Contact Center Analytics System Neutralizing Agent Variability
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Solution Overview
Problem
Current contact center analytics are limited by relying on unstructured audio data and manual analysis, failing to thoroughly evaluate business processes and customer experiences due to agent variability, which hinders efficient evaluation of interaction types and customer segments.
Innovation Solution
A computer program that aggregates and analyzes voice data, agent activity data, customer activity data, and customer history data to generate business process analytics, including dissatisfaction metrics, by separating and mining voice data using linguistic and distress models, and categorizing interactions to provide systematic insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of unstructured audio data is used, then implementation simplicity is maintained, but analysis precision and comprehensiveness deteriorate
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computer-based analysis systems. Voice data is processed through automated transcription, natural language processing, and sentiment analysis algorithms, eliminating the need for manual listening and interpretation while significantly improving analysis precision and consistency.
Solution Approach 2:
The patent introduces intermediary processing layers including speech-to-text conversion, natural language processing models, and sentiment analysis algorithms that bridge the gap between raw audio data and actionable insights. These intermediaries transform unstructured audio into structured, analyzable data formats.
2Loss of information
If only audio conversation data is collected, then data collection simplicity is maintained, but information completeness deteriorates
Solution Approach 1:
The patent merges multiple data sources including voice conversations, keypad inputs, screen displays, and customer profile information into a unified analysis framework. This integration creates a comprehensive view of the customer experience that captures both explicit interactions and contextual information.
Solution Approach 2:
The patent creates a multi-functional data collection system that simultaneously captures various types of interaction data through a single integrated platform. The system handles voice, text, visual, and contextual data collection uniformly, enabling comprehensive analysis without requiring separate specialized systems for each data type.
3Measurement precision
If agent variability is included in analysis, then individual performance assessment is improved, but business process evaluation accuracy deteriorates
Solution Approach 1:
The patent segments the analysis into two distinct components: business process evaluation and individual agent performance assessment. By separating these analytical dimensions, the system can evaluate business process effectiveness independently of agent variability while maintaining the ability to assess individual performance through separate metrics and comparisons.
Solution Approach 2:
The patent applies different analytical approaches to different aspects of the data. Business process evaluation uses aggregated, normalized metrics that eliminate agent-specific variations, while agent performance assessment uses individualized metrics that capture personal strengths and weaknesses. Each analysis type receives the appropriate level and type of detail.
4Productivity
If systematic computer-based analysis is implemented, then analysis precision is improved, but implementation complexity increases
Solution Approach 1:
The patent implements self-service capabilities where the system automatically processes and analyzes data without requiring extensive manual configuration or intervention. Automated transcription, sentiment analysis, and insight generation occur without human input, reducing operational complexity while maintaining high analysis productivity.
Solution Approach 2:
The patent transforms complex unstructured data into standardized parameters and metrics that can be systematically processed. By converting voice data into sentiment scores, conversation topics, and interaction patterns, the system creates uniform parameters that simplify subsequent analysis while capturing the full complexity of customer interactions.
Data Source
AI summary
A method and system for aggregating data associated with a plurality of interactions between at least one customer and at least one agent for generating business process analytics is provided. The method is implemented by a non-transitory computer readable medium having a plurality of code segments and includes selecting a range of the organized plurality of agents and identifying a plurality of interactions associated with an organized plurality of agents within the selected range, receiving voice data associated with each of the identified interactions and analyzing the voice data, agent call activity data, customer call activity data, and customer history data associated with each of the identified interactions, and generating business process analytics for the identified interactions.