Machine Learning Capabilities

Advanced workforce predictions — now.

Who is at risk of resigning? Who should be considered for a promotion or a new role? Predictive analytics provide incredible opportunities to curb costs and improve performance, yet many are based on exclusion reporting instead of machine learning technology, or are unproven in the accuracy of their results.

Visier People™ comes pre-built with predictive analytics that leverage advanced machine learning techniques to make predictions based on your workforce data. Not only are Visier’s predictions proven to be up to 17 times more accurate than other methods, Visier People lets you validate past predictions. And, because your Visier People subscription includes onboarding of your historic or legacy system data, you can get up and running with accurate predictions right away.


Fact or Hype: Validating Predictive People Analytics and Machine Learning
How Visier Works:
Predicting Time-to-Fill
The State of Artificial Intelligence
in HR

Advanced machine learning.

Benefit from predictions that are up to 17 times more accurate than other approaches. Visier’s predictive engine uses random forest classification—a best-practice machine learning technique—and combines classification with regression.

Exits, promotions, internal movement.

Stay up-to-date on which employees should be considered for a promotion—and predict which employees are most likely to leave or change jobs. Predictions are continually trained on current employee data and events including promotions, resignations, and internal hires.

Prediction validation.

Increase the trust of your stakeholders by using Visier People to confirming exactly how accurate your past predictions were. Discover and report how close the number of actual exits, promotions, and internal moves were to their predicted values.

Precise configuration.

Easily configure and personalize exactly which employee attributes to use in training each predictive model. Select the attributes that are most appropriate for your organization, and remove attributes that could lead to unintended bias.

Exploring prediction metrics.

Use predictions as metrics as you explore employee insights and compare employee attributes. Filter and group predictions by range bands—so that you can see, for instance, which employees have a certain likelihood of resigning. Work with predicted values in the tool of your choice by exporting them to Excel or exposing them in a data connection.