Satisfaction framework to determine exit

1. USING REGTRESSION TO PREDICT EXITS

2. Enhancing Retention through Satisfaction Scoring
Key Objectives:
• Satisfaction Framework: A composite metric to quantify employee satisfaction.
Approach:
• Use a weighted satisfaction score to predict employee exits.
Expected Outcomes:
• Identify areas impacting employee satisfaction.
• Develop actionable insights to improve retention.
Why Focus on Satisfaction?
• Low satisfaction directly correlates with higher turnover.
• A unified score simplifies analysis and decision-making.

3. Transforming Data into Numeric Values
Key Steps to Compute Satisfaction Score:
1. Identify Key Variables:
• Work Environment (e.g., employee feedback, team dynamics).
• Compensation (e.g., salary, bonuses, fairness perception).
• Growth Opportunities (e.g., promotions, learning opportunities).
• Work-Life Balance (e.g., hours worked, flexibility, commute time).
• Management (e.g., feedback quality, managerial support).
Followed by Data cleaning
2. Normalize Variables:
• Convert all Independent variables into Nominal, Ordinal, Interval and Ratio variables
• Example: Use Min-Max normalization for numeric fields like salary or hours worked:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data[['Salary', 'Hours_Worked']] = scaler.fit_transform(data[['Salary', 'Hours_Worked']])
3. Combine into Satisfaction Score:
• Assign weights to variables based on importance or by analyzing data of exit employees.
• Example Formula: (Tentative Independent variables, list can be exhaustive)
Satisfaction Score = 0.4 × Job Satisfaction + 0.3 × Salary + 0.2 × Growth Opportunities + 0.1 × Work-Life Balance
Outcome:
• A single, interpretable numeric metric for satisfaction.

4. Leveraging Satisfaction Scores for Prediction
Analyzing the Impact:
1. Satisfaction and Exit Probability:
• Model exit likelihood using satisfaction scores.
• Lower scores correlate strongly with higher turnover.
2. Example Insights:
• Employees with scores <0.5 are 3x more likely to exit.
• Key drivers: Low job satisfaction and lack of growth opportunities.
3. Visualization:
• Distribution of Scores: Compare exited vs. retained employees.
• Feature Importance: Highlight top contributors to satisfaction scores.
Actionable Steps:
• Focus retention efforts on low-scoring employees.
• Address key dissatisfaction factors (e.g., flexibility, growth).
• Regularly update satisfaction metrics for ongoing monitoring.