Asynchronous Messaging Session Detection
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Solution Overview
Problem
Asynchronous communication channels lack clear indications of session endings, making it difficult for contact centers to determine when interactions have concluded, leading to inefficiencies and challenges in managing customer interactions and agent allocation.
Innovation Solution
A system that analyzes customer interactions using content and metadata analysis to determine topic changes and engagement levels, allowing contact centers to intelligently route conversations to appropriate agents, whether human or chatbot, and manage agent workload effectively by identifying when a conversation is paused or completed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If asynchronous communication channels are used to enable flexible customer interactions, then customer convenience and accessibility are improved, but the ability to determine session endings and manage interactions efficiently deteriorates
Solution Approach 1:
The system implements feedback mechanisms by analyzing customer messages with AI to generate predictions about conversation status and topic changes. This feedback loop allows the contact center to automatically adjust agent assignments and manage interactions based on real-time analysis of communication patterns, resolving the complexity of managing asynchronous channels while maintaining customer convenience.
Solution Approach 2:
The system enables self-service by automatically analyzing messages, predicting conversation status, and routing interactions without requiring manual monitoring. The AI-driven analysis and automatic agent assignment allow the system to manage itself, reducing the operational complexity of handling asynchronous communications while preserving ease of customer interaction.
2Measurement precision
If manual monitoring of asynchronous conversations is implemented to determine session endings, then interaction management accuracy is improved, but time consumption and operational efficiency worsen
Solution Approach 1:
The system replaces manual mechanical monitoring with automated AI-driven analysis. The AI model processes messages and predicts conversation status automatically, eliminating the need for human operators to manually review each interaction. This substitution maintains high detection accuracy while dramatically reducing the time required for interaction management.
Solution Approach 2:
The AI analysis system acts as an intermediary between customer messages and agent assignment decisions. It processes and interprets communication patterns, generating predictions about session status and topic changes that guide automatic routing decisions. This intermediary layer provides accurate session detection without requiring direct human intervention in monitoring each conversation.
3Productivity
If AI-driven topic analysis is used to determine conversation status, then agent allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The AI system performs multiple functions including message analysis, topic identification, conversation status prediction, and routing recommendations within a single integrated platform. This multi-functional approach improves agent allocation efficiency while managing system complexity by consolidating various analytical tasks into one universal system rather than requiring separate specialized components.
Solution Approach 2:
The system performs preliminary analysis of customer messages using AI to predict conversation status and identify topic changes before agent assignment is required. This advance preparation of analysis results enables efficient agent allocation by having readiness information prepared beforehand, improving productivity while the automated nature of the preliminary action helps manage the complexity burden.
4Reliability
If conversation context is saved for extended periods to enable seamless resumption, then customer experience quality is improved, but resource consumption and system load increase
Solution Approach 1:
The system applies partial action by selectively saving and maintaining conversation context based on predicted continuity needs. Rather than universally preserving all conversation data indefinitely, the AI analysis identifies which conversations are likely to be resumed and prioritizes context retention for those cases. This approach maintains reliable conversation continuity for relevant interactions while reducing overall resource consumption by not retaining context for all conversations equally.
Data Source
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AI summary
Embodiments of the disclosure provide a method of processing messages received in an asynchronous communication system. In some embodiments, the method includes determining that a conversation is established with a customer communication device using an asynchronous communication channel, analyzing one or more messages exchanged over the asynchronous communication channel to determine a likelihood of communication disengagement, comparing the likelihood of communication disengagement with a predetermined disengagement threshold, and based on the comparison of the likelihood of communication disengagement with the predetermined threshold, updating a graphical user interface of an agent communication device being used to engage in the conversation.