Coach-Assist Controller for Real-Time CSR Interaction Analysis
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Solution Overview
Problem
Conventional customer support systems are inefficient in providing real-time assistance to less experienced customer service representatives (CSRs), leading to suboptimal customer interactions and unresolved issues due to labor-intensive review processes and reliance on supervisor discretion.
Innovation Solution
A coach-assist controller that monitors and analyzes real-time consumer-CSR interactions using intermediary data models and an aggregate data model to generate a weighted interaction score, determining the need for coach support and selectively delivering it to CSRs during ongoing interactions, with methods for coach selection and communication through a coach-assist dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If systematic call reviews are conducted to provide feedback to CSRs, then CSR performance improvement is achieved, but the process becomes labor-intensive and feedback is not available in real-time
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated system using speech-to-text conversion, machine learning models, and automated scoring to analyze customer service interactions. This substitution eliminates the need for manual systematic reviews while providing real-time feedback, thus resolving the contradiction between improving CSR performance and reducing time loss.
Solution Approach 2:
The system enables self-service by automatically generating feedback and insights without requiring supervisor intervention. The automated analysis of customer interactions and generation of actionable insights allows the system to serve itself, eliminating labor-intensive manual review processes while maintaining continuous feedback availability.
2Ease of operation
If supervisors walk the floor to survey CSR needs, then some CSRs receive assistance, but many calls that could benefit from coach support are left unsupported due to supervisor limitations
Solution Approach 1:
The patent replaces the human supervisor's mechanical observation process with an automated speech analysis system. The system continuously monitors and analyzes customer service interactions using speech-to-text conversion and machine learning models, automatically identifying when coach support is needed without relying on supervisor discretion. This enables comprehensive coverage of all calls rather than limited supervisor observation.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the customer interaction and supervisor intervention. The speech analysis system acts as a mediator that continuously evaluates interactions and triggers coach support recommendations, expanding the effective reach of supervisor resources to cover many more calls than direct observation could handle.
3Reliability
If more experienced CSRs are used to improve customer satisfaction, then interaction effectiveness increases, but system complexity and training requirements increase
Solution Approach 1:
The patent replaces the need for human expertise (experienced CSRs) with an automated speech analysis system using machine learning models. The system analyzes customer interactions and provides real-time feedback and insights, achieving high customer satisfaction without requiring CSRs to have extensive experience. This substitution reduces the complexity associated with hiring, training, and managing highly experienced personnel.
Data Source
AI summary
This disclosure describes techniques that allow a coach-assist controller to provide coach support to a customer service representative (CSR) during an ongoing consumer-CSR interaction. The coach-assist controller may intercept a consumer-CSR interaction and generate corresponding interaction data. The coach-assist controller may further analyze the interaction data to infer a current state of the consumer-CSR interaction, and in doing so, determine whether to request coach support for the CSR.