This project analyzes behavioral patterns, retention, and compensation patterns using SQL to uncover key drivers of turnover and performance.
- Identified high-turnover departments (Marketing: 93%, Engineering: 67%)
- Detected weak alignment between salary and performance
- Highlighted early attrition risk (20% leave within 1 year)
- Delivered actionable recommendations to improve retention and compensation fairness
This project analyzes HR data from NextGen Corp., a technology company experiencing concerns around employee turnover, inconsistent performance, and salary disparities across departments. The analysis was designed to answer business-critical questions using SQL and present findings in a stakeholder-friendly format.
The project focused on three key goals:
-
Employee Retention Analysis
- Identify turnover trends across departments
- Understand root causes of employee exits
- Detect employees at risk of leaving based on performance patterns
-
Performance Analysis
- Evaluate employee performance across departments
- Identify high- and low-performing groups
- Highlight areas requiring management intervention
-
Compensation Analysis
- Examine salary distribution by department and job title
- Assess whether compensation aligns with performance
- Recommend fairer salary benchmarks and policy improvements
- SQL – data querying and analysis
- PowerPoint – presentation of business insights and recommendations
- Database file – source data storage
- Who are the top 5 longest-serving employees?
- What is the turnover rate by department?
- Which employees may be at risk of leaving based on performance?
- What are the main reasons employees leave the company?
- How many employees have left the company overall and by department?
- Which departments have the highest share of top and low performers?
- What is the average performance score by department?
- What is the company’s total salary expense?
- What is the average salary by job title?
- How many employees earn above 80,000 by department and job title?
- How does salary relate to performance across departments?
Although this dataset is based on employee data, the same analytical approach applies to user behavior in digital products.
- Employee retention → User retention
- Turnover → Churn
- Departments → User segments or cohorts
- Performance → Engagement or activity levels
This demonstrates how SQL can be used to analyze behavior, identify at-risk groups, and support data-driven product decisions.
- Data exploration and validation using SQL
- Aggregation and grouping to identify behavioral patterns
- Segmentation of users based on performance and retention
- Comparative analysis across departments (cohorts)
- Translation of query outputs into business insights and recommendations
- About 20% of employees stayed one year or less, showing significant early turnover.
- Marketing and Engineering had the highest turnover rates at 93% and 67% respectively.
- The top reasons employees left were Personal Reasons and Found Another Job, suggesting deeper dissatisfaction drivers behind exits.
- 28 employees had left the company, with Engineering and Marketing recording the highest exits.
- Only 9 employees achieved the highest performance score of 5.0, while 45 employees had performance scores below 3.5.
- Marketing had both the highest number of top performers and the highest number of low performers, suggesting inconsistent team performance.
- Total salary expense was 4,850,000.
- Salary distribution varied significantly by job title and department.
- The analysis found weak alignment between salary and performance, especially in Engineering and Marketing.
- Employees with strong performance did not consistently receive higher compensation, indicating possible fairness issues.
- Introduce a fair performance-to-pay structure across departments.
- Use automated performance tracking to reduce bias in salary adjustments.
- Review compensation parity across equivalent roles in different departments.
- Focus retention and performance improvement efforts on Engineering and Marketing.
nextgen-employee-success-analytics/
│
├── README.md
├── sql/
│ └── queries.sql
├── outputs/
│ ├── outputs.pdf
│ └── sql_analysis-output
├── presentation/
│ ├── nextgen_hr_analytics_presentation.pptx
│ └── presentation.pdf
└── images/
├── performance_distribution.png
├── salary_analysis.png
├── salary.png
├── turnover_analysis.png
└── turover_rate.png
This project demonstrates the ability to:
- analyze behavioral data using SQL
- identify retention risks and segment users
- translate data into actionable business insights
- communicate findings clearly to stakeholders
Hamzat Afe Isede Data Analyst / Data Scientist