HR Attrition Risk Analysis & Employee Retention Dashboard (Power BI)

Employee attrition is one of the biggest hidden costs for any organization. Hiring is expensive, training is expensive, and losing experienced employees hurts productivity.

In this project, I built a 3-page Power BI dashboard to analyze:

• Why employees leave
• Which employees are at high risk
• What factors influence attrition
• How HR can design a retention strategy using data

This dashboard converts raw HR data into actionable retention insights.

Problem Statement

HR teams usually know how many employees left, but they don’t know:

• Which department is losing more employees?
• Does salary, overtime, or job satisfaction affect attrition?
• Which age group is at risk?
• Can we identify employees at High / Medium / Low risk before they leave?

This dashboard answers all these questions using DAX, visuals, and risk scoring.

Dataset Used

IBM HR Analytics Employee Attrition Dataset (Kaggle)

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It contains:

• Employee demographics
• Department, Job Role
• Monthly income
• Job satisfaction, work-life balance
• Overtime
• Years at company
• Attrition (Yes/No)

Important DAX Measures Used

Total Employees

Total Employees = COUNT(HR[EmployeeNumber])

Shows total workforce size.

Attrition Count

Attrition Count = CALCULATE(COUNT(HR[EmployeeNumber]), HR[Attrition] = "Yes")

Number of employees who left.

Attrition Rate %

Attrition Rate % = DIVIDE([Attrition Count], [Total Employees]) * 100

Percentage of employees leaving.

Average Monthly Income

Avg Monthly Income = AVERAGE(HR[MonthlyIncome])

Average Years at Company

Avg Years = AVERAGE(HR[YearsAtCompany])

Page 1 — Attrition Overview

KPIs

• Total Employees
• Attrition Count
• Attrition Rate
• Avg Income
• Avg Years

Meaning: Young and mid-career employees in R&D and Sales are most at risk.

Page 2 — Attrition Risk Factors

This page explains why employees leave.

Meaning: Overtime, low satisfaction, and poor balance are major drivers.

Page 3 — Employee Risk Scoring & Retention Strategy

Here we classify employees into Low / Medium / High Risk.

Meaning: HR can act before employees leave.

Key Insights from Dashboard

• 16% attrition rate is high
• Overtime is the biggest attrition trigger
• Young employees leave early due to low satisfaction
• Sales & R&D departments need retention policies
• 27% workforce is already in high-risk zone

How This Helps HR

Instead of reacting after employees leave, HR can:

• Reduce overtime for high-risk employees
• Improve job satisfaction through engagement
• Offer salary revision for early-career employees
• Focus on Sales & R&D retention programs
• Monitor high-risk employees monthly

This turns HR from reactive to predictive.

Tools Used

• Power BI
• DAX
• Data Modeling
• HR Analytics Dataset

Conclusion

This dashboard shows how data analytics can directly help in employee retention strategy.

By combining KPIs, risk factors, and DAX-based scoring, we can predict attrition before it happens.

This project is a perfect example of how a Data Analyst can create business impact, not just charts.

link for dashboard:

portfolio-project/HR attrition risk and empolyee retention dashboard at main ·…