Insight-Based Routing for Help Desk Agent Assistance
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Solution Overview
Problem
Help desk agents often face challenges in efficiently resolving customer issues due to lack of expertise or communication inefficiencies, leading to inefficient processing and reduced customer satisfaction, as they may need to end calls or transfer customers without knowing the next agent's expertise or the customer's frustration level.
Innovation Solution
Insight-based routing is implemented, where real-time insights are generated during communications to determine if a support agent needs assistance, and a second agent is identified from a pool based on analysis of support agent data, including technical expertise and communication skills, to provide additional assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If agents transfer calls to another agent without knowing the customer's frustration level or the next agent's expertise, then call transfer is enabled, but customer satisfaction deteriorates and processing efficiency decreases
Solution Approach 1:
The system performs preliminary actions by collecting customer data, agent expertise data, and interaction history before the call transfer occurs. Real-time insights are generated about customer frustration levels and agent suitability, enabling informed routing decisions before the transfer happens, thus maintaining customer satisfaction while enabling call transfer.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring customer interactions, sentiment, and frustration levels in real-time. This feedback is used to dynamically adjust routing decisions, ensuring that transfers are made to appropriate agents based on current interaction quality and customer state, thereby maintaining reliability and satisfaction.
2Measurement precision
If agents are required to end calls and call customers back to obtain assistance, then expertise matching is improved, but processing efficiency and customer satisfaction deteriorate
Solution Approach 1:
The system performs preliminary matching of customer issues with appropriate agents using real-time data analysis and expertise profiling before the call is transferred or escalated. This preliminary action ensures that the right agent is identified in advance, eliminating the need for call endings and callbacks, thus maintaining both expertise matching accuracy and processing efficiency.
Solution Approach 2:
The system introduces an intermediary intelligent routing mechanism that acts as a mediator between customers and agents. This intermediary analyzes real-time interaction data, customer sentiment, and agent expertise to make automated routing decisions, replacing the manual process of ending calls and calling back with an automated intermediary system that maintains both accuracy and efficiency.
3Measurement precision
If real-time signal data is collected and evaluated to generate insights during communication, then routing accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the complex routing decision-making process into distinct modular components: signal data collection module, real-time evaluation module, insight generation module, and routing decision module. Each component handles a specific aspect of the process, reducing overall system complexity while maintaining high routing accuracy through specialized processing at each stage.
Solution Approach 2:
The system implements a universal intelligent routing platform that performs multiple functions: collecting signal data, evaluating interactions, generating insights, making routing decisions, and providing recommendations. This multi-functional system reduces complexity by consolidating what would otherwise require separate systems for each function, while maintaining high routing accuracy through integrated processing.
Data Source
AI summary
Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing during a communication of a help desk service. Real-time signal data is collected from a communication between a customer and a first support agent. The real-time signal data is evaluated to generate real-time insights for the communication. A determination is made as to whether the first needs assistance based on an evaluation of the real-time insights. A second support agent is identified to assist the first support agent based on a determination that the first support agent needs assistance. The second support agent is identified based on an application of a model that analyzes support agent data for a pool of support agents in correlation with the real-time insights. The second agent is then added to the communication to provide additional assistance in resolving a help desk case.