ML Routing Strategy Generator for Contact Centers
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Solution Overview
Problem
Contact centers face challenges in implementing and optimizing call and interaction routing strategies due to the need for identifying best practices and business logic, which can be time-consuming and result in suboptimal solutions, especially for entities unfamiliar with call center operations.
Innovation Solution
A machine learning system and method that monitors activities in contact centers, updates a knowledge base, predicts outcomes based on customer interactions, and recommends routing strategies, allowing for automatic selection and deployment of optimized routing strategies without requiring skilled technicians or hardcoded approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contact centers manually identify best practices and business logic for routing strategies, then routing effectiveness can be optimized, but deployment time and complexity increase significantly
Solution Approach 1:
The system enables self-service by automatically generating routing strategies through machine learning models that analyze historical contact center data. The automated strategy generator creates optimized routing rules without requiring manual identification of business logic, thereby reducing deployment time while maintaining routing effectiveness.
Solution Approach 2:
The patent replaces the manual mechanical process of identifying business logic and creating routing strategies with an automated machine learning system. The ML models process historical data and automatically generate routing strategies, substituting human analysis with computational algorithms that operate faster and more consistently.
2Ease of manufacture
If contact centers use hardcoded routing strategies, then implementation can be straightforward, but adaptability to changing conditions and suboptimal performance occur
Solution Approach 1:
The system implements dynamic routing strategies that automatically adapt to changing contact center conditions. The machine learning models continuously analyze new data and update routing strategies in real-time, allowing the system to respond to varying call volumes, agent availability, and customer preferences without requiring manual reconfiguration.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors the performance of routing strategies and uses this information to continuously improve future routing decisions. The machine learning models learn from historical outcomes and adjust routing logic to optimize customer satisfaction and operational efficiency over time.
3Reliability
If skilled technicians are involved in creating routing strategies, then strategy quality improves, but deployment complexity and resource requirements increase
Solution Approach 1:
The system performs self-service by automatically generating high-quality routing strategies through machine learning without requiring skilled technicians. The ML models encapsulate expert knowledge and automatically create optimized routing logic, eliminating the need for manual intervention while maintaining strategy quality.
Solution Approach 2:
The patent uses copying by training machine learning models on historical routing data and successful strategies. The models learn patterns from past performance and replicate successful routing approaches, effectively copying expert knowledge into automated algorithms that can be deployed without requiring the original experts to be present.
4Reliability
If extensive research is conducted to identify business logic, then routing effectiveness improves, but deployment time and resource requirements increase
Solution Approach 1:
The patent replaces extensive manual research with automated machine learning analysis. The ML models rapidly process historical contact center data to identify effective business logic and routing patterns, achieving the same analytical depth that would require extensive human research but in a fraction of the time.
Solution Approach 2:
The system maintains continuous analysis of contact center data to identify routing opportunities and optimize strategies. Rather than conducting discrete research projects, the machine learning models continuously learn from incoming data streams, enabling ongoing optimization without interrupting contact center operations.
Data Source
AI summary
A machine learning system and method for contact center use. Activities associated with a plurality of contact centers are monitored and a knowledge base is updated based on the monitored activities. An outcome for a particular contact center may be predicted based on monitored interactions for the particular contact center, and based on information in the knowledge base. An output is then generated based on the predicted outcome.