Self-assessment Divergence Determinant for Contact Center Agent Evaluation
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Solution Overview
Problem
Current contact centers lack an effective mechanism to gauge agent self-assessment effectiveness, leading to inadequate identification of performance issues and reduced employee engagement and loyalty, which negatively impacts customer interactions and overall quality of service.
Innovation Solution
A computerized method and system that calculates a Self-assessment Divergence Determinant (SDD) by consolidating preconfigured data points from agent interactions, determining confidence intervals, and setting divergence indicators to quantify self-assessment effectiveness, enabling targeted remedial measures and improved performance management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agent self-assessment is implemented without measurement mechanism, then agents can review their own performance, but the effectiveness of self-assessment cannot be gauged and actual problem areas cannot be identified
Solution Approach 1:
The system implements a feedback mechanism where agents' self-assessment responses are compared against actual performance data from multiple sources (quality assurance scores, customer satisfaction ratings, peer feedback). This closed-loop feedback allows the system to measure self-assessment effectiveness by detecting discrepancies between perceived and actual performance, enabling continuous improvement of the self-assessment process.
Solution Approach 2:
The patent introduces an intermediary measurement system that acts as a mediator between agent self-assessment and performance evaluation. This intermediary layer collects, processes, and analyzes data from multiple sources to generate objective performance metrics, which are then compared with self-assessment data to gauge effectiveness without directly complicating the agent's self-evaluation process.
2Ease of operation
If agent self-assessment is performed without effectiveness measurement, then agents may feel less threatened and judged, but managers cannot effectively gauge and communicate problem areas impacting agent performance
Solution Approach 1:
The system provides dual feedback: to agents, it confirms their self-assessment insights are valuable; to managers, it delivers actionable intelligence about performance gaps. The feedback loop processes self-assessment data alongside objective performance metrics to identify specific problem areas, enabling managers to communicate targeted improvement opportunities without undermining agent confidence.
Solution Approach 2:
The measurement system segments performance evaluation into distinct components: self-assessment dimensions, objective performance metrics, and gap analysis results. This segmentation allows the system to preserve the simplicity of self-assessment while systematically capturing and analyzing specific problem areas through structured data collection and comparison across multiple performance dimensions.
3Reliability
If no mechanism to measure self-assessment effectiveness exists, then employee engagement and loyalty may be reduced, but implementing measurement mechanisms increases system complexity
Solution Approach 1:
The system enables agents to self-serve in the effectiveness measurement process by allowing them to access their own assessment data, compare it with objective metrics, and identify their own development needs. This self-service approach builds trust and engagement while minimizing the complexity burden on management systems, as agents actively participate in their own performance improvement journey.
Solution Approach 2:
The measurement system is designed with multi-functionality to serve multiple purposes: gauging self-assessment effectiveness, identifying performance problems, tracking employee engagement, and guiding development initiatives. By consolidating these functions into a single integrated platform, the system achieves comprehensive reliability benefits without proportionally increasing complexity, as shared infrastructure supports multiple objectives simultaneously.
Data Source
AI summary
A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval. Then, accumulating the divergence-indicator of the data-points to yield an SDD for the interaction; and sending the SDD to one or more systems.