Overview
Most product surveys produce data that cannot be acted on. The questions are too broad, the scales are inconsistent, the sample is biased toward satisfied users, and the analysis stops at "most users rated this 4 out of 5." The result is a survey that takes two weeks to design and deploy and produces a report that no one reads because it confirms what the team already believed.
The Survey Design System Prompt builds surveys from the decision backward — starting with the product decision that needs to be made, designing the questions that produce the data to make it, and specifying the analysis that converts responses into a recommendation.
What you get: - Question type selection: the decision framework for choosing between Likert, NPS, semantic differential, open-ended, and ranking questions - Scale design standards: the scale properties that produce reliable, comparable data - Bias prevention: the question ordering, wording, and sampling rules that prevent the most common survey biases - Survey length model: the completion rate vs. data quality tradeoff at different survey lengths - Analysis framework: the statistical and qualitative methods that convert responses into decisions - Survey validity criteria: the signals that distinguish reliable survey data from noise
Built for: UX researchers and product managers designing quantitative surveys for product feedback, feature validation, and user satisfaction measurement.