Intent-Based Service Request Routing Using Machine Learning
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Solution Overview
Problem
Current contact center and conversational AI systems are inadequate in routing service requests to live agents efficiently, as they fail to determine friction in automated dialogues, match high-priority chats with skilled agents, and adjust service levels based on intent urgency, leading to suboptimal chatbot and human interaction.
Innovation Solution
A method and system that utilize machine learning models to analyze service requests, determine intents, and adjust service levels dynamically, ensuring that service requests are routed to the most appropriate agents based on skill sets and urgency, thereby optimizing chatbot and human interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If service requests are routed to live agents using traditional methods, then agents can handle customer issues, but wait times increase and service quality decreases due to lack of intent-based prioritization
Solution Approach 1:
The system performs preliminary analysis of service requests using machine learning models to determine intent and priority before routing to agents. This preliminary action enables proactive prioritization and reduces wait times by ensuring high-priority requests are handled first, thereby resolving the contradiction between wait time and service efficiency.
Solution Approach 2:
The system continuously monitors service request characteristics and agent performance, using this feedback to refine routing decisions. By analyzing real-time data on request urgency, intent, and agent availability, the system optimizes the balance between reducing wait times and maintaining high service quality through adaptive routing strategies.
2Ease of manufacture
If all service requests are handled by chatbots, then operational costs decrease, but customer experience deteriorates when friction in automated dialogues is not detected
Solution Approach 1:
The machine learning models continuously analyze chatbot interactions to detect friction points and customer sentiment. When friction is detected, the system automatically triggers transfer to live agents, ensuring optimal balance between cost-effective chatbot operation and high-quality customer experience by using feedback loops to monitor and adapt the service delivery mode.
Solution Approach 2:
The system introduces an intermediary layer of intelligent routing that mediates between chatbot and live agent channels. This intermediary component assesses request characteristics in real-time and makes informed decisions about the appropriate service channel, enabling seamless transition between automated and human-based support based on detected customer needs and friction levels.
3Device complexity
If transfers to live agents are implemented as blind transfers, then implementation complexity is reduced, but service quality decreases due to lack of intent-based matching with skilled agents
Solution Approach 1:
The system performs preliminary characterization of service requests using machine learning models to identify intent, priority, and required agent skills before routing. This preliminary action enables precise matching of requests with appropriately skilled agents, significantly improving service quality while maintaining manageable implementation complexity through automated analysis and routing decisions.
4Device complexity
If service levels are fixed without adjustment, then system configuration is simplified, but the ability to maintain optimum chatbot utilization and live human interaction is compromised
Solution Approach 1:
The system implements dynamic service level adjustment based on real-time analysis of request characteristics, agent availability, and operational conditions. Rather than fixed service levels, the system continuously adapts routing decisions and service parameters to maintain optimal balance between chatbot utilization and live agent engagement, resolving the contradiction between configuration simplicity and operational adaptability.
Data Source
AI summary
A method of facilitating predictive intent-based routing of service requests. Accordingly, the method may include receiving a service request data from a user device, initiating a chatbot, retrieving a service portfolio based on the receiving of the service request data, processing the service portfolio and the service request data, determining a service level, analyzing the service request data using a first machine learning model, determining an intent based on the analyzing, generating an adjusted service level based on the intent using a second machine learning model, assigning an agent to a user of the at least one user based on the adjusted service level, generating a service notification for the agent based on the assigning, transmitting the service notification to the user device and an agent device associated with the agent, and storing the service request data, the service notification, and the adjusted service level.