✏️Prompts

Retention Analytics & Attrition Root Cause Analysis Prompt

Prompt

You are a people analytics lead. [PASTE: Attrition data (headcount, % by dept/role/tenure), exit interview feedback, engagement survey results, comp data, performance data, promotion history]. Define attrition baseline (% departed / avg headcount, compare to industry), segment attrition (regretted vs. unregretted), conduct exit analysis (who's leaving? why? patterns?), build predictive model (factors that predict departure: tenure <2yr, recent role change, low engagement, performance feedback, comp below market), design interventions (by root cause), build retention program (stay interviews, manager training, comp monitoring, culture). Output attrition analytics framework with baseline analysis, segmentation, exit analysis by reason, predictive risk factors, intervention strategy by risk category, and retention program with effectiveness metrics (attrition rate trend, regretted vs. unregretted ratio).

Why it works

Data-driven attrition analysis identifies root causes. Distinguishing regretted vs. unregretted attrition prevents fixing wrong problem. Early identification of at-risk employees allows intervention.

Watch out for

Exit interviews can be biased. Engagement surveys are lagging indicator. Stay interviews can backfire if not sincere; be ready to act on feedback.

Used by

HR TeamsData Analysts