistically

Context: You’re reviewing wave 1 (pre-launch) and wave 2 (post-launch) quant results for a B2B SaaS dashboard.  
Specific Info: Both samples are >200 respondents, but differ slightly in role and region.  
Intent: Assess whether key usability ratings or efficiency perception have statistically significant change.  
Response Format: Provide test recommendation (e.g. t-test), assumptions, and summary of significance outcome.

Ask which variables have matching constructs across waves.  
Recommend visual comparison formats where applicable.

Prompt Template 10: “Generate Closed-Ended Question Variants”

Generate Closed-Ended Question Variants

Context: You are iterating on survey design for a Gen Z-focused social app.  
Specific Info: You’re measuring peer influence on joining or leaving groups.  
Intent: Create multiple question variants (Likert scale, binary, scenario-based) to test optimal data expression.  
Response Format: List 5–7 variations with pros/cons for each.

Ask whether behavioural terminology or slang should be used based on audience.  
Suggest how to run a short pretest to compare versions.

Recommended Tools

• Typeform – clean UI for user-friendly surveys
• Maze – quant+qual research for product teams
• SurveyMonkey – detailed logic and branching tools
• Qualtrics – best-in-class for advanced statistical analysis
• Google Forms – simple and quick for internal pilots

Learn More

• NNGroup: UX Research & Design Articles
• Interaction Design Foundation – Topics
• Google Design Sprint Kit