Automated KPI Strand Generation for Skills Management
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Solution Overview
Problem
Current methods for defining and evaluating employee skill sets in contact centers are labor-intensive, requiring manual setup and weighting of Key Performance Indicators (KPIs) for generating strands, which is time-consuming and inefficient.
Innovation Solution
A system and method for automatically generating improvement profiles using variance calculations and normalized KPIs to determine agent performance and suggest improvement metrics, allowing for efficient alignment of employee skills with job demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setup and weighting of KPIs is used to generate strands, then customization and control over performance metrics is improved, but time consumption and labor intensity increase
Solution Approach 1:
The system performs automatic KPI strand generation using variance calculations on historical data, allowing the system to serve itself rather than requiring manual intervention. The automated process calculates variances, normalizes metrics, and generates weighted strands without human input, eliminating the time-consuming manual setup while maintaining systematic control
Solution Approach 2:
The system changes the approach from manual parameter setting to automated parameter derivation by calculating KPI weights based on statistical variance of historical performance data. This transforms the weighting process from a subjective manual task to an objective automated calculation based on actual performance variability
2Productivity
If automated variance calculation and normalization is used, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical processes with automated computational processes. Instead of manual KPI weighting and strand generation, the system uses automated variance calculations and normalization algorithms processed by a computer, substituting human cognitive and manual operations with machine-based computational operations
Solution Approach 2:
The system introduces statistical metrics (variance, normalization factors) as intermediaries between raw performance data and final KPI weighting. These intermediate calculations serve as mathematical mediators that transform historical data into meaningful weighted strands, managing the complexity through structured computational steps
3Measurement precision
If detailed variance analysis and normalization are performed, then measurement precision of agent performance is improved, but computational effort increases
Solution Approach 1:
The system performs variance calculation and normalization only on selected KPIs that are relevant to specific agent types and performance contexts, rather than analyzing all possible metrics. This partial action approach achieves sufficient measurement precision for decision-making without the excessive computational effort of comprehensive analysis of all available data
Data Source
AI summary
A system and method are presented for improvement profile generation in a skills management platform, using past data and a set of KPIs. Variance calculation is performed with a basic variance formula and these values are used to generate a strand. A strand may be defined as a collection of KPIs, each weighted to show the importance of that KPI for that agent type. KPIs can be selected to generate a strand with, and the strand is generated from those KPIs considering the normalized variance of each KPI. An agent's improvement possibilities may also be determined using the generated strand.