Reason Behind Employee Departures: Forecast Employee Turnover Using Transparent AI (SHAP)
In the ever-evolving world of business, employee retention has become a critical concern for HR departments. A new approach, utilising the IBM HR Analytics Employee Attrition dataset, is proving to be a game-changer in this area. This dataset, containing information about over 1400 employees, including age, salary, job role, satisfaction scores, and more, is being used to identify the root causes of employee attrition.
By harnessing the power of analytics, HR can now discover historical employee data model patterns, demographics, and demystify the factors leading to employee turnover. This valuable insight allows companies to design targeted actions to improve retention rates.
One of the key tools in this analysis is SHAP (SHapley Additive exPlanations), a method used to understand which features or factors were most important in predicting attrition. SHAP dependence plots or seaborn visualisations of Attrition versus Over Time, for instance, help dig deeper into the data.
The SHAP approach follows a systematic process: Load and Explore the Data, Understand the Problem, Build the ML Model, Interpret the Results using SHAP, and Revise Plans based on the insights. This methodology helps HR take action before it's too late.
SHAP analysis effectively highlights the most important features influencing attrition. In employee attrition models, job satisfaction, age, and years with the current manager have emerged as the top predictors of whether an employee is likely to leave. This helps managers focus on targeted factors to improve retention.
Moreover, SHAP values can be summed across all features of a single individual to estimate their personalised risk of attrition, facilitating tailored interventions for employees based on their unique profile.
SHAP also reveals the multifaceted nature of attrition, as it is influenced by a combination of individual, demographic, and environmental factors. In a healthcare setting, factors like race, commute times, and community deprivation index have been found to impact attrition, underscoring that addressing retention requires a holistic approach beyond just workplace variables.
By quantifying the contribution of each factor to attrition risk, SHAP empowers managers to prioritise interventions on modifiable attributes such as improving job satisfaction, strengthening manager-employee relationships, or addressing specific employee concerns to reduce turnover.
Predicting employee attrition can help companies keep their best people and help to maximise profits. In 2024, 33% of employees left their jobs due to a lack of career development opportunities. By using the SHAP approach, companies can create a backup or succession plan, ensuring a smoother transition when an employee does decide to leave.
In conclusion, the SHAP approach provides transparent, interpretable insights into both global and individual drivers of employee attrition, enabling data-driven, personalised, and effective strategies to mitigate turnover risk. This innovative method is set to revolutionise HR practices, making them more proactive, targeted, and ultimately, more successful.
Machine learning in the field of finance and business is being utilised by HR departments to tackle the challenge of employee retention. SHAP, a method from data-and-cloud-computing technology, is proving to be a crucial tool in this analysis. By using SHAP, companies can identify the top predictors of employee attrition, such as job satisfaction, age, and years with the current manager, allowing them to address these factors and improve retention rates. Moreover, SHAP can quantify the contribution of each factor to attrition risk, empowering managers to prioritise interventions and reduce turnover, thereby maximising profits. Finally, the SHAP approach is set to revolutionise HR practices by providing transparent, interpretable insights into employee attrition, making them more proactive, targeted, and ultimately, more successful.
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