Uncovering the Reasons Behind Workforce Departures: Forecast Employee Turnover Utilizing Clear-Cut AI (SHAP)
Employee attrition, the gradual loss of employees when positions are not refilled, is a common issue faced by many companies. According to Gartner, this phenomenon is often due to voluntary resignations, retirements, or internal transfers. In a company of 180 employees, for instance, it's estimated that 60 employees might resign in a year due to various reasons, including a lack of career development opportunities.
However, predicting and preventing employee attrition can help companies keep their best people and maximize profits. One tool that can significantly aid HR departments in this endeavour is SHAP (SHapley Additive exPlanations), a method and tool used to explain the output of Machine Learning (ML) models.
Using SHAP for Employee Attrition Analysis
SHAP offers several benefits for HR departments:
- Feature Interpretation: SHAP values help in understanding the contribution of each feature towards the prediction of employee attrition. This allows HR departments to identify critical factors such as job satisfaction, compensation, work environment, opportunities for growth, etc., that might influence an employee's decision to leave.
- Model Explainability: By quantifying the impact of each feature, SHAP explains why a particular employee is likely to leave. This insight can be used to target interventions more effectively.
- Data-Driven Decision Making: With SHAP, HR can make informed decisions based on data. For instance, if a model indicates that lack of opportunities for advancement is a significant predictor of attrition, HR can focus on providing more training and development programs to address this need.
- Personalized Strategies: SHAP can help tailor retention strategies to individual employees based on their specific circumstances. For example, if an employee is identified as being at risk due to low job satisfaction, HR can focus on improving their work environment or offering counseling.
- Forecasting and Prevention: By analyzing SHAP values over time, HR departments can forecast potential attrition trends and implement preventive measures before they occur.
Implementation Steps
To integrate SHAP into their predictive models, HR departments can follow these steps:
- Data Collection: Gather relevant data on employees, including demographic information, job history, performance metrics, and feedback.
- Model Development: Train machine learning models using this data to predict attrition. A fast and accurate model like XGBoost can be used for evaluation.
- SHAP Analysis: Use SHAP values to interpret the results and identify key factors contributing to attrition predictions.
- Intervention Strategies: Develop targeted retention strategies based on these insights. For example, if a model indicates that overtime work increases the risk of attrition, HR can reduce overtime or offer incentives to employees.
- Continuous Monitoring: Regularly update models and analyze SHAP values to ensure the effectiveness of interventions.
By integrating SHAP into their predictive models, HR departments can create more informed and effective strategies to prevent employee attrition. For instance, Jyoti Makkar, a writer and AI Generalist who co-founded WorkspaceTool.com, is using SHAP to help businesses find the best software for their needs, including HR analytics dashboards for attrition prediction and workforce optimization.
Sources: - Gartner - WorldMetrics - IBM
- The incorporation of SHAP (SHapley Additive exPlanations) in Machine Learning (ML) models can aid HR departments in understanding the factors contributing to employee attrition, such as job satisfaction, compensation, work environment, opportunities for growth, and more.
- With SHAP analysis, HR can make data-driven decisions to implement personalized retention strategies, like focusing on improving work environments for employees identified as being at risk due to low job satisfaction.
- By constantly updating ML models and analyzing SHAP values, HR departments can forecast potential attrition trends and prevent employee attrition, ensuring they keep their best employees and maximize profits in the realm of data-and-cloud-computing and technology-driven education-and-self-development.
){kind=link}