Examining Staff Departures: Forecast Employee Turnover Using Clarifyable Artificial Intelligence (SHAP)
The SHAP (SHapley Additive exPlanations) approach is making waves in the realm of human resources, offering a novel way to predict employee attrition and, more importantly, understand the factors driving this trend.
By quantifying the contribution of each feature to the prediction, SHAP helps identify the key drivers of individual employees' likelihood to leave. This level of explainability uncovers nuanced factors such as job satisfaction, workload, promotion history, or demographic factors that increase attrition risk for specific employees.
Proactive Retention Strategies with SHAP Insights
HR departments can leverage these insights from SHAP explanations to prevent valuable employees from leaving. Here's how:
- Targeted Interventions: Instead of broad, one-size-fits-all programs, HR can tailor retention efforts based on the top attrition drivers for at-risk employees. For instance, if SHAP highlights lack of promotion or low recognition as critical for particular employees, HR can prioritize career development or recognition initiatives for them.
- Proactive Engagement: SHAP reveals early warning signs embedded in complex data patterns, enabling HR to engage employees proactively before dissatisfaction leads to resignation.
- Improving Overall Employee Experience: By aggregating SHAP insights across the workforce, HR can identify systemic issues in onboarding, workload distribution, or leadership impacting attrition and fix those root causes.
- Data-Driven Decision-Making: SHAP provides transparent explanations of black-box predictive models, which helps HR leaders trust and act on AI-driven attrition predictions confidently rather than relying on intuition.
The SHAP Approach: More Than Prediction
In summary, SHAP empowers HR to move beyond simply predicting who might leave to understanding why they might leave, thereby enabling more effective, targeted retention strategies that preserve organizational knowledge and reduce costly turnover. This approach aligns with the broader trend in HR towards data-driven talent management using explainable AI and predictive analytics to enhance employee engagement and retention outcomes.
Key Findings
- Employees working overtime are more likely to leave.
- Low job and environment satisfaction increase the risk of attrition.
- Monthly income also has an effect, but less than OverTime and job satisfaction.
A Call to Action
Predicting employee attrition can help companies keep their best people and help to maximise profits. With the SHAP tool, HR can take action before it's too late and create a backup/succession plan.
Jyoti Makkar, a writer and AI Generalist, co-founded WorkspaceTool.com, a platform to discover, compare, and select the best software for business needs. For HR departments looking to implement the SHAP approach, consider revisiting compensation plans, reducing overtime or offering incentives, improving job satisfaction through feedback, and carrying forward a better work-life balance notion.
- By utilizing machine learning techniques such as SHAP, finance and business sectors can enhance their HR strategies, particularly in retaining valued employees.
- The SHAP approach, along with data-and-cloud-computing and technology, can provide HR departments with insights that go beyond prediction, enabling proactive measures to improve employee satisfaction, reduce turnover, and increase overall business profitability.
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