Sentiment Detection for Customer Service Queue Routing
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Solution Overview
Problem
Current customer support systems lack real-time feedback and context during interactions between customers and agents, leading to inefficiencies in managing customer service queues and agent allocation.
Innovation Solution
A method using facial recognition analysis to determine the emotional state of customers and agents during video calls, which informs the decision to transfer calls and provides sentiment scores for real-time feedback and queue management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If facial recognition analysis is performed in real-time during video calls, then real-time feedback and sentiment detection are improved, but device complexity and processing requirements increase
Solution Approach 1:
An interaction management computing device is introduced as an intermediary between customers and agents. This mediator performs facial recognition analysis, determines emotional states, and manages service queue insertion, thereby capturing interaction context without requiring complex processing in the customer or agent devices themselves.
Solution Approach 2:
The patent replaces manual analysis of interaction context with automated facial recognition technology. The computing device uses image processing and emotional state detection algorithms to automatically analyze customer and agent emotions, substituting what would otherwise require human observation and interpretation.
2Productivity
If emotional state detection is used to optimize call transfers, then customer service quality is improved, but measurement precision requirements increase
Solution Approach 1:
The system continuously monitors emotional states of both customers and agents during interactions and uses this feedback to dynamically manage service queues and call transfers. The interaction management computing device adjusts routing decisions based on real-time emotional data, improving service quality while managing the precision requirements through continuous adaptation.
3Reliability
If real-time monitoring of customer-agent interactions is implemented, then service quality management is improved, but loss of time for processing increases
Solution Approach 1:
The system performs facial recognition analysis and emotional state determination as calls are being routed and during idle moments in the interaction flow. By preparing emotional state assessments in advance and during natural pauses, the system minimizes processing time while maintaining continuous monitoring capability for service quality management.
Data Source
AI summary
Technologies for monitoring interactions between customers and agents include an interaction management computing device communicatively coupling a customer computing device and an agent computing device to facilitate a support call interaction. The interaction management computing device is configured to receive a video call from a customer and perform a facial recognition analysis of the customer based on images of the customer received with the video call. Additionally, the interaction management computing device is configured to determine a probable emotional state of the customer as a function of the facial recognition analysis of the customer and insert the video call into a service queue as a function of the probable emotional state of the customer. Additional embodiments are described herein.