Call-Modeling System for Real-Time Sales Conversation Analysis
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Solution Overview
Problem
Sales conversations are largely unanalyzed, preventing optimization for desired outcomes due to the lack of accessible content for modeling, despite advances in automatic speech recognition and natural language processing.
Innovation Solution
A call-modeling system that analyzes voice conversations in real-time using features like transcripts, emotional signals, and personality traits to generate probabilities and on-call guidance for improving conversation outcomes, incorporating offline and real-time analysis components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sales conversations are left unanalyzed, then the system complexity remains low, but the ability to optimize conversation outcomes is lost
Solution Approach 1:
The system segments the conversation analysis into distinct modules: automatic speech recognition (ASR) for transcription, natural language processing (NLP) for topic modeling, and machine learning classifiers for outcome prediction. Each module handles a specific aspect of the analysis, allowing the complex task to be divided into manageable components that can be processed independently and combined to achieve comprehensive conversation optimization
Solution Approach 2:
The patent introduces intermediate processing layers between the raw conversation data and the optimization outcomes. Transcripts serve as intermediaries between spoken language and analytical processing, while topic models and feature extractions act as intermediaries between raw text and predictive analytics. These intermediaries transform unstructured conversation data into structured information that can be effectively analyzed and acted upon
2Loss of information
If comprehensive conversation analysis is implemented, then the ability to identify feature requests improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of conversation data by generating transcripts, extracting topics, and identifying key features before full analysis is required. This preliminary action prepares the data in advance, creating structured representations that can be quickly queried and analyzed when optimization decisions are needed, reducing the time required for comprehensive analysis
Solution Approach 2:
The patent replaces manual analysis of conversations with automated computational systems. Machine learning algorithms and natural language processing models substitute for human analysts, enabling comprehensive processing of conversation content at scale without proportionally increasing processing time. The automated systems can analyze multiple conversations simultaneously and identify patterns that would be difficult for humans to detect efficiently
Data Source
AI summary
A feedback identification system to automatically determine product feature requests by analyzing conversations of representatives with customers. The feedback identification system retrieves recordings of various conversations, extracts features of the conversations, and analyzes the features to determine a set of features that is indicative of a feature request. A feature request is a request for adding a specified functionality to a product. The set of features is analyzed to generate a feedback manifest, which includes the feature request (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.