Perception Gap Analysis for Accurate Agent Coaching Plans
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Solution Overview
Problem
Conventional customer experience systems inaccurately rate agents based on customer satisfaction alone, lack flexibility in data collection and analysis, and require multiple software tools for managing interaction event data, making it difficult to develop targeted coaching plans efficiently.
Innovation Solution
An agent evaluation system that utilizes machine learning models to compare user and agent feedback data, generating perception gaps and performance scores, and provides suggested actions and coaching plans through a unified graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems base agent ratings solely on customer satisfaction ratings, then the rating process is simple, but the accuracy of agent ratings deteriorates because other aspects of agent performance are not accounted for
Solution Approach 1:
The patent combines multiple data sources including customer satisfaction ratings, agent self-assessments, interaction event data, and performance metrics into a unified agent rating system. This merging of diverse data streams enables comprehensive and accurate agent evaluation while maintaining system manageability through integrated architecture.
Solution Approach 2:
The system serves multiple functions simultaneously: collecting customer feedback, gathering agent self-assessments, analyzing interaction events, calculating performance metrics, and generating comprehensive ratings. This multi-functionality allows the system to improve rating accuracy without requiring separate specialized systems for each function.
2Ease of operation
If conventional systems require multiple software tools to collect and report customer experience data, then data collection coverage is comprehensive, but the ease of operation deteriorates due to the need to navigate multiple tools and steps
Solution Approach 1:
The patent consolidates multiple previously separate software tools into a single unified platform that performs data collection, analysis, and reporting functions. This integration eliminates the need for managers to navigate multiple tools while maintaining comprehensive data collection capabilities across all interaction events.
Solution Approach 2:
The unified system performs multiple functions including survey distribution, data collection from multiple sources, automated analysis, and report generation within a single platform. This multi-functionality provides comprehensive data collection coverage while significantly improving ease of operation by eliminating tool-switching requirements.
3Productivity
If conventional systems collect detailed data from multiple sources, then the information available for coaching plans is comprehensive, but the productivity deteriorates due to the time required to gather and analyze data across multiple tools
Solution Approach 1:
The system performs preliminary automated data gathering, cleaning, and organization from multiple sources before coaching plan development is needed. By pre-processing and structuring data in advance, the system eliminates time-consuming manual data gathering steps while maintaining comprehensive information availability for coaching decisions.
Solution Approach 2:
The system provides automated feedback loops that continuously collect, analyze, and present relevant data insights to managers and agents. This real-time feedback mechanism reduces the time required to gather and analyze data by automatically processing information and presenting actionable insights without manual intervention.
4Reliability
If conventional systems use only customer perspective data, then the data collection process is straightforward, but the reliability of agent performance assessment deteriorates due to lack of multiple perspectives
Solution Approach 1:
The patent merges data from multiple perspectives including customer satisfaction ratings, agent self-assessments, and objective interaction event data into a unified assessment framework. This combination of diverse perspectives enhances the reliability of performance assessments by providing a balanced view that accounts for biases inherent in single-source data.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for determining an existence of a perception gap for an interaction event. In particular, in one or more embodiments, the disclosed systems generate a suggested action and provide the suggested action for display via a graphical user interface of an agent device. In some embodiments, the disclosed systems utilize a machine learning model to generate the suggested action for the agent. Furthermore, in one or more embodiments, the disclosed systems generate an agent performance score reflecting an overall performance of an agent. In some embodiments, the disclosed systems utilize a machine learning model to generate the agent performance score.