# Customer Survey Design > Use when the user wants to design a customer or market survey — choosing the questions, structure, scales, and sample size to get unbiased, decision-useful answers. Also use when the user mentions survey, questionnaire, NPS, customer feedback, market research survey, product-market fit survey, sample size, margin of error, "how many responses do I need," or "what should I ask my customers." Produces a structured survey with unbiased questions mapped to decisions, plus computed sample-size, invite, and segment-readability numbers from a runnable engine. - Skill: `sarojkjha/customer-survey-design` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add sarojkjha/customer-survey-design` - Raw SKILL.md: https://api.skillmd.com/api/skills/sarojkjha/customer-survey-design/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - License: MIT - Author: sarojkjha (https://skillmd.com/u/sarojkjha) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/sarojkjha/customer-survey-design --- # Customer Survey Design Design surveys that produce **answers you can act on**, not vanity data. The discipline is starting from the decision the survey must inform and writing questions that don't lead the respondent — most surveys fail by asking biased questions that confirm what the author hoped. The second, quieter failure mode is fielding a survey that can't answer its own question: 60 responses split across five segments, then decisions made on ±13-point noise. This skill computes what a readable survey actually requires **before** anything is sent — and refuses to bless segment cuts the sample can't support. ## When to use The user is about to field a survey and needs the questions, structure, and sample plan to be right before it goes out. ## Before you start 1. **Name the decision.** What will you do differently based on the results? If a question doesn't inform a decision, cut it. 2. **Identify the respondents** and how you'll reach them (existing customers, churned users, prospects, market panel) — this shapes wording and length. Get the reachable audience size and a realistic response rate if known. 3. **List the cuts you'll report.** If the readout says "by segment," the sample must be sized for the smallest cell, not the total. 4. **Pick the survey type** to match: PMF ("how would you feel if you could no longer use this?"), NPS + why, onboarding/CSAT, churn reason, or discovery. ## Method The engine (`resources/survey-design.js`, Node, stdlib only) computes: - **Required completes** — n₀ = z²·p(1−p)/e², with finite-population correction n = n₀/(1+(n₀−1)/N) when the audience is finite. Worked check: ±5pp at 95% (z=1.96, p=0.5) → n₀ = 384.16 → **385**; corrected for N=2,000 → **323**. - **Invites** — completes ÷ response rate, with a feasibility verdict against the audience size (loosen the margin, lift response, or run a census and report the achieved ±). - **Segment readability** — worst-case margin per cell, e = z·√(0.25/n_cell), labelled READ (≤±5pp), CAUTION (±5–8pp, directional), DON'T READ (wider). Thresholds are working labels, not laws. Modes: `sample ''` and `segments ''`. `--demo` runs both worked examples with no arguments; `--help` prints the schema. ## Workflow 1. **Size the survey first** — run `node resources/survey-design.js sample ''` with the margin, confidence, audience, and response rate. If it's not feasible, fix the plan before writing a single question. 2. **Check the cuts** — run `segments` mode with planned segment shares. Merge or oversample any DON'T READ cell now, not after fielding. 3. **Map questions to decisions** — list the decisions, then write the minimum questions that inform each. 4. **Choose question types and scales** using `resources/survey-template.md` (open-ended for discovery, closed/scale for measurement; consistent scale direction). 5. **Write unbiased wording** — neutral, one idea per question, no leading or loaded phrasing, balanced options. 6. **Order for flow and low drop-off** — easy/engaging first, sensitive/demographic last, group by topic, keep it short. 7. **Add the key open-ended "why"** after rating questions — that's where the actionable insight lives. ## Present the result Lead with the sample plan (completes, invites, feasibility, and the segment-readability table from the engine), then the survey in order (question, type, scale/options) with the decision each informs, an estimated completion time, and the recommended channel/timing. ## Guardrails & common mistakes - **Don't promise segment reads the sample can't support.** If a cell says DON'T READ, either resize or drop the cut from the readout. - **No leading questions.** "How much did you love our amazing onboarding?" yields noise. Ask neutrally. - **One idea per question.** Double-barreled questions ("Was it fast and easy?") can't be answered cleanly. - **Keep it short.** Every extra question lowers completion and data quality. - **Balance the scale.** Offer as many negative as positive options; include a neutral where appropriate. - **Pilot first.** Test with a few people to catch confusing wording before fielding. - **Survey sampling ≠ experiment significance.** Testing whether variant B beat variant A belongs in `ab-test-significance`; this engine sizes *reads*, not experiments. ## Related AAJ resources - Interactive tool: https://aajconsult.com/tools/survey-studio ## Related skills `persona-builder` (turn answers into personas) · `positioning-statement` (questions that test messaging) · `lifecycle-and-retention` (churn-reason surveys feed the retention diagnosis) · `ab-test-significance` (experiments, not survey reads). ## Credits Original AAJ skill. The Agent Skills format and Corey Haines' `coreyhaines31/marketingskills` (MIT) were references for structure and coverage; this skill is independently written. See the repository README.