An Analysis of Salary Progression and Forecasting Models
Summary
This project evaluates salary progression relative to years of experience, aiming to develop accurate forecasting models to optimize compensation planning, budgeting, and workforce management.
Key Insights
- Data Trends: o Salaries increase linearly with experience, with key milestones at 5, 7, and 10 years showing significant growth. o Variability exists at certain levels, likely to reflect promotions or job changes.
- Data Suitability: o The dataset is complete and appropriate for regression and time-series forecasting models. o Lack of additional factors (e.g., education, role) limits deeper insights.
Error Analysis Across Models: Model MSE Comments
Business Scenario and Objectives: In a competitive job market, organizations must optimize compensation structures to attract and retain top talent while maintaining financial sustainability. This project focuses on analyzing salary progression relative to years of experience, a key driver in compensation decisions. The insights aim to guide organizations in designing competitive pay structures, forecasting future salary trends, and addressing potential gaps in workforce planning. Management Question: How does employee experience influence salary progression, and what forecasting model best predicts salary trends to aid in compensation planning and workforce budgeting? By answering this question, the organization can: • Ensure competitive salaries to retain talent. • Optimize budgets for workforce planning. • Design data-driven career progression frameworks.
Dataset Insights: • The dataset contains two key variables: Years of Experience and Salary. • Linear Growth Trend: Salary increases as years of experience grow, with a clear upward trend. • Variability at Milestones: Variations in salary are observed at certain experience levels (e.g., 4 years and 8 years), potentially due to job changes or industry factors. • No Missing Data: The dataset is complete, which simplifies modeling and forecasting efforts. Appropriateness for Modeling: • The dataset is well-suited for linear regression and time-series forecasting methods due to its numerical and sequential nature. • Absence of additional variables (e.g., education, industry, role) limits the depth of salary prediction models and insights into other influencing factors. Potential Limitations: • Small dataset size may reduce the generalizability of results. • Lack of demographic details restricts multi-factor analysis (e.g., age, gender).
The dataset is publicly available at Kaggle. It represents realistic salary progression and can be extended for workforce modeling by adding more variables.
Using the data set provided, various forecasting methods were evaluated to model and predict salary trends. The goal was to minimize prediction errors and ensure accurate insights for management decisions. Models Applied:
- Winters' Forecasting Method (Triple Exponential Smoothing): o Captures trends and seasonality. o Mean Squared Error (MSE): 27,439,356 o Performs well for datasets with consistent patterns, though seasonality in this dataset is limited.
- Exponential Smoothing (Single Smoothing): o Smooths data without accounting for trends or seasonality. o MSE: 54,814,741 o Struggles to capture the upward trend effectively.
- Moving Average (3-Year and 5-Year): o Smooths short-term fluctuations. o 3-Year MSE: 20,721,759 (lowest error in simple models). o 5-Year MSE: Higher than the 3-year moving average, indicating less adaptability to the dataset.
- Regression Analysis (Linear): o Predicts salary using the equation: Salary=25077+9582×(Experience) o MSE: 1,152,850,273 due to cumulative error over multiple predictions.
- Trendline + Seasonal Adjustments: o Combines linear trends with seasonality indices. o MSE: 152,453,429 o Moderately effective in capturing variability but less precise than Winters' method.
- Solver Optimization (Linear Best Fit): o Adjust parameters to minimize error. o Results are not explicitly reported but improves linear model accuracy.
Error Analysis: • Best Method: Winters' Forecasting Method achieved the lowest MSE (27,439,356), making it the most accurate for this dataset. • Runner-Up: 3-Year Moving Average with MSE of 20,721,759, which is simpler but less robust than Winters' method. Rationale: • Winters' method captures both trend and short-term variability. • Moving Average is easier to implement but lacks adaptability to fluctuations in salary progression. Error Analysis Across Models: Model MSE Comments Winters' Forecasting 27,439,356 Best for dynamic trends and short-term changes. Moving Average (3-Year) 20,721,759 Simplistic but performs well for gradual trends. Exponential Smoothing (Single) 54,814,741 Too simplistic for linear salary growth. Linear Regression 1,152,850,273 High residual errors reduce reliability. Trendline with Seasonal Adj. 152,453,429 Better than regression but less accurate overall.
Winters' Forecast Results: The chart below illustrates how Winters' Forecast aligns closely with actual salary data. The model effectively predicts salary trends, accounting for fluctuations.
Linear Regression Line Fit: A regression plot demonstrates the upward trend of salaries with increasing years of experience.
Key Findings:
- Salary Trends: o A consistent upward trajectory with slight variability at key milestones (e.g., 5 and 8 years of experience). o Salaries increase significantly between 7 and 10 years, reflecting possible promotions or job role changes.
- Best Forecasting Model: o Winters' Forecasting Method provides the most accurate predictions for salary trends with the lowest error (MSE: 27,439,356). o The 3-Year Moving Average is a simpler alternative for non-seasonal gradual salary trends.
- Budget Implications: o Accurate forecasts enable better financial planning, ensuring funds are allocated efficiently for salary adjustments and retention efforts.
Optimize Compensation Structure: • Use the Winters' Forecasting Method to forecast future salary trends and adjust compensation plans. • Focus on ensuring competitive salaries at key milestones (e.g., 5, 7, and 10 years) to retain high-value employees. Strategic Budgeting and Workforce Planning: • Incorporate salary forecasts into annual budgeting to align salary expenses with business growth. • Develop targeted retention programs at critical career stages, including career development opportunities and tailored incentives.
The analysis of the dataset reveals a clear linear relationship between years of experience and salary progression. Winters' Forecasting Method proved to be the most effective tool for predicting salary trends, ensuring accurate financial planning and workforce management. These insights and recommendations enable organizations to optimize compensation structures, retain valuable employees, and stay competitive in the job market. By leveraging accurate forecasting models, businesses can align their operational goals with long-term talent strategies. Key Takeaway: Investing in predictive analytics like Winters' Forecasting equips organizations with the tools to stay competitive in the job market and maintain financial sustainability. Next Steps:
- Validate findings with a larger dataset incorporating additional variables (e.g., job roles, industry).
- Explore the integration of predictive analytics into HR management systems for real-time salary adjustments.
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