Enterprise Speech Intelligence Analysis for Call Center Repeat Calls
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Solution Overview
Problem
Call centers face challenges in identifying and addressing the causes of repeat calls and chronic callers, leading to increased operational costs, customer dissatisfaction, and lost revenue due to inadequate initial responses.
Innovation Solution
A method that uses a data processor to analyze call center data, identifying call series based on caller identifiers, call parameters, and audio content analysis to construct and analyze sequences of calls, generating reports that highlight repeat caller patterns, topics, and agent performance, enabling targeted improvements in customer service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If call centers manually monitor and investigate customer interactions to identify repeat call causes, then customer satisfaction can be improved through targeted service improvements, but operational costs increase and productivity decreases due to the time-consuming nature of manual analysis
Solution Approach 1:
The patent replaces manual mechanical analysis of call recordings with automated speech intelligence analysis using audio processing algorithms. The system automatically transcribes calls, identifies topics, detects emotions, and generates insights without human intervention, thereby maintaining high reliability in customer satisfaction monitoring while dramatically improving operational productivity
Solution Approach 2:
The system enables self-service analysis where the call center database automatically processes and analyzes call data without requiring manual investigation. The automated generation of call series reports and repeat caller identification allows the system to serve itself in monitoring and improving customer satisfaction, eliminating the need for dedicated manual analysis resources
2Loss of energy
If call centers implement comprehensive call analysis to identify chronic callers and repeat call patterns, then lost revenue can be reduced through improved first-call resolution, but device complexity and measurement difficulty increase due to the need to process multiple call parameters and audio content
Solution Approach 1:
The patent segments the complex call analysis task into distinct modular components: call identification using caller identifiers, call series formation based on temporal and contextual parameters, audio transcription and topic identification, emotion detection, and report generation. This segmentation reduces system complexity by breaking down the comprehensive analysis into manageable, independent modules that can be processed sequentially
Solution Approach 2:
The system introduces an intermediary layer of speech intelligence analysis that bridges raw call data and actionable insights. This intermediary processing layer automatically transcribes audio, identifies key topics, and extracts meaningful patterns, thereby reducing the complexity of directly analyzing raw audio content while enabling effective identification of chronic callers and revenue loss patterns
3Measurement precision
If call centers analyze multiple call parameters including audio content, caller identifiers, and call series patterns, then measurement precision of repeat call causes can be improved, but the difficulty of detecting and measuring increases due to the volume and variety of data to be processed
Solution Approach 1:
The patent implements a universal speech intelligence analysis platform that handles multiple types of data (caller identifiers, audio content, call timestamps, agent information) through a single integrated system. The system performs multiple functions including transcription, topic identification, emotion detection, and pattern recognition within one unified framework, thereby improving measurement precision without proportionally increasing detection and measurement difficulty
Solution Approach 2:
The system performs preliminary processing of call data by automatically transcribing audio content and identifying topics before the main analysis of repeat call patterns. This preliminary action prepares the data in advance, making subsequent measurements of call series and chronic caller identification more precise while reducing the overall complexity of processing raw audio data
Data Source
AI summary
A method includes accepting, via an input interface, a caller identifier parameter and a target value of at least one call series parameter; identifying, using a data processor, a plurality of calls each associated with the caller identifier parameter from amongst a set of calls stored in a call center database; analyzing, using the data processor, the identified plurality of calls to determine a value of the at least one call series parameter for the identified plurality of calls; comparing, using the data processor, the determined value of the at least one call series parameter with the target value of the at least one call series parameter; and defining, using the data processor, the identified plurality of calls as a call series based at least in part on results of the comparing.