So, you built and launched a machine learning model with a One AI recipe, congratulations! But the work doesn’t stop there. A model isn’t only about making predictions, it’s about using those predictions to understand your organization and take meaningful action. For example, identifying the main drivers of voluntary attrition can guide targeted retention strategies, while seeing how risk is spread across the business can help you step in early to reduce turnover.
In One AI, our visualization templates take predictions a step further by turning them into insights you can use. These visuals have been shaped through years of client work to make complex results clearer and easier to share with stakeholders. With templates that are tested and ready to use, you can spend less time explaining the output and more time acting on it.
We believe trust starts with transparency, which is why model performance is front and center. This visualization shows F1 Score, Precision, and Recall for both classes: employees who stay and employees who leave. Showing performance for each class is critical, especially in imbalanced datasets common in people analytics.
For instance, if only 10% of employees voluntarily leave and the model predicts everyone will stay, it might appear highly accurate overall (90% accuracy) but completely fail to identify those at risk, which is the point of the model. Breaking metrics down by class gives a true picture of effectiveness and helps you avoid relying on a model that should not be trusted.
What factors are pushing employees to leave, and what factors are keeping them engaged? The Drivers Visualization shows the most important influences on both sides and how much they matter. For example, in this chart, higher manager performance and longer tenure support retention, while frequent travel and higher job levels increase attrition.
With this view, you can take action to address the factors that drive people out and strengthen the ones that encourage them to stay. Because the models use SHAP, you can filter to any group, such as managers, the sales team, or employees in California, and the drivers will reorder to show what matters most for that group.
Can the same factor push some employees to stay while causing others to leave? The Feature Directionality and Impact visualization shows both the direction of influence and the strength of each factor’s contribution. For instance, salary increases might strongly support retention, while salary stagnation can signal a higher risk of attrition. This view helps you recognize the tradeoffs and complexities behind workforce behavior, so you can weigh interventions more carefully and avoid one-size-fits-all solutions.
Where are the pockets of highest turnover risk in your organization? This visualization groups employees into low, medium, and high risk and breaks that down by areas like department, tenure, gender, performance score, and more. For example, tenures or job levels may show a higher concentration of high-risk employees. This makes it easier to pilot retention strategies with smaller groups where the impact of intervention will be most immediate and impactful.
The geospatial map shows where turnover risk is concentrated across regions and locations. For example, you may see higher risk in certain countries or cities compared to others. This makes it easier to identify regional patterns and focus on location-specific strategies, whether the drivers are market conditions or internal practices.
This view provides predictions at the individual level, showing an employee’s likelihood of leaving and the factors influencing that outcome. While models are most reliable at the group level, individual results can still provide helpful context in specific use cases. Because of the sensitivity, this visualization should be tightly permissioned and used with care.
All these visualizations use SHAP (SHapley Additive exPlanations), a method that makes predictions easier to understand at both the individual and group level. For each individual data point, such as an employee, SHAP calculates how much each factor influenced the prediction. These individual explanations are then brought together to show the overall drivers of attrition and retention across the organization.
Because SHAP works at this individual, detailed level, you can filter results by any dimension, such as department, tenure group, or location. The visuals then adjust dynamically, helping you uncover trends and insights tailored to your organization’s needs.
The goal of these visualizations is not just to explain your data but to guide meaningful action. By seeing the main drivers of voluntary attrition, how they vary across groups, and where the highest risks sit, you can make decisions that directly support your organization’s goals. Use these tools to:
Machine learning should help you understand and shape your organization. The visualizations in One AI make sure you are not only building models but also building a stronger, more informed workplace.