SUMMARY
Purpose: Post-test surveys (quantitative testing) are used to collect measurable feedback from users immediately after usability testing sessions to evaluate satisfaction, task ease, and perceived success.
Design Thinking Phase: Test
Time: 45–60 min session + 1–2 hours analysis
Difficulty: ⭐⭐
When to use: When you need statistically comparable data across multiple test sessions When you want to validate a prototype’s usability with scale-based evidence When stakeholders require quantitative proof to support UX recommendations
What it is
Post-test surveys are structured questionnaires given to users after a usability test to quantitatively measure key usability indicators like perceived task difficulty, satisfaction, confidence, and likelihood to reuse. They often include Likert scales, System Usability Scale (SUS), or custom metrics tailored to your product or goals.
📺 Video by NNgroup. Embedded for educational reference.
Why it matters
Post-test surveys complement observational usability testing data with statistically comparable scores. They're especially useful when performance metrics are ambiguous and you need insight straight from the user’s perspective.
This quantitative layer helps UX teams justify design trade-offs, prioritise fixes based on user sentiment trends, and track improvements over time across design iterations.
When to use
- After moderated or unmoderated usability testing sessions
- When tracking usability improvements across design iterations
- When assessing satisfaction or confidence in beta features or MVPs
Benefits
- Rich Insights: Helps uncover user sentiment that complements behavioural observation.
- Flexibility: Customisable to any product, task, or test flow.
- User Empathy: Quantifies perceptions that highlight user pain points or confidence gaps.
How to use it
- Define research goals: What do you need to assess — satisfaction, confidence, task clarity?
- Choose your survey type: Use standard scales (e.g., SUS) or tailor metric questions specific to your feature or flow.
- Keep it short and focused: Aim for 5–10 questions using consistent scale types (e.g., 1 to 5 or 1 to 7 Likert).
- Run the survey immediately after the usability test when the context is fresh.
- Visualise and analyse patterns across testers to identify outliers or consistent friction points.
Example Output
Here’s a fictional example of a post-test survey result summary:
- Question: “How easy was this task to complete?” (1 = Very Difficult, 7 = Very Easy)
Average score: 5.1 - Question: “How confident are you that you completed this task correctly?” (1 = Not Confident, 7 = Very Confident)
Average score: 3.8 - Question: “Would you use this feature in your real workflow?” (1 = Not Likely, 7 = Very Likely)
Average score: 6.3
Common Pitfalls
- Asking too many questions: Users are fatigued post-session. Keep it concise to maintain data quality.
- Vague scales: Use clear wording and label both ends of the scale to avoid interpretation bias.
- Mismatched timing: Run surveys immediately after the session to capture fresh impressions; delays skew perception.
10 Design-Ready AI Prompts for Post-Test Surveys – 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: “Design a Quant UX Survey for a Mobile Checkout Flow”
Design a Quant UX Survey for a Mobile Checkout Flow
Context: You are a Senior UX Researcher testing a redesigned mobile checkout experience for a retail e-commerce app.
Specific Info: The test observed 6 participants completing a 4-step checkout process. You noticed varied behaviours but need post-test survey data to interpret perceived task difficulty.
Intent: Create a concise, 6-question post-test survey using Likert scales to measure confidence, friction, and satisfaction.
Response Format: Output final survey questions and recommended Likert scale format.
If checkout complexity or platform constraints aren’t clear, ask follow-up questions.
Then, suggest how to visualise the collected data for stakeholders.
Prompt Template 2: “Refine a Post-Test Survey for a Multilingual User Base”
Refine a Post-Test Survey for a Multilingual User Base
Context: You are a UX researcher finalising a survey for usability-tested flows in 3 languages.
Specific Info: Survey includes 10 Likert questions; prior tests showed inconsistent interpretation across languages.
Intent: Optimise wording and structure for simplicity and cross-language reliability.
Response Format: Return revised survey language (English) with short rationale for each phrasing change.
Ask if translation resources or cultural phrasing norms are available before finalising.
Then, suggest a method to validate the revised version across language variants.
Prompt Template 3: “Generate Post-Test Survey Metrics for an MVP Help Feature”
Generate Post-Test Survey Metrics for an MVP Help Feature
Context: You are testing a newly added help drawer in a SaaS dashboard MVP.
Specific Info: Test involved 8 participants using the drawer to complete 2 critical tasks.
Intent: Create 5 quantitative survey questions to assess perceived usefulness and findability of the feature.
Response Format: Output question list with suggested 5- or 7-point scale.
Ask for clarification about the types of assistance provided (e.g., FAQs, contextual tips).
Then, suggest how to include these results in your usability report.
Prompt Template 4: “Audit an Existing Survey for Redundancy and Bias”
Audit an Existing Survey for Redundancy and Bias
Context: You are reviewing a reused post-test survey for a new mobile sign-up flow.
Specific Info: Original survey has 12 questions, some from unrelated features.
Intent: Eliminate leading, redundant, or irrelevant questions while maintaining data intent.
Response Format: Return a revised 6-question version with justifications for each removal or phrasing tweak.
Ask whether previous survey scores are being compared over time.
Then, recommend areas where qualitative follow-up may be stronger than survey data.
Prompt Template 5: “Summarise Survey Results in an Executive-Friendly Way”
Summarise Survey Results in an Executive-Friendly Way
Context: You’ve just wrapped a round of post-test surveys for an internal enterprise tool.
Specific Info: Survey results from 9 participants include average satisfaction = 4.2/5 and confidence = 3.1/5.
Intent: Create one-page slide content conveying key insights and recommendations.
Response Format: Output bullet copy formatted for a deck slide or email brief.
Ask for the audience’s familiarity with research context (execs vs product leads).
Then, propose a headline or data visualisation to strengthen the impact.
Prompt Template 6: “Design a Survey to Validate a Hypothesis About Onboarding Confusion”
Design a Survey to Validate a Hypothesis About Onboarding Confusion
Context: You observed that new users hesitated during Step 3 of a 5-step onboarding.
Specific Info: Usability test showed pauses and questions; team believes wording is confusing.
Intent: Create survey questions to measure perceived clarity and task difficulty for that step.
Response Format: Output 3–5 Likert-scale survey questions with comments optional.
Ask to confirm if new users had seen similar onboarding patterns before.
Then, suggest one usability A/B approach to test variants if confusion persists.
Prompt Template 7: “Compare Quant Usability Scores Between Two Prototypes”
Compare Quant Usability Scores Between Two Prototypes
Context: You tested two versions of a dashboard navigation layout.
Specific Info: Each version was tested with 5 users and received post-test surveys on ease of use and task speed.
Intent: Generate a table comparing both designs by score averages and standard deviation across all questions.
Response Format: Output a comparison table + 1–2 sentences interpreting the differences.
Ask if design goals changed between the two versions.
Then recommend the best statistical graph or visual to share in a stakeholder workshop.
Prompt Template 8: “Adapt a Post-Test Survey for Cognitive Accessibility”
Adapt a Post-Test Survey for Cognitive Accessibility
Context: Your project supports neurodivergent users testing an education platform.
Specific Info: Original survey uses 7-point scales and abstract wording.
Intent: Simplify the survey to increase clarity and reduce anxiety or overthinking.
Response Format: Revised version with plain language, binary/emoji scales, and reason for changes.
Ask if this population prefers alternate survey formats (audio, icons).
Then, propose a way to test comprehension of the new survey.
Prompt Template 9: “Translate Survey Findings Into Design Actionables”
Translate Survey Findings Into Design Actionables
Context: You have completed testing on a B2B invoicing tool with quant survey results in hand.
Specific Info: Key findings include low ease-of-use ratings for invoice preview.
Intent: Generate a list of tactical UX changes to improve the UI in response.
Response Format: Return a table pairing each survey insight with a UX change recommendation.
Ask if interaction logs from the session are also available.
Then propose a priority sort mechanism based on functional impact and feasibility.
Prompt Template 10: “Benchmark Against Industry SUS Standards”
Benchmark Against Industry SUS Standards
Context: You collected SUS scores after testing a new feature in your finance app.
Specific Info: You received an average score of 66.2 across 12 users.
Intent: Interpret this score against standard industry benchmarks and recommend improvements.
Response Format: Output interpretation + action areas in clear list format.
Ask if prior SUS baselines exist for comparison.
Then suggest one improvement that could raise the next round’s score meaningfully.
Recommended Tools
- Google Forms or Typeform – for lightweight survey capture and logic branching
- UsabilityHub or Maze – to pair unmoderated testing with post-task survey capture
- Lookback or Dovetail – to correlate qualitative session logs with quant scores
- SUS Calculator by MeasuringU – for consistent System Usability Scale scoring
