Through machine learning techniques (supervised learning), we discovered patterns that allow us to predict behaviors.

The People Science team tests a series of statistical models that process large volumes of information, allowing for a clearer interpretation of how the score of the indicator being studied behaves according to the sentences or dimensions.

The results show us which significant behavioral patterns are present in the various sentences or dimensions and the indicator results that the collaborators respond to.

These patterns enable us to identify which sentences or dimensions are the most important (ranking of impact) in predicting a certain indicator.

By taking into account the favorability of the sentences or dimensions and their place in the impact ranking, we recommend a level of priority for actionable steps.