Surveys

What is it?

Surveys are a quantitative (and sometimes qualitative) research method for collecting self-reported data from a large number of users simultaneously. In UX research, surveys are used to measure attitudes, preferences, satisfaction, and needs — data that is difficult to obtain from behavioural analytics or that requires scale beyond what interviews can achieve.

Why it matters

Surveys are one of the most scalable UX research methods — a well-designed survey can collect structured responses from thousands of users in days. They are particularly valuable for: measuring satisfaction (NPS, CSAT, SUS), understanding user demographics and context, prioritising features across a large user base, and tracking attitudinal changes over time. However, surveys are also one of the most misused methods — poorly designed surveys produce data that actively misleads design decisions.

Best Practices

• Define your research question before writing a single survey question. Every question should be traceable to a research objective.
• Write one concept per question. Double-barrelled questions ("How easy and enjoyable was the checkout?") produce uninterpretable responses.
• Avoid leading questions. "How helpful did you find the new navigation?" assumes it was helpful. "How would you rate the navigation?" does not.
• Use established scales for attitudinal measurement: Likert scale (1–5 or 1–7), NPS (0–10), SUS (10 standardised questions). Don't invent your own scales unless necessary.
• Put the most important questions first — survey completion drops rapidly after the first 3–5 minutes.
• Target 5–10 minutes maximum for general UX surveys. Task-specific intercept surveys should be 2–3 minutes.
• Random sampling matters. If you survey only active users, you are systematically excluding users who churned — often the most important group to understand.
• For NPS and CSAT: always follow up the numeric rating with an open-text question ("Why did you give that score?") — the qualitative response is often more actionable than the number.
• Pilot the survey with 5 users before full deployment to catch ambiguous questions.
• Analyse open-text responses with thematic coding, not keyword counting.

Common Mistakes

• Writing questions first and defining the research objective after — produces surveys that answer no meaningful question.
• Double-barrelled questions that cannot be cleanly interpreted.
• Leading or loaded questions that bias responses toward expected answers.
• Survey length that causes satisficing — users give any answer to finish rather than their true opinion.
• Using surveys when observation would produce more accurate data. Users report what they think they do; observation shows what they actually do.
• Convenience sampling: surveying only users who voluntarily engage — these are usually your most enthusiastic users, not representative of the whole user base.
• Treating correlation in survey data as causation without supporting evidence.
• Never closing the loop: collecting survey feedback and making no visible changes — damages user willingness to respond in future.

Checklist

Research & Theory

Real-World Examples