Supervisor Impact Score Calculation via Sentiment Sampling
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Solution Overview
Problem
Current methods lack the ability to effectively measure and track the impact of supervisor actions on customer experience in contact centers, hindering improvements in supervisor skills, staffing, and customer experience.
Innovation Solution
A system and method for calculating a supervisor impact score by sampling customer interactions where a supervisor intervened, identifying the supervisor's intervention points, and using sentiment models to assess the impact on customer experience before and after the supervisor's actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervisor actions are tracked and measured, then customer experience improvement is enabled, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system comprising sentiment models, interaction samplers, and impact score calculators that mediate between raw interaction data and supervisor impact measurement. This intermediary layer processes and transforms complex data into meaningful metrics without requiring direct complex analysis of all interaction data, thus enabling reliable measurement while managing system complexity.
Solution Approach 2:
The patent replaces manual supervisor impact assessment with automated computational systems including sentiment analysis models and impact score calculators. This substitution of mechanical/manual processes with automated computational mechanisms enables reliable and consistent measurement of supervisor impact on customer experience without proportionally increasing operational complexity.
2Measurement precision
If comprehensive interaction data is analyzed, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent employs interaction sampling that selects a representative subset of customer interactions rather than analyzing all interactions comprehensively. The interaction sampler identifies and processes only those interactions where supervisor actions occurred, applying sentiment models to these sampled interactions to calculate impact scores. This partial analysis approach maintains measurement precision for supervisor impact while significantly reducing overall processing time and computational resources required.
Data Source
AI summary
Systems adapted to measure impact of supervisor actions and methods, and non-transitory computer readable media, include identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction; determining an impact score for each of a plurality of behavioral factors; aggregating the impact scores for the plurality of behavioral factors and determining an average of the impact scores to provide an overall impact score for the supervisor action; and performing an action automatically based on the overall impact score to improve contact center performance.