Dynamic Routing Rules via Sentiment Feedback Analysis
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Solution Overview
Problem
Current customer engagement center (CEC) systems lack the ability to automatically analyze customer service interactions, including feedback from surveys and sentiment, to dynamically update routing and guidance rules, leading to inefficient communication routing and potential customer relationship damage due to outdated protocols.
Innovation Solution
The system employs a metadata analytics service (MAS) to analyze incoming communications, generating sentiment data that is used by a routing analysis engine (RAE) and guidance analysis engine (GAE) to determine optimal CSR assignment and provide real-time guidance, while a rules analytics service (RAS) updates routing and guidance analytics rules based on feedback and sentiment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static generalized rules are used for routing communications, then the system is simple to implement, but the system cannot adapt to changing customer preferences and CSR performance
Solution Approach 1:
The system implements continuous feedback loops where customer survey results and sentiment analysis are fed back into the routing and guidance rule sets. The RAS analyzes this feedback and automatically updates rules to improve routing decisions and CSR guidance over time, enabling the system to adapt to changing customer preferences and performance metrics.
Solution Approach 2:
The patent transforms static routing rules into dynamic rules that automatically adjust based on real-time and historical feedback data. The system continuously modifies routing logic and guidance parameters based on analyzed performance metrics, customer sentiment, and survey results, allowing the system to evolve without manual intervention.
2Speed
If manual updates of routing rules are performed, then the system maintains control over rule changes, but the system cannot respond quickly to performance issues
Solution Approach 1:
The system performs self-updating of routing and guidance rules through the RAS, which automatically analyzes feedback data and modifies rule sets without human intervention. This self-service capability enables the system to rapidly respond to performance issues by automatically adjusting routing logic based on analyzed survey results and sentiment data.
Solution Approach 2:
The patent replaces manual mechanical rule updates with an automated electronic system that uses data analysis and algorithmic decision-making. The RAS substitutes human operators by automatically processing feedback data and implementing rule changes, dramatically increasing response speed to performance issues.
3Measurement precision
If comprehensive feedback analysis is implemented, then routing accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of feedback data and sentiment information in advance of routing decisions. By pre-processing and analyzing survey results and customer sentiment before they are needed for routing, the system prepares optimized rule sets that can be quickly applied, reducing real-time processing requirements while maintaining high routing accuracy.
Data Source
AI summary
The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.