Call Center Training Management via Communication Analytics
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Solution Overview
Problem
Call centers face challenges in recognizing and adapting to changing training requirements for agents, as they need to handle diverse topics and issues that trend over time, requiring a dynamic training solution to ensure agents are adequately prepared.
Innovation Solution
The system analyzes communications between agents and customers to identify keywords or keyphrases that exceed a predetermined threshold, automatically determining training topics and scheduling agents for relevant training based on these analyses, using a combination of speech analytics and learning management system architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual training scheduling is used for call center agents, then training can be provided to agents, but the system cannot recognize changes in training requirements over time
Solution Approach 1:
The system implements automated feedback loops by continuously monitoring communication data and call metrics to detect changes in training requirements. Analytics results are automatically processed to identify emerging topics, and the system feeds this information back to scheduling agents for appropriate training interventions, enabling dynamic adaptation without manual intervention.
Solution Approach 2:
The system enables self-service by automatically identifying training needs through analytics processing and autonomously scheduling agents for training based on detected changes in communication patterns and performance metrics, eliminating the need for manual recognition and scheduling of training requirements.
2Reliability
If comprehensive training is provided to all agents on all topics, then agents are well-prepared, but training resources are wasted on topics agents already know
Solution Approach 1:
The system applies local quality by providing differentiated training to specific agents based on their individual performance gaps and identified needs. Rather than uniform training for all agents, the system schedules targeted training interventions for specific agents on specific topics where they demonstrate deficiencies, optimizing resource allocation while maintaining overall agent preparedness.
3Adaptability or versatility
If training topics are identified manually from communication data, then training can be provided, but the process is time-consuming and inefficient
Solution Approach 1:
The system replaces the mechanical manual process of reviewing communication data with automated analytics processing. The system uses computational algorithms to analyze communication patterns, identify emerging topics, and generate training recommendations automatically, substituting human manual labor with automated information processing systems that are both accurate and efficient.
Data Source
AI summary
Technologies are generally presented herein pertaining to identifying a training topic for agents at a contact center. In various embodiments, these technologies comprise performing an analysis on communications conducted between agents at the contact center and contact parties over a time period. In particular embodiments, the analysis performed on the communications involves identifying a number of occurrences of a keyword or a keyphrase found in the communications between the agents and the contact parties. Further, in various embodiments, in response to the number of occurrences of the keyword or the keyphrase over the time period being more than a predetermined threshold, the technologies presented herein comprise automatically identifying a training topic associated with the keyword or the keyphrase, and then providing a training course to the agent.