NLP Ticket Assignment With Inferred Customer Data Balancing
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Solution Overview
Problem
Existing customer service systems struggle to efficiently assign tickets to agents based on their skills and customer similarity, leading to inefficiencies and reduced customer satisfaction due to uneven distribution of workloads among agents.
Innovation Solution
A system and method that utilizes natural language processing and machine learning to infer missing customer data, allowing for accurate ticket assignment to agents based on their skills and customer similarity, using a combination of known, missing, and inferred data to optimize ticket distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tickets are assigned to the best agents only, then customer satisfaction and resolution quality improve, but workload distribution deteriorates and best agents become overloaded
Solution Approach 1:
The system changes the parameters used for ticket assignment from simple performance metrics to a multi-dimensional scoring system that includes skill matching, customer similarity, agent availability, and historical performance. This allows tickets to be distributed more evenly while maintaining high satisfaction by matching tickets to agents whose specific skills and customer demographics align with the ticket requirements.
Solution Approach 2:
The ticket assignment system dynamically adjusts agent scoring and ticket distribution based on real-time factors such as current workload, recent performance, availability, and changing customer needs. This dynamic approach prevents overload of top performers by adapting assignment criteria to current system state, thereby improving both satisfaction and workload balance.
2Speed
If tickets are assigned based on simple performance metrics, then assignment speed improves, but assignment accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating agent scores, skill profiles, and customer similarity metrics before ticket assignment occurs. This preprocessing allows the system to make rapid, accurate assignments in real-time without sacrificing precision, as the complex calculations are already completed and stored for quick retrieval.
Solution Approach 2:
The system replaces manual or simple rule-based ticket assignment with an automated machine learning model that processes multiple factors simultaneously. This substitution enables the system to evaluate complex assignment criteria accurately while maintaining high speed through algorithmic optimization and automated decision-making.
3Adaptability or versatility
If manual ticket assignment is used, then flexibility and control improve, but time consumption and inefficiency worsen
Solution Approach 1:
The system enables self-service ticket assignment by automatically matching tickets to agents based on predefined criteria and real-time data without requiring manual intervention. This automation maintains flexibility through configurable parameters and adaptability to changing conditions while eliminating the time consumption associated with manual assignment processes.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor assignment outcomes, agent performance, and customer satisfaction. This feedback loop allows the system to automatically adjust assignment strategies, refine agent scoring, and improve matching accuracy over time, providing the flexibility and control previously requiring manual management without the associated time costs.
Data Source
AI summary
A system and method are disclosed for assigning a ticket including one or more databases that store data describing electronic communication between one or more customer system communication devices and one or more service center communication devices. Embodiments further include a computer coupled with one or more databases and configured to monitor communication activity to determine whether a communication channel is opened between one or more customer system communication devices and one or more service center communication devices, responsive to a ticket being opened between one or more customer system communication devices and one or more service center communication devices, analyze the ticket for at least one topic and customer data, search the one or more databases for missing customer data, and in response to detecting missing customer data, identify one or more relationship models that predicts a value for the missing customer data based on known customer data.