HR / PEOPLE OPS

Employee Exit Interview & Attrition Intelligence

Understand why your best people actually leave, proven with statistics.

Upload exit interview transcripts, engagement surveys, and HRIS records, and Steeped AI reveals why your best people actually leave. Custom topic mapping surfaces concepts generic sentiment tools miss: manager conflict, compensation dissatisfaction, burnout language, and career-growth frustration. Steeped AI then quantifies how often each appears by department, tenure, and manager.

University-grade statistical testing confirms which factors are genuinely driving attrition versus normal noise, so HR leaders stop chasing anecdotes. The Eureka Score ranks which combinations, like short tenure plus a specific manager, predict resignation risk before it happens. Messy HRIS exports get cleaned and standardized automatically, so nothing skews the analysis. Turn scattered exit data into a cited, board-ready retention report that tells people leaders exactly where to intervene, proven with statistics instead of guesswork.

Expand
Rank Attrition Drivers by Impact
Manager Conflict + <1yr
43%
Comp Dissatisfaction
26%
Burnout Signals
19%
No Career Path
14%
Base Column
manager ▾
Value Column
resignation_risk ▾
43%ofManager B=Resignation Risk
predictive power
84%
resigned count
31
report count
72
see examples

Talk to Your Data

Ask retention questions in plain language and get answers backed by exact calculations. Instead of manually reading hundreds of exit interviews, ask why top performers leave a specific team or tenure band, and Steeped AI returns the quantified drivers in seconds, each one traceable to the source records.

DISCOVER
Talk to Exit Data for Insights
top resignation driversat-risk teams
Why do top performers leave Engineering?|
Find Insights
• 39% of "exit_reason" cited "limited career growth"
• 27% cited "manager conflict" under 1yr tenure

AI Topic Mapping

Use Steeped AI's AI topic mapping to surface nuanced departure reasons like manager conflict, compensation dissatisfaction, and burnout that keyword tagging and generic sentiment scores miss. Quantify how often each appears by department, tenure, and manager so you act on real drivers.

manager conflictburnoutcareer growth frustration

Statistical Significance Testing

Steeped AI's automated significance testing separates real attrition drivers from noise, so HR acts on validated factors, not anecdotes. A university-grade, 3-part framework confirms which departure patterns are statistically real before you take them to the board.

Real Driver Confirmedp < 0.01 · FDR corrected

Employee Journey Mapping

Apply Steeped AI's journey mapping to the employee lifecycle. Map tenure milestones and career events into one measurable story of engagement, so you can see exactly where disengagement begins and which stages carry the highest attrition risk.

Yr 1: -18%hiredresignedrisk 84%

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.