Context-Based Channel Switchover in Contact Centers
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Solution Overview
Problem
In contact centers, customers may experience suboptimal communication sessions due to language barriers, agent skill mismatches, or inappropriate communication channels, leading to inefficiencies and poor customer experiences.
Innovation Solution
A system that actively monitors communication sessions to detect difficulties and suggests or implements channel changes using artificial intelligence and machine learning, analyzing sentiment, agent availability, and historical data to switch between channels like voice, video, text, or chat to improve understanding and resolution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If customers are connected to agents on randomly available channels, then connection speed is improved, but communication effectiveness deteriorates
Solution Approach 1:
The system performs preliminary analysis of customer attributes, agent skills, and channel characteristics before connection is established. This pre-assessment enables the system to predict optimal channel match quality and proactively manage channel changes before communication effectiveness deteriorates
Solution Approach 2:
The system continuously monitors communication session attributes and compares them against historical data and performance thresholds. When suboptimal communication is detected, the system provides feedback by suggesting or automatically implementing channel changes to improve effectiveness
2Measurement precision
If multiple communication channels are monitored and analyzed, then channel selection accuracy is improved, but system complexity increases
Solution Approach 1:
The channel management engine performs multiple functions using a single integrated system: monitoring communication attributes, analyzing historical data, comparing channel performance, detecting suboptimal conditions, and managing channel changes. This multi-functionality achieves accurate channel selection without proportionally increasing system complexity
Solution Approach 2:
The system uses historical communication data and performance metrics to automatically train and improve its own channel selection algorithms. By leveraging its own operational data, the system enhances measurement precision without requiring external complex analytical systems
3Reliability
If channel changes are suggested and implemented during active communication sessions, then customer satisfaction is improved, but communication disruption increases
Solution Approach 1:
The system suggests channel changes before communication problems fully develop or at optimal moments during the session. By timing channel change suggestions proactively rather than reactively, the system minimizes disruption while improving customer satisfaction
Solution Approach 2:
The channel management engine acts as an intermediary that coordinates channel changes smoothly. It monitors both the communication session and available channels, then manages the transition by suggesting or automatically switching channels in a way that maintains communication continuity and reduces disruption
Data Source
AI summary
The present disclosure provides, among other things, methods and systems of managing a first channel, including: receiving a request for a communication session on the first channel; determining that a monitored attribute of the communication session has met a first threshold; comparing, by a channel change analysis, a first performance measure of the first channel with a second performance measure of a second channel; and managing a channel change based on the determining and the channel change analysis.