Contact Manager System Aggregating Customer Context for Agent Guidance
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Solution Overview
Problem
Contact centers face challenges in optimizing resource utilization and customer service efficiency due to incomplete information provided to agents during customer interactions, which can lead to suboptimal handling of incoming contacts and difficulty in managing high-value customer relationships.
Innovation Solution
A computer-implemented method that aggregates contextual information from WebRTC sessions to guide agents, including generating and displaying aggregated context information based on customer interactions, enabling agents to make informed decisions and improve handling of incoming contacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional techniques are used to route calls to agents, then call routing is simple and fast, but information provided to agents is incomplete leading to suboptimal handling
Solution Approach 1:
The system performs preliminary aggregation of customer interaction information from multiple sessions and agents before the customer contacts the contact center again. This pre-processing ensures that when the customer arrives, the agent receives comprehensive information immediately, eliminating the need for complex real-time information gathering during the interaction.
Solution Approach 2:
The patent introduces an intermediary system that sits between the routing mechanism and the agent interface. This intermediary automatically collects, aggregates, and presents relevant customer information from various sources (multiple sessions, multiple agents, different communication channels) without requiring the agent to manually search for information, thus resolving the contradiction between information completeness and system complexity.
2Productivity
If agents handle multiple customer sessions manually, then flexibility is maintained, but time consumption increases and efficiency decreases
Solution Approach 1:
The system enables self-service by automatically providing agents with aggregated customer information from previous interactions. The agent's workstation autonomously retrieves and displays relevant data without requiring the agent to manually search databases or contact previous agents, thus eliminating time loss while maintaining handling flexibility.
Solution Approach 2:
The system implements feedback loops where information from completed customer sessions is automatically captured, stored, and fed back to agents in subsequent interactions. This continuous feedback mechanism ensures agents receive up-to-date customer information instantly, improving productivity without consuming additional agent time.
3Reliability
If contact centers optimize for service level and match rate, then customer service quality improves, but resource utilization optimization becomes difficult
Solution Approach 1:
The system dynamically adjusts resource allocation based on aggregated customer information and interaction patterns. By analyzing accumulated data from multiple sessions, the system can adaptively route customers to appropriately skilled agents, optimizing both service quality and resource utilization. The routing criteria evolve dynamically based on learned patterns rather than static rules.
Data Source
AI summary
Contact manager computer system and method to dynamically generate an aggregated context information, including: a monitoring module configured: to monitor a communication session with a customer; to determine one or more communication contexts of the customer; to determine one or more communication contexts of an agent assisting the customer; to obtain customer context information from the one or more determined communication contexts of the customer; to obtain agent context information from the one or more determined communication contexts of the agent; a processor coupled to a memory, the memory configured to store context information under control of the processor; an aggregated context information generation module to generate the aggregated context information from the obtained customer context information and the obtained agent context information; an inference module to create an inference from the aggregated context information; and a display module to display a result of the inference to an agent.