Dynamic Support Ticket Prioritization via Sentiment Analysis
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Solution Overview
Problem
Conventional CRM systems fail to accurately track dynamic natural language in support tickets, leading to inefficient priority level assignment, customer dissatisfaction, and increased costs due to rigid priority levels and manual tracking, which can result in poor service quality and inaccurate reporting.
Innovation Solution
A machine learning-based system that trains on support ticket communications to determine content, metadata, and context data, converting this information into impulses to generate a Needs Attention Score, which automatically prioritizes support tickets in real-time, allowing for dynamic and accurate priority assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigid priority levels are used in CRM systems, then priority assignment is simple and standardized, but the system cannot accurately track dynamic natural language and customer sentiment, leading to poor service quality
Solution Approach 1:
The patent transforms static, rigid priority levels into dynamic priority scores that automatically adapt to changing customer sentiment and communication content. The system continuously analyzes natural language in support tickets and updates priority levels in real-time based on detected sentiment changes, ensuring both operational simplicity and measurement precision.
Solution Approach 2:
The system changes the parameter of priority from fixed categorical levels to continuous dynamic scores influenced by sentiment analysis. By introducing sentiment-derived parameters that modify priority levels, the system maintains ease of operation while significantly improving the accuracy and responsiveness of priority assignment.
2Measurement precision
If manual monitoring and evaluation of support tickets is employed, then priority assignment can be customized and accurate, but the cost increases significantly and the solution does not scale
Solution Approach 1:
The system enables self-service automated priority assignment through sentiment analysis technology. The support ticket system automatically detects customer sentiment, analyzes communication patterns, and adjusts priority levels without human intervention. This eliminates the need for costly manual monitoring while maintaining or improving priority assignment accuracy and enabling seamless scaling.
Solution Approach 2:
The patent replaces the mechanical system of manual human monitoring with an automated electronic sentiment analysis system. By substituting human analysts with machine learning-based sentiment detection, the system achieves the same or better measurement precision at lower cost and with full scalability.
3Adaptability or versatility
If support agents manually adjust priority levels based on their judgment, then flexibility is improved, but customer dissatisfaction increases due to perceived lowering of priority and loss of trust
Solution Approach 1:
The system implements continuous feedback loops where customer sentiment directly influences priority level adjustments. The automated sentiment analysis provides real-time feedback about customer state, and the system responds by adjusting priorities accordingly. This transparent, objective feedback mechanism maintains flexibility while building customer trust through consistent, unbiased priority management.
Solution Approach 2:
The patent introduces sentiment analysis as an intermediary between customer communications and priority assignment decisions. This neutral mediator objectively translates natural language into priority adjustments, eliminating the subjective human judgment that causes customer dissatisfaction while preserving the necessary flexibility to adapt to different situations.
4Device complexity
If simple first-come-first-served approach is used, then resource allocation is straightforward and low-cost, but service quality deteriorates due to inability to prioritize critical issues
Solution Approach 1:
The system changes the resource allocation parameter from simple time-based first-come-first-served to sentiment-informed dynamic priority scoring. By introducing sentiment analysis parameters that automatically adjust ticket priorities, the system maintains straightforward resource allocation mechanics while dramatically improving service quality through intelligent prioritization of critical issues.
Data Source
AI summary
System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.