Real-Time Voice Intent Assistant for Call Centers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current call center systems face inefficiencies in handling customer inquiries, leading to increased resource consumption, prolonged call handling times, and a poor customer experience due to the need for agents to research information from various sources during calls, often resulting in incomplete transactions and lost sales.
Innovation Solution
An assistant system utilizing a machine learning model that processes real-time audio data, customer data, chat data, and IVR data to determine customer intent and perform actions such as providing transcripts, generating chat flows, and accessing necessary applications, thereby reducing the need for agents to manually research information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents manually research information from various sources during calls, then they can answer customer questions, but call handling time increases and customer experience deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically determining customer intent, generating relevant information, and preparing responses before the agent needs to speak with the customer. This eliminates the need for agents to manually research during calls, reducing call handling time while maintaining accuracy.
Solution Approach 2:
An automated assistant system acts as an intermediary between the customer and the agent, analyzing customer inputs, determining intent, and providing agents with prepared information and suggested responses. This mediator handles the research function that previously required manual agent effort.
2Ease of operation
If agents manually research information during calls, then they can provide customer service, but resource consumption increases
Solution Approach 1:
The system enables self-service by automatically performing information retrieval, intent determination, and response preparation without requiring human agents to manually research. This automates the service delivery process, reducing both operational complexity and resource consumption.
3Loss of information
If agents manually research information from various sources, then they can answer questions, but transaction completion rate decreases
Solution Approach 1:
The system determines customer intent and prepares relevant information in advance, ensuring that all necessary information is available to the agent before the call. This preliminary preparation prevents information gaps that would otherwise cause incomplete transactions.
4Measurement precision
If the system processes multiple data types in real time, then customer intent determination accuracy improves, but system complexity increases
Solution Approach 1:
The automated assistant system performs multiple functions including audio processing, intent determination, information retrieval, and response generation within a single integrated platform. This multi-functionality handles the complexity internally while presenting a unified interface, managing system complexity through consolidation.
Data Source
AI summary
A device may receive real time audio data associated with a call between an agent and a customer, and may receive customer data identifying historical interactions with the customer. The device may receive chat data associated with the customer or interactive voice response (IVR) data associated with the customer, and may generate, based on the real time audio data, transcript data identifying a real time transcript of the call with the customer. The device may process the real time audio data, the customer data, the chat data or the IVR data, and the transcript data, with a machine learning model, to determine a customer intent and one or more actions to perform based on the customer intent; and may perform the one or more actions.