AI Call Routing System with Dynamic Queue Adjustment
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Solution Overview
Problem
Call centers face challenges in providing efficient customer service due to lengthy wait times and frustration caused by interactive voice response (IVR) systems, which often fail to quickly identify customer needs and route requests to suitable live agents, leading to poor customer experience and increased operational costs.
Innovation Solution
A system that uses machine learning and artificial intelligence to quickly determine the customer's cause for their request by sending data output and receiving input via various communication channels, identifying suitable agents, and adjusting the customer's queue position to minimize wait time, potentially offering rewards for quick resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional IVR systems are used to automate calls, then operational costs are reduced, but customer experience deteriorates due to tedious voice menus and long wait times
Solution Approach 1:
The patent introduces an intermediary system that bridges automated IVR and live agents. This system uses natural language processing to understand customer intent and automatically routes calls to appropriate agents with relevant context, eliminating the need for customers to navigate complex voice menus while maintaining operational efficiency.
Solution Approach 2:
The patent replaces traditional mechanical IVR voice menu navigation with an AI-based natural language understanding system. Instead of requiring customers to follow predefined voice prompts, the system processes spoken language directly to determine customer needs and route accordingly, significantly improving ease of operation.
2Reliability
If customers are placed in queue to speak with live agents, then service quality improves, but wait time increases causing customer frustration
Solution Approach 1:
The patent performs preliminary actions by analyzing customer intent and preparing routing information before the customer reaches the front of the queue. The system processes natural language input in real-time to determine the appropriate agent and pre-configures the call routing, enabling immediate connection when an agent becomes available and minimizing actual wait time.
Solution Approach 2:
The patent implements dynamic queue management where customer priority and routing are continuously adjusted based on real-time analysis of customer needs and agent availability. The system dynamically repositions customers in the queue based on urgency and matches them with the most appropriate agent, reducing overall wait time while maintaining service quality.
3Ease of operation
If live agents are made available to customers, then customer satisfaction improves, but agents may lack adequate knowledge to address specific customer requests
Solution Approach 1:
The patent implements a feedback loop where the AI system continuously monitors customer interactions and provides real-time information to agents about customer intent, history, and specific needs. This feedback mechanism ensures agents have complete knowledge context to effectively address customer requests, improving both satisfaction and efficiency.
Solution Approach 2:
The system performs preliminary analysis of customer requests using natural language processing to extract key information and customer context before connecting to an agent. This preliminary action prepares the agent with relevant knowledge and context, ensuring they can immediately address the customer's specific needs without requiring additional information gathering.
4Productivity
If AI systems are used to quickly identify customer needs, then routing efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary AI layer that handles the complexity of natural language processing and intent recognition, while presenting simplified routing decisions to the existing call center infrastructure. This intermediary approach improves routing efficiency without requiring complete system replacement, managing complexity through layered architecture.
Data Source
AI summary
Methods and systems route requests for service in a call center. A non-transitory computer-readable medium stores data representative of a queue of customers waiting to be serviced. A processor queues a request for service initiated by a customer. Data output is sent to the customer to ascertain the customer's cause for the request via a data communication channel. Data input is received from the customer via the data communication channel. The data input indicates the customer's cause for the request. An agent is identified from a plurality of agents suitable to address the customer's cause for the request. The customer's position is adjusted in the queue based on the identified agent. A routing instruction is determined about routing the request to the identified agent.