Research

Classify Survey Responses with AI

Product, Pricing, Support Experience, Onboarding, Documentation — multi-label topic classification for open-ended survey answers.

Overview

Open-ended survey questions produce the most valuable and least countable feedback: one answer touches pricing, onboarding, and the docs in three sentences. This setup classifies responses by topic in Multiple Labels mode — every topic that's genuinely addressed, strongest first — with definitions that keep adjacent topics apart: Support Experience is about interactions with the team, Documentation is about the help content itself, Onboarding is the getting-started phase regardless of what it touched. Conservative ambiguity keeps topic counts honest.

Workflow

Classify per response
Each answer gets its own label list — aggregation into counts happens in your sheet, with traceability per response.
Respect the mention threshold
"A merely mentioned topic does not earn its label" — a pricing aside in an onboarding story should not count as pricing feedback.
Keep waves comparable
Freeze the label set and definitions across survey waves — changed definitions mean uncomparable counts.

Why This Works

Multi-label matches the bundled nature of open-ended answers
Team-vs-content definitions cleanly split support and documentation feedback
Conservative policy keeps trend lines driven by real signal, not generous labeling

Best for

Research teams with hundreds of free-text responses per wave
Surveys whose answers bundle multiple topics
Topic taxonomies with adjacent, easily-confused categories

Not for

Extracting the quotes worth keeping — pair with the Extraction Prompt Generator for that
Sentiment per topic — run sentiment as its own classification pass

Use cases

Making open-ended answers countable by topic
Tracking topic mix across survey waves
Splitting support-experience feedback from documentation feedback

Customize This Resource

Opens this setup in Data Classification Prompt. Generate to get the full classification prompt — then adjust the labels, ambiguity mode, and confidence output.

Open in Data Classification Prompt

Prompt Template

TASK
Categorize open-ended survey responses by the topics they address.
Assign one or more of the labels defined below to the text in the input.

LABELS
- Product: Feedback about the product itself — features, quality, reliability.
- Pricing: Feedback about cost, plans, or perceived value for money.
- Support Experience: Feedback about interactions with the support team.
- Onboarding: Feedback about getting started — setup, first use, learning curve.
- Documentation: Feedback about guides, help articles, or missing documentation.

CLASSIFICATION RULES
- Read the entire text before deciding — the deciding signal may come late.
- Judge by content and intent, not by tone, length, or formatting.
- Match the text against the definitions, not just the label names.
- Use only the labels defined above — never invent a new label.

EDGE CASE RULES
- If the text fits more than one label, return every label that applies.
- Order the labels by how strongly each applies, strongest first.
- A merely mentioned topic does not earn its label — it must be a real subject of the text.

AMBIGUITY POLICY
- Choose a label only when its definition genuinely covers the text.
- When torn between two labels, re-read both definitions and pick the closer one — do not guess from the names.

OUTPUT FORMAT
Return your classification as a single valid JSON object with a "labels" array.

EXAMPLE OF A VALID RESPONSE
{
  "labels": ["Product", "Pricing"]
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Return the label text exactly as defined above — no paraphrasing, no abbreviations, no new labels.

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