Machine-Learning Supervisor Interface for Real-Time Agent Monitoring
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Solution Overview
Problem
Current systems lack real-time visibility into agent activities in call centers, hindering supervisors' ability to efficiently manage resources, address complex issues, and provide timely guidance, leading to suboptimal customer service quality.
Innovation Solution
A system and method utilizing machine learning models to analyze call data, generate problem and action profiles, and provide a dynamic interface for supervisors to monitor and manage agents in real-time, including agent workstations, supervisor workstations, and a server with modules for data processing, problem detection, and dynamic interface display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If supervisors manually monitor agent activities in call centers, then they can provide guidance to agents, but they lack real-time visibility into agent activities and cannot efficiently allocate resources
Solution Approach 1:
The patent replaces manual mechanical monitoring with an automated machine learning-based system. The system automatically analyzes agent activities, detects problems, and provides real-time visibility through a dynamic interface, eliminating the need for supervisors to manually monitor each agent while reducing information loss and improving response time.
Solution Approach 2:
The system enables self-service monitoring where the machine learning models automatically detect agent problems and generate alerts without requiring supervisor intervention for basic monitoring tasks. This allows supervisors to focus on higher-value activities while maintaining real-time visibility into agent performance.
2Reliability
If supervisors manually intervene to provide guidance to agents during complex issues, then they can improve customer service quality, but they cannot respond promptly due to lack of real-time observation
Solution Approach 1:
The system implements continuous feedback loops where machine learning models monitor agent activities in real-time, detect problems, and immediately alert supervisors through a dynamic interface. This feedback mechanism ensures supervisors receive timely information about agent struggles with complex issues, enabling prompt intervention to improve customer service quality.
Solution Approach 2:
The system performs preliminary detection and classification of agent problems using machine learning models before supervisors need to intervene. By pre-analyzing agent activities and identifying potential issues early in the interaction, the system prepares the ground for rapid supervisor response and improves overall response time.
3Productivity
If call centers increase the number of agents to handle surge in call volumes, then they can improve customer service capacity, but they cannot identify idle agents in real time to optimize resource utilization
Solution Approach 1:
The patent replaces complex manual resource management with automated machine learning-based monitoring. The system automatically tracks agent status, detects idle agents, and provides real-time visibility through a dynamic interface, simplifying resource management while supporting increased productivity and capacity during call volume surges.
Data Source
AI summary
The present invention discloses a system and method for providing real time dynamic interface for supervising individuals. The system is configured to monitor a plurality of agent data of each agent or individual in real time. The system is configured to analyze the call data and generate a call data set. The system is configured to analyze the call data set and generate a plurality of problem profiles. The system is configured to analyze the problem profiles to determine a plurality of actions for each problem profile and generate a plurality of action profiles for each problem profile. The system is configured to select at least one problem profile, determine at least one action profile for the selected problem profile and enables the system to implement the actions in the action profile. The system is configured to provide supervisors with recommended actions and real time graphical user interface to quickly monitor and engage with agents.