What it is
Structured questionnaires sent to representative users to collect quantitative and qualitative data on behaviors, attitudes, and needs at scale, complementing interviews with breadth.
Why use it
Surveys show how widespread a problem or behavior is and help prioritize deeper investigation. They can test whether interview findings hold more broadly, if the sample suits.
Use this when
- You need the frequency of a behavior across many users
- You want to check whether interview findings hold more broadly
- You need satisfaction, CSAT, or NPS during scoping
- A specific hypothesis can be assessed with closed-ended questions
You may not need this when
- The population is so small that segment comparisons would be unreliable
- You need the "why" behind behaviors; use interviews
- Equivalent recent survey data already exists
Dependencies — what you need
- Clear objectives and unbiased questions
- A channel reaching representative users (email, in-app, panel)
- Survey tooling (e.g., SurveyMonkey, Qualtrics, Typeform, Google Forms)
- Incentives, if needed for response
People involved
- Design
- Research
- Product
- Analytics
Typical outputs
- A quantitative dataset with segmentation
- Themes from open-text responses
- Interview findings supported, challenged, or left open
- Problem areas ranked by people affected and importance to them
Template
User Surveys template
A fill-in structure for running this method: what to prepare, what to do, what to record, and how to read the result.
How AI can help
AI as augmentation — not replacement
- Draft questions aligned to objectives, for review before use
- Flag leading, biased, or double-barreled questions
- Find recurring themes in open text
- Summarize findings by user type, from the numbers supplied
AI prompt
A prompt for any AI assistant. It works only from material you paste in, keeps assumptions apart from findings, and leaves the decisions to you.