There are massive hidden costs associated with managing a workforce. Human resource teams that have a strong understanding of an organization’s employees represent significant cost savings for their company. Data science can help these teams better understand the relationship between salary, vacation, and other employee attributes and how they impact attrition, recruitment, and employee engagement. This dashboard assists HR professionals as they analyze the attrition profiles of different groups and predict how managers or company policies will impact attrition. The dashboard leverages a single data science model to produce results, but the unique implementation of the model is what provides the most value to the company.
First and foremost, the analysis helps HR teams understand the current state of attrition likelihood. While that insight is helpful to some extent, it does little in helping human resource organizations intervene and proactively address employee retention. For example, the 21-30-year-old age group at this organization has a relatively high predicted attrition rate. Managers of this group will be interested to know what levers they can pull to better retain this group of employees, many of which represent future leadership for the company.
Suppose a manager selects “Salary” on the “Change Type” dropdown, knowing that a 2-10% salary increase is within budget for a number of these employees. After running the model, we see how salary increases in this range impact the attrition likelihood for this group. In this case, a manager will likely decide to offer a salary increase of 2-3%, noting that this increase predicts a relatively large decrease in attrition rate. This manager eliminates the guesswork of salary increases. With this tool, management can achieve financially responsible salary increases for this group and be confident in their ability to retain talent.
Data science up-levels this dashboard by showing how changes to the employee experience impacts attrition likelihood. This ability to test important levers like salary and vacation is what makes this dashboard and the model that supports it so valuable. With data science, HR teams move away from guesswork that does little to retain talent and focus on data-driven intervention strategies that decrease attrition.
Explanation of dashboard use
Left Hand Bar Charts
This section shows model results across employee groups. This provides visibility into groups of people within the company that are more likely to leave. Leading to more targeted efforts to retain talent in specific groups, if desired.
1 year/2 years toggle
The year toggle allows dashboard users to see attrition likelihood within 1- or 2-year timeframes. This is helpful for HR professionals as they consider when retention efforts should be made in order to retain an employee or group of employees.
Scenario testing section
This feature allows dashboard users to understand how they can impact attrition through salary, vacation, medical benefits, and 401K contributions. This flexibility allows users to capture the organization’s budget and policies. The model runs in the background to produce results, which are shown to the right.
As dashboard users adjust salary, vacation, medical benefits, and 401K contributions, they consume the results of those adjustments on this chart. A zero percent increase demonstrates the current average likelihood of employees leaving. The x axis is dynamic based on user entry as well. With this functionality, users can change specific increments of retention efforts and identify the most financially responsible way to retain talent.
These boxes give a high-level profile of attrition at the company by reporting the number of employees that have different attrition likelihoods. This snapshot provides dashboard users a descriptive overview of their workforce, which can be helpful as retention efforts are planned and executed.