Job-Fit Neurometric is organizational neuro-analytics modeling that reveals the brain signature of potential high-performing talents. It predicts the future performance of talents or leaders based on their brain scores. Using machine learning, Job-Fit Neurometric transforms neuroelectrical brain data into a predictive performance map of people within the organization. It reveals:

1. Predictive Performance, which displays an organizational brain map of high and low performers across job functions and/or managerial levels

2. Brain Signatures, which explains the neuro-behavior characteristics that distinguish high performers and low performers in the organization

3. Potential Functions, which suggests potential job functions that are most possible for each talent to perform well in the organization

The machine-learning algorithm of Job-Fit Neurometric separates the high performers from low and medium performers based on brain patterns underlying motivation, goal-setting, cognitive processing, problem-solving ability, and emotional regulation. It had been tested across industries with 78% predictive accuracy.

Grounded in cognitive neuroscience research, it provides a scientific measurement that allows your organization to discover:

• what are the brain signatures of high-performing talents in specific job functions or a particular layer of managerial levels

• what are the brain functions or systems of low or medium performers that need to be improved, altered, or optimized to be able to perform better

• why some talents tend to perform higher while others tend to perform lower by understanding how their brain works

• how to lead more effectively to enable talents to achieve peak performance considering their predominant neuro-behaviors