People and HR High ROISkill: Intermediate Sprint
Analyze employee exit surveys
Uncover patterns in exit feedback to reduce unwanted turnover
$01//
Expected Impact
Exit survey analysis identifies preventable turnover patterns worth 20% reduction in attrition and saves $15K+ per prevented departure through targeted interventions.
$02//
Recommended Models
Anthropic: Claude Sonnet 4.5OpenAI: GPT-5.2
$03//
Inputs Required
Exit Survey DataTime PeriodDepartment/Role DataTurnover Context
$04//
Prompt Template
Copy and customize for your context
I need to analyze employee exit survey data to identify patterns and improvement opportunities.
Exit survey data: [EXIT_SURVEY_DATA]
Time period: [TIME_PERIOD]
Department/role breakdown: [DEPARTMENT_ROLE_DATA]
Context: [TURNOVER_CONTEXT: voluntary vs involuntary, regretted vs non-regretted]
You are a people analytics and retention expert. Help me uncover actionable insights from exit feedback.
CONSTRAINTS:
- Protect individual anonymity in findings
- Distinguish between themes and outliers
- Separate stated reasons from underlying root causes
- Focus on actionable, preventable issues
DELIVERABLES:
- Top exit themes by frequency and impact
- Trend analysis: improving or worsening areas
- Department/role breakdown of key issues
- Comparison of regretted vs non-regretted turnover drivers
- Root cause analysis for top 3-5 themes
- Recommended interventions prioritized by impact
- Leading indicators to monitor
$05//
Implementation Tips
1
Provide survey data with demographic segments so AI can identify patterns across groups
2
Ask AI to preserve powerful direct quotes—aggregate themes lose the emotional impact of real feedback
3
Request "surprising insights" that contradict common assumptions about why people leave
For:
HR ManagerPeople AnalyticsCHROHR Business Partner
Best with a Custom GPT for repeated use
May involve sensitive data — review before sharing
