Insight-Based Routing for Help Desk Service Requests
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Solution Overview
Problem
The inefficiency in connecting help desk agents with customers due to unfamiliarity and the lack of awareness about customer frustration levels leads to inefficient processing and reduced customer satisfaction.
Innovation Solution
Insight-based routing is implemented, where insights are generated from analyzing customer-specific data and support agent data to match the most suitable agent with the customer, considering factors like technical proficiency, communication skills, and customer satisfaction risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing methods are used to connect agents and customers, then the system is simple to operate, but processing efficiency is low and customer satisfaction is reduced due to unfamiliarity between agents and customers
Solution Approach 1:
The system performs preliminary analysis of customer data (frustration levels, technical proficiency, communication preferences) and agent data (technical expertise, communication skills, availability) before routing occurs. This advance preparation enables efficient matching without adding operational complexity during the actual routing process.
Solution Approach 2:
An intermediary routing system is introduced that acts as a mediator between customers and agents. This intermediary analyzes multiple factors including customer frustration levels, technical proficiency, and agent expertise to make intelligent matching decisions, thereby improving efficiency without requiring direct complex interactions between customers and agents.
2Reliability
If agents are randomly assigned to customers, then the routing process is simple, but customer satisfaction decreases due to lack of awareness about customer frustration levels and agent compatibility
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor customer frustration levels, interaction outcomes, and agent performance. This feedback is used to refine routing decisions, ensuring that customers are consistently matched with appropriate agents based on real-time and historical data, thereby improving reliability of customer satisfaction.
Solution Approach 2:
The system performs preliminary evaluation of customer frustration levels, technical proficiency, and agent compatibility before routing decisions are made. By analyzing these factors in advance, the system ensures reliable matching that improves customer satisfaction without requiring complex real-time evaluations during interactions.
3Ease of operation
If detailed analysis of customer and agent data is performed to improve matching quality, then customer satisfaction and processing efficiency improve, but the complexity of data analysis and model application increases
Solution Approach 1:
The data analysis process is segmented into distinct components: customer data analysis (frustration levels, technical proficiency, communication preferences), agent data analysis (technical expertise, communication skills, availability), and matching logic. This segmentation makes the complex analysis manageable and enables targeted improvements in interaction quality without overwhelming system complexity.
Solution Approach 2:
The routing system is designed to perform multiple functions simultaneously: analyzing customer frustration levels, evaluating technical proficiency, matching communication preferences, and assessing agent availability and expertise. This multi-functionality achieves high interaction quality through a single integrated system rather than multiple separate complex systems.
Data Source
AI summary
Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing of a service request through a help desk service. A request for support (e.g. technical support) can be received through a modality of a help desk service. The request is evaluated, where an evaluation of the request comprises analyzing an issue associated with the request as well as user-specific signal data associated with a customer and generating insights. A support agent is matched to the customer based on an evaluation of the request. The support agent is selected from a pool of support agents based on application of a model that analyzes support agent data in correlation with the generated insights. An interaction between the matched support agent and the customer may be initiated through a modality of the help desk service.