Strengths and Limitations of the Predictive Models

Learn more about the strengths and limitations of the predictive models.

Strengths

Visier's data scientists are working continuously to improve the predictive strength of the machine learning algorithm for all customers. We measured the predictive success of the risk of resignation model by looking at the predictions we made for all our clients. For each client, we looked at the employees who we predicted as having the highest likelihood of resigning and determined how many of them actually resigned in the following year. The results show that Visier's risk of resignation predictions can be 17 times more accurate than guesswork. The average predictive success for all clients was measured at 5 times more accurate than guesswork.

The reasons why some of our customers do much better than the rest is due to:

• Data volume: The more information that is available the better the predictions.
• Customer specific differences: Some industries and locations have a more predictable employee turnover behavior.

Limitations

• Properties with more than 50% missing values are excluded from the predictive models.

As a best practice we also recommend that you exclude any sensitive data and backdated data. Including sensitive data such as an employee'zs age or gender may inadvertently reinforce unconscious biases in recruitment or retention. Backdated data may make your predictions less accurate because you are including information in the predictive algorithm that was not actually available.