How to design surveys your AI can actually understand

Garbage in, garbage out. If your survey questions are confusing to humans, they will produce confusing data for AI to analyze.

Here are the best practices for designing surveys that yield clean, actionable insights.

Common Survey Design Mistakes

1. Double-Barreled Questions: "How was our support and billing?" If they say "Bad," is it the support or the billing?
2. Vague Scales: "Rate us 1-10" without finding what 1 means (Terrible? Average?).
3. No Context: Asking for open feedback without a prompt leads to generic "It's okay" responses.

Principles for AI-Friendly Surveys

1. One Concept Per Question

Keep it simple. "How was the onboarding?" is better than "How was your onboarding and first project creation?"

2. Use Standardized Scales

Stick to known standards like 0–10 for NPS or 1–5 for CSAT. AI models are trained on these patterns and understand them well.

3. Targeted Open-Ended Prompts

Always follow a rating question with a "Why?"

• Bad: "Any other comments?"
• Good: "What is the main reason for the score you gave above?"

4. Add Context in the Stem

Put the context in the question itself.

• Bad: "How was it?"
• Good: "How easy was it to find the documentation you needed?"

How This Plays with FeedPulse AI

The clearer your questions, the more precise our AI's driver extraction and summarization will be.

• Messy Input: "Support/Billing" → AI struggles to separate sentiment.
• Clean Input: "Billing Process" → AI clearly identifies "Billing Confusion" as a driver.

Test your survey design

Unsure if your questions are clear? Use our templates or upload an existing survey export and see how the AI handles it.

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