Contact Center Agent Metrics Normalization for Comparable Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in accurately evaluating contact center agent performance due to varying factors such as agent roles, work schedules, and workload fluctuations, making it difficult to compare performance across agents effectively.
Innovation Solution
A system that normalizes agent performance metrics by converting raw indicators into points scores using gamification zones and calculating goal percentages, allowing for comparison across a common standard and facilitating visualized performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If raw performance indicators are used directly for evaluation, then the evaluation process is simple, but the accuracy and comparability of performance evaluation deteriorates due to varying factors
Solution Approach 1:
The system transforms raw performance indicators into normalized goal percentages by changing the parameter representation. Different performance metrics are converted to a common scale (0-100%) allowing accurate comparison across agents with varying workloads and roles, thus improving measurement precision without significantly increasing process complexity
Solution Approach 2:
The system introduces an intermediary normalization layer that converts diverse raw performance indicators into a unified goal percentage format. This intermediary transformation enables accurate performance comparison across different agents while maintaining a relatively simple evaluation process structure
2Adaptability or versatility
If performance metrics are normalized to a common standard, then the comparability across agents improves, but the complexity of the evaluation system increases
Solution Approach 1:
The system creates a universal evaluation framework that handles multiple performance metrics and agent types through a single normalization process. The goal percentage calculation serves multiple functions: comparing agents, tracking progress, and evaluating different performance dimensions, thereby improving adaptability without proportionally increasing system complexity
Solution Approach 2:
The system segments the evaluation process into distinct components: data collection, normalization calculation, and comparison evaluation. This segmentation allows the complex normalization task to be broken down into manageable steps, improving comparability while keeping the overall system complexity organized and manageable
3Quantity of substance
If multiple performance metrics are collected and normalized, then the comprehensiveness of performance evaluation improves, but the processing time and computational complexity increases
Solution Approach 1:
The system merges multiple performance metric calculations into a unified goal percentage computation. By combining the normalization and comparison operations into a single efficient process, the system maintains comprehensive multi-metric evaluation while reducing processing time compared to separate evaluation approaches
Data Source
AI summary
A method of evaluating agent performance according to an embodiment includes obtaining metric data for one or more agents including a plurality of raw performance indicators each corresponding to a particular agent performance metric of a plurality of agent performance metrics and converting, based on the metric data, each of the plurality of raw performance indicators into a points score for the particular agent performance metric to provide a plurality of points scores for the plurality of agent performance metrics.