Guided Support Identification System for Dynamic Agent Routing
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Solution Overview
Problem
Traditional customer service systems lack the ability for users to choose preferred agents, leading to inefficient routing of customer inquiries and potentially unsatisfactory support experiences due to limitations in agent availability and skill matching.
Innovation Solution
A guided support identification system that analyzes user interactions to recommend agents with the necessary skills and preferences, allowing users to select preferred agents for live support sessions, and updates agent ratings based on user feedback to improve customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional automated routing systems are used to distribute calls to available agents, then call volume can be handled efficiently, but users cannot choose their preferred agents leading to potentially unsatisfactory support experiences
Solution Approach 1:
The system performs preliminary actions by pre-establishing user preferences for specific agents before call routing occurs. User profiles are created and stored with preferred agent selections in advance, allowing the routing system to automatically connect users with their chosen agents without requiring manual selection during the call, thus maintaining ease of operation while managing system complexity.
Solution Approach 2:
A preference management module acts as an intermediary between the user and the call routing system. This intermediary layer handles the complexity of preference storage, retrieval, and validation, shielding the user from system complexity while enabling personalized routing. The intermediary translates user preferences into routing parameters that the automated system can process.
2Productivity
If the number of agents in the contact center is increased to handle more calls, then customer service capacity improves, but physical space limitations constrain the number of agents available
Solution Approach 1:
The system creates virtual copies of agent capabilities through detailed digital profiles that capture agent skills, expertise areas, and performance metrics. These digital profiles allow the routing system to effectively 'copy' and replicate agent capabilities across multiple routing decisions, enabling the system to handle more calls with the same physical agent pool by optimally matching calls to available agents based on their recorded capabilities.
Solution Approach 2:
The system transitions from a physical dimension constraint to a digital dimension solution by implementing virtual agent profiles and skills databases. This adds a digital layer that allows unlimited agent capability representation regardless of physical space, enabling the routing system to manage and allocate agent resources more efficiently to increase overall service capacity.
3Productivity
If agents are assigned based on skill level and availability, then call routing efficiency improves, but users lack control over selecting familiar or preferred agents
Solution Approach 1:
The system merges two previously separate routing criteria into a unified approach: automated skill-based matching and user preference selection. The preference management module combines user-stored preferences with real-time agent availability and skill data to generate routing decisions that satisfy both efficiency requirements and user control desires, allowing users to choose preferred agents while maintaining routing efficiency.
Solution Approach 2:
The routing system becomes dynamic by allowing user preferences to be stored and updated over time, rather than being static. The system adapts to individual user choices while maintaining the underlying skill-based routing logic, creating a flexible system that can respond to both automated efficiency requirements and individual user control needs in real-time.
Data Source
AI summary
User interactions with content presented during a particular browsing session are monitored in real-time during the browsing session. In response to different user interactions, content type of the content being interacted by the user is determined dynamically. A skill set is determined based on the content type within the same browsing session. Subsequently during the same browsing session, in response to a request from the user for connecting with an agent, a list of agents who possess the skill set is identified. A live communication session is established between a user device of the user and an agent device of an agent selected from the list.