Exit Interview Analyzer

The Challenge

Exit interviews collect valuable data, but insights get buried in individual responses. HR lacks time to analyze patterns across departures and translate findings into retention strategies.

The AI Desk Solution

AI Desk analyzes exit interview data to surface patterns, trends, and actionable recommendations.

The Workflow

Step 1: Data Collection

Scope: Q1 2026 exit interviews
Sources: Exit surveys, manager notes, tenure data

Step 2: Pattern Analysis

• Theme extraction
• Department comparison
• Tenure correlation

Step 3: Insights Report

📊 Exit Interview Analysis: Q1 2026

OVERVIEW
├── Total departures: 24
├── Voluntary: 20 (83%)
├── Regrettable: 12 (50%)
└── Avg tenure: 2.1 years

TOP DEPARTURE REASONS

Career Growth (42%)
├── "Limited advancement opportunities"
├── "Skills not being developed"
├── Highest in: Engineering, Sales
└── Action: Review promotion criteria

Compensation (33%)
├── "Below market for my role"
├── "Competing offers 15-20% higher"
├── Highest in: Engineering
└── Action: Market comp analysis

Management (25%)
├── "Lack of feedback"
├── "Unclear expectations"
├── Departments: Mixed
└── Action: Manager training

BY DEPARTMENT
├── Engineering: 10 (concern level: high)
├── Sales: 6 (concern level: medium)
├── Marketing: 4 (within normal)
├── Support: 4 (within normal)
└── Industry avg turnover: 15%

TENURE PATTERNS
├── < 1 year: 8 (onboarding issue?)
├── 1-2 years: 9 (growth ceiling?)
├── 2-4 years: 5 (normal progression)
└── > 4 years: 2 (natural turnover)

RECOMMENDATIONS
├── 🔴 Engineering comp review (urgent)
├── 🔴 Career ladder clarity
├── 🟡 Manager training program
├── 🟡 Onboarding improvements
└── 🟢 Continue culture investments

Value Proposition

• Time Saved: 2 hours of analysis
• Pattern Detection: Trends visible
• Actionable: Clear recommendations