Quantitative Survey 📋 Prompts

SUMMARY

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.