Agent Action Ranking System for Customer Service Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Customer service systems face challenges in providing equally timely and efficient experiences across various communication channels, leading to user preference for human agents over automated agents, which can be inefficient for organizations.
Innovation Solution
A system that generates an agent action score based on task completion probability, considering factors like task complexity, user relationships, and speech analysis, to optimize interactions across human and automated agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated agents are used to handle customer service, then productivity increases and costs decrease, but service quality and user satisfaction worsen
Solution Approach 1:
The patent introduces an automated agent as an intermediary between the user and the human agent. The automated agent handles initial interactions, gathers information, and determines whether human intervention is needed. This mediator approach allows the system to maintain high productivity through automation while preserving service quality by escalating to human agents when necessary.
Solution Approach 2:
The system dynamically adjusts the level of automation based on the complexity and nature of the customer service request. Simple queries are handled entirely by automated agents, while complex or emotionally charged interactions are routed to human agents. This dynamic allocation optimizes both productivity and service quality based on real-time conditions.
2Reliability
If human agents are used to provide customer service, then service quality improves, but productivity decreases and operational costs increase
Solution Approach 1:
The patent segments customer service interactions into different levels: routine tasks handled by automated agents and complex tasks handled by human agents. This segmentation allows human agents to focus exclusively on high-value interactions that require empathy and complex problem-solving, thereby improving overall productivity while maintaining service quality.
Solution Approach 2:
The system enables self-service through the automated agent, which can independently handle a wide range of customer service tasks without human intervention. This self-service capability increases productivity by handling high-volume routine queries, allowing human agents to concentrate on tasks that truly require human expertise.
3Adaptability or versatility
If multiple communication channels are provided, then adaptability and user convenience improve, but system complexity increases
Solution Approach 1:
The automated agent is designed with universal capabilities to operate across multiple communication channels (voice, text, chat). This multi-functionality allows the system to provide adaptability and user convenience across different channels while maintaining a single, unified system architecture, thereby avoiding the complexity that would arise from implementing separate systems for each channel.
Data Source
AI summary
Method starts with processing, by processor, receiving audio signal of communication session between member-related client device and agent client device. Processor processes audio signal to generate caller utterances, each including audio caller utterance and transcribed caller utterance. Caller utterances includes first and second caller utterances. For each of the plurality of caller utterances, processor generates relationship data based on transcribed caller utterance, generates identified task based on transcribed caller utterance, and generates task completion probability result based on audio caller utterance, relationship data, and identified task, and stores the task completion probability result in a database. Processor computes agent action ranking score that is based on difference between task completion probability result of first caller utterance and task completion probability result of second caller utterance that precedes first caller utterance. Processor generates agent action result including the agent action ranking score. Other embodiments are disclosed herein.