Updated Sentiment Scoring for Contact Center Session Escalation
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Solution Overview
Problem
Conventional real-time analytics tools for contact centers fail to monitor end-to-end user journey information in real-time, leading to delayed or missed interaction information, which negatively affects customer satisfaction due to extended holds and frustration.
Innovation Solution
Implementing sentiment-based prioritization by determining a score for contact center engagements, displaying it in a graphical user interface, and allowing supervisors to participate in sessions based on threshold-based alerts, enabling real-time monitoring, communication, or joining the engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional real-time analytics tools are used to monitor contact center engagements, then device complexity is reduced, but information completeness deteriorates due to failure to monitor end-to-end user journey information
Solution Approach 1:
The patent combines multiple data sources including contact center engagement data, user journey information, sentiment analysis, and interaction history into a unified analytics platform. This integration ensures comprehensive monitoring of end-to-end user experiences while maintaining manageable system complexity through centralized data processing and analysis.
2Loss of information
If supervisors manually monitor each contact center engagement, then information completeness is improved, but time consumption increases leading to delayed response
Solution Approach 1:
The system implements real-time feedback mechanisms through automated sentiment analysis and anomaly detection that continuously monitor engagement quality and alert supervisors immediately when issues arise. This feedback loop enables supervisors to respond promptly to critical situations without manually reviewing every interaction, thus reducing both information loss and response time.
Solution Approach 2:
The analytics platform performs self-monitoring of contact center engagements using automated sentiment analysis and quality assessment algorithms. The system independently identifies problematic interactions and generates alerts, freeing supervisors from manual monitoring while ensuring comprehensive information capture and rapid response capability.
3Reliability
If real-time sentiment analysis is implemented for all engagements, then customer satisfaction is improved, but computational resource consumption increases
Solution Approach 1:
The system applies sentiment analysis with varying intensity based on engagement characteristics. High-priority engagements such as those showing negative sentiment trends or involving complex issues receive comprehensive real-time analysis, while routine positive interactions receive lighter monitoring. This localized quality approach maintains high customer satisfaction for critical cases while optimizing computational resource consumption across the entire contact center workload.
Data Source
AI summary
A sentiment-based score is determined for a contact center engagement between a first contact center service operator and a contact center user. The sentiment-based score is indicated within a graphical user interface displaying information associated with multiple contact center engagements at a device of a second contact center service operator. Based on a request to participate in the contact center engagement received from the device of the second contact center service operator via the graphical user interface, information associated with the contact center engagement is transmitted to the device of the second contact center service operator, and a contact center session involving a device of the contact center user and the device of the second contact center service operator is established.