Communication Session Dynamic Learning for Real-Time Skill Gaps
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Solution Overview
Problem
Conventional call centers face inefficiencies due to agents lacking proficiency in specific topics, leading to prolonged calls and customer dissatisfaction, as existing systems fail to dynamically identify and address skill gaps in real-time.
Innovation Solution
A machine learning-based system that analyzes audio data from communication sessions to identify topics and sub-topics, dynamically joins additional agents for training, and routes calls to supervisors when necessary, using intelligent routing and dynamic learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents are assigned to handle customer calls based on conventional routing methods, then calls can be processed, but call duration increases and customer satisfaction decreases due to agents lacking proficiency in specific topics
Solution Approach 1:
The system dynamically adjusts agent assignments and skill profiles in real-time based on call topic analysis. Machine learning models continuously learn from call outcomes and update agent competencies, enabling the routing system to adapt to changing skill requirements and optimize call duration while maintaining productivity.
Solution Approach 2:
The system implements feedback loops where call outcomes, customer satisfaction metrics, and topic classifications are fed back into the machine learning models. This feedback continuously refines agent skill assessments and routing decisions, improving call efficiency and reducing duration over time.
2Productivity
If additional agents are joined to ongoing calls for training purposes, then agent proficiency improves, but call complexity and system resource requirements increase
Solution Approach 1:
The system introduces an intelligent intermediary layer consisting of machine learning models that analyze call topics and match them with appropriate agents. This intermediary automatically manages the complexity of joining agents to calls based on skill requirements, reducing manual intervention while improving proficiency.
Solution Approach 2:
The system performs preliminary analysis of call topics and agent skills before calls are connected. By pre-identifying the most suitable agents for specific call topics, the system reduces the need for complex real-time adjustments and minimizes system complexity while maximizing agent proficiency.
Data Source
AI summary
Arrangements for machine learning-based dynamic learning are provided. In some examples, audio data associated with a plurality of calls may be received and analyzed to identify a topic and sub-topic of each call and metadata of each call. Feedback data may also be received. A machine learning model may be executed by inputting, to the model, the identified topic and sub-topic and metadata of each call, and the feedback data, to output one or more topics or sub-topics of concern. A plurality of ongoing calls may be monitored to identify an ongoing call related to one of: a topic or sub-topic of concern. A plurality of agents who are not subject matter experts in the identified topic or sub-topic of concern and are available may be identified and joined, via respective computing devices, to the ongoing call in a dynamic learning session.