Background:
A large multinational corporation, was grappling with a high employee turnover rate that was negatively impacting their operations and profitability. They faced the challenge of retaining top talent and reducing the costs associated with recruitment and training. To address this issue, The company embarked on a predictive modeling initiative to identify and proactively address factors contributing to employee turnover.
Challenges:
- High Turnover: The company had been experiencing a consistently high employee turnover rate, particularly among their high-performing employees.
- Cost Implications: The costs associated with recruitment, onboarding, and training new employees were substantial, impacting the company’s bottom line.
- Retention Strategy: The comany needed to develop a targeted employee retention strategy but lacked insights into the underlying causes of turnover.
Strategy:
The strategy involved implementing predictive modeling techniques to analyze historical employee data and identify factors contributing to turnover. Here’s how they approached the challenge:
1. Data Collection:
- Gathered historical employee data, including employment history, performance evaluations, salary information, and exit interviews.
- Collected data on various external factors, such as industry benchmarks and economic indicators.
2. Feature Engineering:
- Identified relevant features and engineered new variables that could potentially impact turnover, such as job satisfaction scores, time since last promotion, and commute times.
3. Predictive Modeling:
- Employed machine learning algorithms, including logistic regression and random forests, to build predictive models.
- Trained models using historical data to predict which employees were at the highest risk of leaving the company within the next six months.
4. Interpretation and Action:
