Contact Center Message Routing with Machine-Learning Relevance Tags
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Solution Overview
Problem
Contact centers face inefficiencies in routing incoming emails, leading to unnecessary utilization of hardware resources due to spam and low-priority messages, which can be addressed by implementing machine learning and data mining techniques to categorize and prioritize messages effectively.
Innovation Solution
Implementing machine learning tools and ensemble classifiers to analyze contact history, automatically categorize incoming emails, and prioritize messages based on relevancy, using data models like Decision Trees, SVMs, and Bayesian classifiers to improve routing efficiency and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all incoming emails are routed through the contact center system, then no legitimate messages are missed, but hardware resources are wasted processing spam and low-priority messages
Solution Approach 1:
The system performs preliminary classification of incoming emails using machine learning models before they enter the full contact center routing process. This preliminary action identifies and filters out spam and low-priority messages, preventing them from consuming hardware resources while ensuring legitimate messages are properly routed.
Solution Approach 2:
The system extracts and separates spam and low-priority messages from the main email stream using ensemble classifiers. These extracted messages are handled differently (rejected or low-priority queued) rather than being processed through the complete contact center routing system, thus conserving hardware resources.
2Reliability
If all emails are assigned to agents for review, then customer service quality is maintained, but agent productivity decreases due to handling unnecessary messages
Solution Approach 1:
The system performs preliminary filtering and classification of emails before assignment to agents. By using machine learning models to identify spam and low-priority messages in advance, the system ensures that agents only receive legitimate, high-priority customer messages, thereby maintaining service quality while improving productivity.
Solution Approach 2:
The system extracts unwanted messages (spam and low-priority emails) from the general email stream and handles them through automated processes or separate queues. This extraction prevents these messages from being assigned to agents, allowing agents to focus exclusively on legitimate customer service tasks.
3Ease of manufacture
If traditional keyword-based routing is used, then implementation is simple, but routing precision is insufficient for complex message categorization
Solution Approach 1:
The system merges multiple machine learning classifiers (Naive Bayes, Decision Trees, SVMs) into an ensemble model. This combination leverages the strengths of different algorithms to achieve superior message categorization accuracy compared to single classifiers or simple keyword-based systems, while still maintaining reasonable implementation complexity.
Solution Approach 2:
The system changes the parameters of message analysis by moving from simple keyword matching to multi-parameter machine learning classification. The ensemble classifiers analyze multiple features of emails (content, sender, patterns, etc.) simultaneously, significantly improving categorization precision while the modular architecture keeps implementation manageable.
Data Source
AI summary
The present disclosure provides, among other things, a method of managing contacts in a contact center, the method including: receiving a text-based communication from a customer of the contact center; analyzing the text-based communication to determine a relevancy associated with the text-based communication; based on the analysis, determining a relevancy level to assign to the text-based communication; tagging the text-based communication with a relevancy tag that identifies the determined relevancy level; updating a priority associated with assigning the text-based communication to an agent of the contact center based on the relevancy tag; assigning the text-based communication to the agent of the contact center; enabling a machine learning process to analyze a database of text-based communications; and updating a data model used to automatically tag text-based communications with relevancy tags based on the analysis performed by the machine learning process.