Designing Open-Ended Items for Comment Copilot

Open-ended survey comments can yield powerful insights when we ask the right questions. Crafting open-ended items with intentionality, grounded in an understanding of the end users and the analytic tools at their disposal, is essential to translating qualitative data into actionable outcomes.

When managers are the primary consumers of comment data accessing responses through standard Perceptyx comment reports, questions should be designed to facilitate clarity and inform action. In these cases, it is especially effective to prompt employees with focused questions tied to a strategic priority, process or recommendation for improvement. Specificity enhances the relevance of responses and supports more targeted action planning at the local level.

Conversely, when open-ended data is intended for use by senior leaders or survey administrators, and analyzed through tools like Comment Copilot, a broader, more neutrally phrased question may be appropriate. This allows for a wider spectrum of perspectives to emerge and enables AI to surface patterns and themes that may otherwise go unnoticed.

To fully harness the value of open-ended responses, we must be deliberate in how we structure our prompts. This guide is designed to support you in developing open-ended survey items that align with behavioral science principles and the capabilities of AI-powered analysis with Comment Copilot. With thoughtful design, open-ended questions can unlock richer insights, drive meaningful conversations, and inform more effective organizational decisions at the right level.

Considerations for Designing Open-Ended Questions

Design Best Practices for Comment Copilot

Sentiment: Use Neutral Framing

Comment Copilot performs best when questions are neutrally worded, inviting open reflection rather than steering responses toward overly positive or negative tones. A neutrally phrased item allows Copilot to draw from a broader set of prompts and uncover a wider range of insights from a single question. Neutral language also helps reduce bias and supports more honest, comprehensive feedback. 

• Instead of: "What improvements would you recommend to make the company a better place to work?"

• Try: "What additional comments do you have about your overall experience working at the company?"

This subtle shift opens the door for employees to share a broader range of experiences and insights that matter most to them.

Specificity: Encouraging Breadth with Room for Insight

There are situations where a highly targeted question is necessary to gain specific feedback on a strategic topic. For example, asking about a specific change or process can provide the direct insight needed for a focused initiative. At the same time, Copilot excels at analyzing responses to more general open-ended prompts. These broader questions are especially useful when survey space is limited and can uncover insights on topics not directly addressed elsewhere. With its ability to apply theme filters, Copilot surfaces meaningful feedback across a wide range of areas. A question like "What additional feedback would you like to share?" allows exploration of multiple themes like safety, collaboration, processes, leadership, or communication all through a single prompt.

• Instead of: "What one thing could we do to enhance our culture of safety?"

• Try: "What feedback would you like to share about your experience in your role?"

This broader framing creates space for employees to surface what matters most to them, while the AI identifies patterns across those reflections.

Response Length: Inviting Deeper Insight

The richness of feedback directly impacts the quality of AI-generated insights. The way we frame questions and structure response fields can nudge employees toward brevity or depth. For example, shorter responses often encouraged by smaller text boxes or character limits, work well for visuals like word clouds. However, Copilot performs best with longer, more detailed responses. When deeper analysis is the goal, it's helpful to use larger text fields and invite employees to share specific experiences or examples.

• To encourage short responses: Use direct prompts like "What 3–5 words describe our culture?" (great for word clouds).

• To encourage deeper insight: Ask questions like "Describe how our culture impacts your daily work experience."

Providing brief guidance encouraging respondents to provide examples or descriptions can also prompt more thoughtful feedback.

Access: Designing for the Right Audience

Consider who will be using the comment data and at what level decisions will be made. Some organizations limit comment access to the highest levels, while others expect managers to use the feedback directly. Comment Copilot is designed to be used by high level survey administrators, so questions should be crafted to generate insights meaningful at that level. For organizations doing action planning at the manager, department or store level, more specific questions are useful to inform actions. For Copilot use, higher level insights are better suited for broader analysis and Admin-level reporting.

• Instead of: "What can your store leader do to help you be more effective?"

• Try: "How does your store leader impact your overall experience in your role?"

This ensures the feedback can be synthesized and acted upon by those with visibility into broader themes.

Getting the Most from Comment Copilot

Great survey design isn’t just about gathering data, it's about creating a conversation between employees and their organization. Comment Copilot helps unlock this dialogue by turning open-ended responses into actionable insight. When questions are thoughtfully crafted and grounded in behavioral science, Copilot can surface the themes that matter most, helping organizations understand employee perspectives and plan their next steps with confidence.