Voice-Based Agent Coaching System Using Real-Time Sentiment Analysis
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Solution Overview
Problem
Current systems lack effective methods to provide real-time intelligent assistance to agents during customer interactions, failing to adequately utilize sentiment analysis and context-specific resource identification to enhance customer support and agent coaching.
Innovation Solution
An Information Handling System (IHS) that processes audio inputs to identify sentiment and resources, using language models and sentiment indications to generate instructions for coaching and resource allocation, enabling context-based intelligent assistance and agent support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sentiment analysis and resource identification are implemented during customer communications, then customer support quality and agent coaching effectiveness are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system performs sentiment analysis and resource identification during the communication process itself, rather than analyzing recorded communications afterward. This preliminary action during the interaction enables real-time coaching recommendations and resource allocation, improving support quality without requiring complex post-processing systems.
Solution Approach 2:
The system introduces an intermediary coaching layer that analyzes communication data and provides recommendations to agents in real-time. This intermediary component handles the complex analysis separately from the core communication system, allowing sentiment analysis and resource identification to enhance support quality while isolating system complexity to a dedicated coaching module.
2Measurement precision
If multiple language models are brokered to improve text recognition accuracy, then measurement precision of audio transcription is improved, but processing time and computational resources increase
Solution Approach 1:
The system brokers multiple language models but does not require all models to complete full processing of every utterance. Instead, it uses partial action by selecting appropriate models based on context, using fewer models for straightforward cases and engaging multiple models only when higher precision is needed, thus maintaining accuracy while reducing average processing time.
Solution Approach 2:
The system dynamically selects and brokers language models based on the specific communication context, sentiment detected, and resource identification needs. This dynamic approach allows the system to adjust processing intensity in real-time, using more computational resources when precision is critical and fewer resources during routine interactions, balancing accuracy and processing time adaptively.
Data Source
AI summary
Systems and methods for providing intelligent assistance using voice services for agent coaching. In some embodiments, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive an instruction from a voice service provider in response to audio captured during a communication between a customer and an agent, where the instruction includes a sentiment indication; identify a resource used by the agent during the communication; determine a result of the communication; and store the sentiment indication, the resource identification, and the result of the communication, in a coaching database.