Call Center Agent Performance Scoring via ML Sentiment Analytics
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Solution Overview
Problem
Manual evaluation of agent performance in call centers is time-consuming, inefficient, and highly subjective, making it difficult to assess agents across an enterprise effectively for workforce optimization and targeted coaching.
Innovation Solution
A system and method using machine learning to generate behavioral metrics by transcribing and analyzing audio recordings of calls, extracting text-based, sentiment-based, and prosody-based features, and training a model to produce scores for agent evaluation, which can be used for coaching and performance improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation by supervisors is used, then agent performance can be assessed, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an automated machine learning system. The ML model automatically analyzes call data, extracts features, and generates performance scores, eliminating the need for supervisors to manually listen and evaluate calls, thus resolving the contradiction between assessment accuracy and time consumption
Solution Approach 2:
The system enables self-evaluation by having agents automatically assessed through the ML model without requiring supervisor intervention. The automated system processes calls and generates performance metrics independently, allowing continuous evaluation without human time investment
2Measurement precision
If manual evaluation is used, then agent performance can be assessed, but the evaluation is highly subjective
Solution Approach 1:
The patent replaces subjective human judgment with an objective machine learning system. The ML model uses consistent algorithms and features to evaluate all agents uniformly, eliminating personal biases and subjectivity inherent in manual supervisor evaluations, thereby improving measurement precision and objectivity
Solution Approach 2:
The system transforms subjective performance assessment into objective quantitative measurements by extracting specific features (text-based, audio-based, sentiment-based) and converting them into standardized scores. This parameter transformation enables consistent, unbiased evaluation across all agents
3Productivity
If automated machine learning evaluation is implemented, then evaluation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the evaluation system into distinct modular components: data collection module, feature extraction module (text-based, audio-based, sentiment-based), model training module, and scoring module. This segmentation manages system complexity by organizing functions into separate, manageable units that can be independently developed and maintained
4Measurement precision
If comprehensive feature extraction is performed, then evaluation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent divides feature extraction into three distinct categories: text-based features (from call transcripts), audio-based features (from call recordings), and sentiment-based features (from tone and emotion analysis). This segmentation allows comprehensive feature extraction while managing complexity through organized, modular processing of different feature types
Data Source
AI summary
A system and method for generating an agent behavioral analytics including extracting text-based features, sentiment-based features, and prosody-based features from tagged calls; training a machine learning behavioral model, based on the text-based features, the sentiment-based features, and the prosody-based features extracted from the tagged calls and an at least one score associated with an at least one behavioral metric of the tagged calls, to produce a trained machine learning behavioral model; extracting text-based features, sentiment-based features, and prosody-based features from an incoming call; and using the trained machine learning behavioral model to produce an at least one behavioral label for the agent in the incoming call for the at least one behavioral metric, based on the text-based features of the incoming call, the sentiment-based features of the incoming call and the prosody-based features of the incoming call.