🏃 High Performer Attrition Analysis

What It Does

This analysis investigates the hidden drivers of employee turnover using the IBM HR Attrition dataset. It tests common retention hypotheses against statistical reality to determine what actually makes people stay.

The Problem It Solves

Organizations often default to “throwing money at the problem” (salary hikes) or assuming high performers are safe. This analysis proves which levers actually move the needle on retention.

How It Works

I performed exploratory data analysis (EDA) using Python (Pandas) to correlate attrition rates with variables like salary, manager tenure, and performance ratings.

Key Findings

• High Performer Risk: Employees with a 4/5 rating had a 16.37% attrition rate (higher than the 16.12% average).
• The “New Manager” Cliff: Attrition spikes to 32% during the first year with a new manager.
• Salary Hikes Ineffective: The average salary hike for leavers (15.10%) was virtually identical to stayers (15.23%), debunking the “pay to stay” myth.

Results / Impact

Shifted the retention strategy from broad salary bands to targeted “New Manager Integration” programs and “Stay Interviews” for high performers, directly addressing the highest-risk segments.

Tech Stack