Abstract: In some examples, a server determines, based on a portion of aggregated data, a plurality of metrics associated with an employee. The aggregated data includes activities performed by the employee using a computing device. The server determines, based on the plurality of metrics, a unified metric associated with the employee. The server determines a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees. The server determines a location of the employee on the distribution curve. The server predicts, using a plurality of artificial intelligence engines executing on the server and based on the location of the employee on the distribution curve, a next best action for the employee and sends the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.

Abstract: One or more monitoring servers are connected via a data link or API to a server system. The monitoring servers may operate one or more monitoring services that monitor the agent computing devices and agent displays to collect metrics. The server system processes recommendations and auto-delivering actions based on the monitored data of each employee and a variance against a high performer persona. The actions and recommendations are optimized by machine learning logic to improve the agent's performance in one or more metrics. The server system connects to one or more agent computing devices and/or agent displays to auto-deliver the actions.

Abstract: In some examples, a server determines, based on a portion of aggregated data, a plurality of metrics associated with an employee. The aggregated data includes activities performed by the employee using a computing device. The server determines, based on the plurality of metrics, a unified metric associated with the employee. The server determines a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees. The server determines a location of the employee on the distribution curve. The server predicts, using a plurality of artificial intelligence engines executing on the server and based on the location of the employee on the distribution curve, a next best action for the employee and sends the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.

Abstract: In some examples, a server determines, based on aggregated data, metrics associated with an employee. The aggregated data includes activities performed by the employee using a computing device. The server determines, based on the metrics, a unified metric associated with the employee. The server determines a distribution curve based on the unified metric associated with the employee and additional unified metrics associated with additional employees and determines a location of the employee on the distribution curve. The server predicts, using multiple artificial intelligence engines, a next best action to improve a future performance of the employee. The server determines, a predetermined time interval after the particular data, a second unified metric associated with the employee. If the second unified metric is greater than the first unified metric, the server determines that the coaching session was successful and increases a coaching effectiveness metric associated with the coach.

Abstract: One or more monitoring servers are connected via a data link or API to a server system. The monitoring servers may operate one or more monitoring services that monitor the agent computing devices and agent displays to collect metrics. The server system processes recommendations and auto-delivering actions based on the monitored data of each employee and a variance against a high performer persona. The actions and recommendations are optimized by machine learning logic to improve the agent's performance in one or more metrics. The server system connects to one or more agent computing devices and/or agent displays to auto-deliver the actions.

Abstract: The present disclosure relates to management of employees that allows for ranking via balanced scorecard, heatmap based on performance to goal, with integrated processes and systems to allow managers to improve performance for any specific category or metric via system generated improvement knowledge base suggestion and tracking of timing and actual metric of improvement. Data and tracking gathered with this method can provide innovative predictable forecasting of future performance based on manager, product or company level statistics.