Expert Management System for Customer Inquiry Routing
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Solution Overview
Problem
Traditional customer service systems are costly to establish and maintain, and they often inefficiently route customer inquiries, leading to high response times and reduced customer satisfaction.
Innovation Solution
Implementing an expert management system that processes customer service inquiries by routing them to appropriate experts based on pre-classification, using automated response logic, internal, or external experts, and leveraging machine learning to optimize routing and response generation, thereby reducing the burden on contact centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional contact center systems are used to handle customer service inquiries, then customer service can be provided, but the system becomes expensive to establish and maintain
Solution Approach 1:
The system segments customer service inquiries into different types (informational vs. transactional) and routes them to appropriate handling channels (external experts vs. contact center), thereby reducing the burden on expensive contact center infrastructure while maintaining service capability
Solution Approach 2:
The patent introduces an intermediary expert management system that acts as a mediator between customers and the contact center, processing informational inquiries through external experts and only escalating transactional inquiries to the contact center, thus reducing overall system costs
2Reliability
If traditional routing methods are used in contact centers, then inquiries can be handled, but response times increase and customer satisfaction decreases
Solution Approach 1:
The system performs preliminary classification of inquiries before they reach the contact center, pre-processing informational inquiries through external experts and only escalating necessary cases, thereby reducing response times for the majority of informational inquiries
Solution Approach 2:
The patent changes the routing parameter from traditional queue-based FIFO (first-in-first-out) routing to classification-based routing, where inquiries are routed based on their type (informational/transactional) and required expertise, optimizing response times for different inquiry categories
Data Source
AI summary
In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service inquiries. Customer service inquiries and answers from a first enterprise and/or industry are optionally used to train an inquiry classifier for a second enterprise and/or industry. The classifier being configured to predict if a new customer service inquiry will require access to confidential information for a human to generate an answer that resolves the inquiry.