Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: Systems for call processing access and provide structured data about a customer to a representative, where the data is configured and presented through an optimized network are provided. The disclosed call processing systems provide a tailored, interactive graphical user interface (GUI) that provides access to specific user-focused internal and external data resources, presented to the representative, to allow the representative to drive conversations and address the customer's needs quickly.

Abstract: Systems for call processing access and provide structured data about a customer to a representative, where the data is configured and presented through an optimized network are provided. The disclosed call processing systems provide a tailored, interactive graphical user interface (GUI) that provides access to specific user-focused internal and external data resources, presented to the representative, to allow the representative to drive conversations and address the customer's needs quickly.

Abstract: A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

Abstract: Systems for call processing access and provide structured data about a customer to a representative, where the data is configured and presented through an optimized network are provided. The disclosed call processing systems provide a tailored, interactive graphical user interface (GUI) that provides access to specific user-focused internal and external data resources, presented to the representative, to allow the representative to drive conversations and address the customer's needs quickly.

Abstract: Systems for call processing access and provide structured data about a customer to a representative, where the data is configured and presented through an optimized network are provided. The disclosed call processing systems provide a tailored, interactive graphical user interface (GUI) that provides access to specific user-focused internal and external data resources, presented to the representative, to allow the representative to drive conversations and address the customer's needs quickly.