Ontology Programming for Automated Call Center Data Analysis
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Solution Overview
Problem
Current data analysis systems in call centers require manual analysis to determine context and identify patterns, which is time-consuming and labor-intensive, necessitating the development of an automated solution for efficient knowledge extraction and funnel analysis.
Innovation Solution
The use of machine learning-based ontology programming that self-trains on communication data to detect meaningful terms, classify them into semantic concepts, and automatically identify patterns and sequences of events, leveraging speech and textual analysis combined with metadata to provide comprehensive analytical data without human oversight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to determine context and identify patterns in call center data, then measurement precision can be maintained through human judgment, but productivity deteriorates due to time-consuming and labor-intensive processes
Solution Approach 1:
The ontology programming system performs self-training by automatically processing and analyzing communication data to learn domain-specific language patterns, relationships between terms, and contextual meanings without requiring manual programming of each rule, thereby achieving both precision and productivity
Solution Approach 2:
Manual human analysis is replaced with an automated machine learning-based ontology system that uses algorithms to detect meaningful terms, classify them into semantic concepts, and identify patterns and sequences of events, substituting human mechanical judgment with automated computational processes
2Productivity
If automated analysis systems are implemented to improve productivity, then data processing speed increases, but measurement precision deteriorates due to lack of human context understanding
Solution Approach 1:
The system automatically trains itself on domain-specific communication data to learn contextual relationships, linguistic patterns, and semantic meanings, enabling the automated system to understand context as well as human analysts without requiring manual intervention
Solution Approach 2:
The ontology programming performs preliminary self-training by processing a defined corpus of communication data before actual analysis, learning domain-specific terminology, relationships, and contextual patterns in advance to ensure accurate context determination during automated analysis
3Measurement precision
If comprehensive ontology programming is used to improve measurement precision through detailed semantic classification, then analysis accuracy increases, but device complexity increases due to sophisticated machine learning requirements
Solution Approach 1:
The ontology programming system automatically builds and refines its own semantic classification framework by self-training on domain data, eliminating the need for complex manual ontology construction and reducing system complexity while maintaining high classification accuracy
Solution Approach 2:
The system adapts to the language used in specific domains by learning linguistic patterns, relationships between terms, and syntactical variations from the data itself, dynamically adjusting its classification parameters to match domain-specific characteristics without requiring pre-defined complex rules
Data Source
AI summary
Systems, methods, and media for the application of funnel analysis using desktop analytics and textual analytics to map and analyze the flow of customer service interactions. In an example implementation, the method includes: defining at least one flow that is representative of a series of events comprising at least one speech event, at least one Data Processing Activity (DPA) event, and at least one Computer Telephone Integration (CTI) event; receiving customer service interaction data comprising communication data, DPA metadata, and CTI metadata; applying the at least one flow to the customer service interaction data; determining if the customer service interaction data meets the at least one flow; and producing an automated indication based upon the determination.