Automated Quality Evaluation System for Call Center Interactions
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Solution Overview
Problem
Traditional quality evaluation methods in call centers are limited by their dependence on human evaluators, leading to low evaluation capacity, subjective biases, and an inability to consider all relevant factors, resulting in non-representative and inefficient assessments that do not incorporate customer satisfaction or external factors.
Innovation Solution
An automated system that uses machine learning and AI to evaluate interactions by extracting features from historic data, assigning scores based on rules, and providing real-time alerts for exceptional interactions, thereby overcoming human limitations and biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual evaluation by human evaluators is used, then evaluation can be performed with simple system complexity, but evaluation capacity and productivity are low
Solution Approach 1:
The patent replaces the mechanical system of manual human evaluation with an automated computer-based system that uses machine learning models, AI algorithms, and data processing mechanisms to evaluate interactions, thereby dramatically increasing evaluation capacity while reducing dependence on human evaluators
Solution Approach 2:
The patent introduces an automated evaluation system as an intermediary between interactions and evaluation results, using trained models and algorithms to mediate the evaluation process, which enables scalable high-capacity evaluation without proportionally increasing human evaluator workload
2Measurement precision
If human evaluators perform assessments, then evaluation can be conducted with limited factors considered, but subjective biases are introduced
Solution Approach 1:
The patent replaces human subjective judgment with automated algorithmic evaluation that applies consistent objective criteria across all interactions, eliminating evaluator-specific biases while maintaining systematic evaluation through programmed rules and machine learning models
Solution Approach 2:
The patent transforms the evaluation process from subjective human assessment to objective parameter-based measurement by quantifying interaction features into numerical scores using predefined criteria and algorithms, thereby improving measurement precision and objectivity
3Measurement precision
If sampling of interactions is used for evaluation, then evaluation time is reduced, but evaluation accuracy and representativeness deteriorate
Solution Approach 1:
The patent replaces the time-consuming manual sampling process with automated system capability to evaluate all interactions comprehensively, using machine learning models that can process large volumes of data efficiently, thereby achieving both high accuracy and reasonable time consumption
Solution Approach 2:
The patent performs preliminary automated processing and feature extraction from all interactions before final evaluation, preparing data in advance so that comprehensive evaluation can be conducted efficiently without excessive time loss, enabling accurate assessment of all rather than sampled interactions
4Reliability
If post-activity evaluation by another person is performed, then evaluator independence is maintained, but real-time intervention capability is lost
Solution Approach 1:
The patent performs evaluation during or immediately after interactions using automated real-time processing, enabling timely identification of quality issues while maintaining objective assessment through system independence, thus allowing both reliable evaluation and timely intervention
Solution Approach 2:
The patent implements automated feedback mechanisms that provide real-time or near-real-time evaluation results to supervisors and agents, enabling immediate intervention and corrective action while maintaining evaluation reliability through systematic automated assessment
Data Source
AI summary
A method and apparatus for automatic quality evaluation of an activity related to an organization, such as an agent of an organization which interacts with a calling party, a product, a campaign or the like, based on any combination of one or more of the following: the interaction itself and particularly its vocal part; meta data related to the call, to the call parties or to the environment; information extracted from the call or general information. The method may be activated off-line or on-line, in which case all alert can be generated for one or more calls.