User Satisfaction Survey Design
Measure experience with reliable questions.
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Design a user satisfaction survey. Product or service: [PRODUCT_OR_SERVICE]. Decisions the data will inform: [DECISIONS]. Audience and channel: [AUDIENCE_AND_CHANNEL]. Max length: [MAX_LENGTH].
Create 10 to 12 questions: overall satisfaction (5-point scale with labeled endpoints), task-based ease ratings, key moments (onboarding, support), likelihood to recommend (explain how the Net Promoter Score is computed: percent promoters, scoring 9 to 10, minus percent detractors, scoring 0 to 6; and its limits), open-ended questions ("What is the one thing we should improve?"), and demographics only if necessary and optional.
Provide question wording, response scales, branching logic, and a rationale for each. Add an invitation (80 words), thank-you page text, an analysis plan (average, distribution, top-box, segments, open-text coding), minimum-response advice, and privacy settings (anonymous or confidential, consent, retention) with local rules flagged [VERIFY in [JURISDICTION]]. Show before and after rewrites of three poor questions. Do not invent results.
Provide the final survey as a numbered list with exact wording and scales, the order of questions with reasons, and an estimate of completion time. Add a plan for the first 30 responses: check completion rate and drop-off by question before wider release.
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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.
See an example result (fictional data)
Q1 (5-point, very dissatisfied to very satisfied): "Overall, how satisfied are you with the booking experience?" Q2 (7-point ease): "How easy or difficult was it to find an available time?" Q3: "How likely are you to recommend us to a friend or colleague? (0 to 10)" Score: promoters 9 to 10, detractors 0 to 6. If 120 respond with 60 promoters and 18 detractors, NPS = 50 - 15 = 35. Q4 open: "What is the one thing we should improve?" Invitation (about 60 words): short, states 3 minutes, anonymous, how results are used. Analysis: distribution, top-two-box, themes from Q4 with counts. Before: "How much do you love our easy app?" After: Q2 above.
Illustrative only, made-up data. Your results will differ and must be reviewed.