Employee Attrition Analysis Dashboards with key insights and general recommendation

Table of content

• Analysis
• Overview
• Key Insights
• Recommendations
• Limitations of the Analysis
• References

Analysis

Data Source: Kaggle

Tool(s): Microsoft excel

Objective: To identify key factors influencing employee attrition and develop actionable strategic recommendation to improve employee retention.

Methodology & Procedures

To ensure accurate and insightful analysis, the following data processing steps were followed:

A. Data Collection

Sourced a comprehensive Employee Attrition dataset from Kaggle. The dataset includes employee demographics, job roles, education, salaries, tenure, and attrition status.

B. Data Cleaning

Handled Missing Values: Checked for nulls and removed or imputed missing entries as needed. Standardized Categorical Variables: Normalized gender, marital status, department, and education levels for consistency. Filtered Relevant Columns: Retained columns that contribute directly to attrition analysis, e.g., age, job role, business travel, education, etc.

C. Data Transformation

Created Age Groups: For clearer analysis of attrition by age bracket. Grouped Job Roles and Departments: To identify areas with the highest attrition rates. Calculated Attrition Rate: Added a metric to quantify the percentage of employees who left.

D. Visualization

Used visual dashboards (as shown) to explore relationships between: Attrition vs. Department, Job Role, Business Travel Attrition vs. Age, Education Level, Marital Status Demographic distributions and tenure patterns

E. Insight Extraction

Analyzed charts and graphs to spot trends and outliers. Identified high-risk groups for attrition and potential causes based on travel demands, job stress, or career stagnation.

Overview

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This analysis explores employee attrition trends using demographic, departmental, educational, and job-related data. The goal is to uncover patterns and potential causes of attrition, helping organizations make data-driven decisions to reduce turnover and improve employee satisfaction.

Total Employees: 1,470

Active Employees: 1,233 (84%)

Total Attrition: 237 (16%)

Average Tenure: 4.56 years

Total Annual Salaries: $166,046,052

Key Insights

A. Demographics

Gender: Fairly balanced (675 females, 651 males); similar attrition across genders.

Age: Highest concentration between 25–30 years, also the peak attrition age.

Marital Status: Most employees are married (624), followed by single (549); younger, single employees may be more prone to leaving.

Education Level: Highest numbers in Bachelor’s (572) and Master’s (398) holders, with Bachelor’s degree holders showing higher attrition.

B. Department & Job Role

Top Departments:

Technology (961 employees)

Sales (446)

HR (63)

Job Roles with High Headcounts:

Sales Executive (327), Software Engineer (294), Data Scientist (261)

High Attrition Jobs:

Sales Executives and Software Engineers show significant attrition.

C. Business Travel

Employees with “Some Travel” (1,043) exhibit the highest attrition, suggesting a link between travel and job dissatisfaction or fatigue.

D. Location

Majority of employees are from California (875), followed by New York (419) and Illinois (176).

Recommendations

1. Investigate Key Attrition Roles:

Focus retention strategies on Sales Executives and Software Engineers, including workload management and incentive structures.

2. Strengthen Career Development:

Create clear advancement paths for Bachelor’s and Master’s degree holders to boost retention.

3. Reassess Travel Policies:

Introduce travel alternatives or support systems for employees with “Some Travel” responsibilities.

4. Support Younger Employees:

Tailor onboarding, mentoring, and career planning for employees aged 25–30 to help them settle and grow.

5. Enhance Work-Life Balance:

Offer flexible work options, especially for single and younger staff who may experience burnout more easily.

6. Use Data-Driven HR Strategies:

Leverage demographic and role-specific insights to create targeted engagement and retention programs.

Limitations of the Analysis

1. Static Historical Data:

The dataset is based on past employee records and may not reflect current workplace dynamics or recent policy changes.

2. Lack of Qualitative Insights:

While the data provides quantitative indicators (age, role, tenure, etc.), it lacks qualitative factors like employee satisfaction, management quality, or company culture, which significantly affect attrition.

3. Assumed Causality:

The analysis identifies correlations (e.g., high attrition in sales), but does not establish direct cause-and-effect relationships without further qualitative validation.

4. Geographical Generalization:

The dataset includes only three states (CA, NY, IL), which limits the generalizability of findings to a broader workforce across other locations.

5. Imbalanced Department Sizes:

The Technology department accounts for a disproportionately large share of employees, which might skew overall attrition patterns.

6. Simplified Travel Categories:

The “Business Travel” variable is broad and doesn't differentiate between occasional long-distance travel and frequent short commutes, which may affect employees differently.

7. Educational Field Gaps:

While education level and field are included, there's limited insight into how educational background directly impacts job satisfaction or performance.

8. External Factors Not Considered:

Economic shifts, industry trends, personal life events, or pandemic-related impacts are not captured in the dataset but can influence attrition.

References

1. Kaggle Dataset Source

• IBM HR Analytics Employee Attrition & Performance Dataset
• Retried from:

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