Context-Based Routing for Multi-Tenant Support Systems
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Solution Overview
Problem
In multi-tenant computer systems, tenants face difficulties in adding and engaging with services due to cumbersome and technically complex processes, often requiring multiple support agent transfers, leading to user dissatisfaction.
Innovation Solution
A context-based routing system that gathers context information and engagement state data to identify issues and route users to suitable support agents, ensuring quick and effective support by matching issues with qualified agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technical support requests are routed to individual technicians without context analysis, then the routing process is simple and fast, but the problem identification accuracy deteriorates leading to multiple transfers
Solution Approach 1:
The system performs preliminary analysis of context information and engagement state data before routing the support request. This advance preparation includes gathering tenant engagement metrics, service usage patterns, and historical support data to pre-identify the most suitable technician, thereby improving problem identification accuracy without adding complexity to the actual routing execution
Solution Approach 2:
The routing system automatically analyzes context information and engagement states to self-determine the appropriate technician assignment. This automated self-service approach eliminates manual intervention in the routing decision process, maintaining simplicity while improving accuracy through systematic analysis of available data
2Reliability
If multiple support agent transfers are performed to correctly identify the issue, then the problem resolution accuracy improves, but the time required and user satisfaction deteriorate
Solution Approach 1:
The system gathers context information and engagement state data in advance before the support request is processed. This preliminary action includes collecting tenant service usage patterns, historical engagement metrics, and relevant system data to pre-identify the correct technician, thereby achieving accurate problem resolution on the first contact without requiring multiple transfers
Solution Approach 2:
The system uses historical engagement state data and context information as feedback to continuously improve routing accuracy. By analyzing past support interactions, engagement patterns, and problem resolution outcomes, the system refines its routing decisions to ensure correct technician assignment from the first contact, reducing the need for repeated transfers
3Measurement precision
If context information and engagement state data are gathered and analyzed, then the issue identification accuracy improves, but the processing complexity increases
Solution Approach 1:
The engagement state identification system performs multiple functions using a single integrated approach. It simultaneously analyzes context information, determines engagement states, identifies issues, and routes requests - consolidating what could be separate complex processes into one unified system that improves accuracy without proportionally increasing complexity
Solution Approach 2:
The system automatically processes context information and engagement state data through self-service mechanisms. The engagement state identification system autonomously analyzes the gathered data, correlates it with issue patterns, and determines routing decisions without requiring external intervention, thereby managing processing complexity through automated self-service
Data Source
AI summary
Context information, indicative of a tenant's engagement with a multi-tenant service, is obtained. An engagement state for the tenant is determined and the context information, and engagement state, are correlated to an issue to be addressed. A user experience is conducted, based upon the likely issue to be addressed.