AI Conversation Analysis for Missed Content in Contact Centers
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Solution Overview
Problem
Contact centers face challenges in reducing average handle time (AHT) due to varying customer questions, demands, and understanding, leading to repeated calls about the same issues, which can be addressed by inferring customer intentions and providing real-time alerts or notifications to agents and customers to enhance service efficiency.
Innovation Solution
A system utilizing AI and machine learning to analyze customer interactions in real-time, detecting distractions or unabsorbed content, and providing automated alerts or notifications to agents and customers to address these issues, thereby reducing AHT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time interaction analysis and automated notifications are implemented, then average handle time is reduced and service efficiency is improved, but system complexity and implementation cost increase
Solution Approach 1:
An automated notification system acts as an intermediary between the contact center system and customers. The system detects interaction outcomes and automatically sends notifications to customers about resolution status, eliminating the need for customers to initiate follow-up calls and reducing agent workload without requiring complex manual processes
Solution Approach 2:
The system enables self-service by automatically notifying customers about their issue resolution status without requiring customer initiation. Customers receive proactive updates about their interactions, allowing them to stay informed without additional agent involvement, thus reducing overall handle time
2Reliability
If real-time interaction analysis is performed to detect customer understanding, then customer comprehension is improved and call-backs are reduced, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts only the essential elements needed to determine customer understanding from complex interaction data. Rather than analyzing all conversation details, it focuses on key indicators such as whether the customer's issue was resolved or if they requested follow-up actions, significantly reducing computational requirements while maintaining reliability
Solution Approach 2:
The system applies partial analysis by focusing on critical moments in the interaction that indicate customer understanding, such as confirmation of resolution or expressions of confusion. This selective approach processes only the necessary portions of interactions rather than analyzing every detail, optimizing resource usage
3Reliability
If automated notifications are sent to customers about interaction outcomes, then customer satisfaction is improved and repeated calls are reduced, but communication channel usage and notification management complexity increase
Solution Approach 1:
The notification system is designed to be universal and multi-functional, capable of sending notifications through multiple communication channels (email, SMS, in-app notifications) using a single unified system. This allows the same infrastructure to serve multiple purposes and reach customers through their preferred channels without requiring separate systems for each communication method
Data Source
AI summary
A system and method are provided for analyzing and reacting to interactions between entities using electronic communication channels. The method includes receiving data captured from a conversational exchange between a first entity communicating with a second entity using an electronic communication channel. The method also includes analyzing the captured data, for example using machine learning, to detect an indication that content was missed or not understood. The method also includes determining based on the indication, an automated message to at least one of the first entity and the second entity for executing the action.