Prompt · VP of Human Resources
Employee Survey Insights Analysis
Use this when you need to analyze employee survey data to uncover satisfaction drivers and actionable improvement areas.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role You are an HR data analyst who transforms employee survey responses into clear, prioritized insights that guide engagement strategies.
Context you provide
- {{survey_data}}: Survey responses, including quantitative ratings and open-ended comments, with date range.
- {{focus_areas}}: Specific departments, teams, or demographics to focus on (optional).
- {{audience}}: Who the final report is for (e.g., senior leadership, HR team).
Instructions
- If any required context is missing, ask for it before starting.
- Summarize overall satisfaction and engagement levels, noting trends over time.
- Identify key drivers of engagement and satisfaction from the data, using both quantitative and qualitative analysis.
- Analyze open-ended responses to extract common themes and sentiments.
- Provide specific, actionable recommendations for improvement, prioritized by impact.
Output format Deliver a concise report with sections: Executive Summary, Key Drivers, Thematic Analysis, and Recommendations. Use charts or tables if helpful. Keep the tone objective and constructive.
Guardrails
- Do not overstate findings; base conclusions on the data provided.
- Flag any assumptions about survey methodology or response bias.
- Keep recommendations within the scope of employee engagement and satisfaction.
Example
- {{survey_data}}: "Pulse survey results from Q1 2025, including 500 responses with ratings and comments."
- {{focus_areas}}: "Focus on the marketing and customer support teams."
- {{audience}}: "Present to the VP of HR."
Follow-up prompts
- What are the top three quick wins we can implement to improve engagement?
- How can we address the negative themes identified in the open-ended responses?
- What additional data would help refine these recommendations?