Real-Time Conversational Metrics Prediction From Normalized Transcripts
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Solution Overview
Problem
Existing systems struggle to accurately predict key metrics and moments in conversations across various channels like chat, SMS, and phone, failing to correlate sentiment analysis with actual customer survey scores and lacking a robust model architecture for normalized data sources.
Innovation Solution
A conversational analysis system utilizing a Model Production Module and Conversational Analysis Module, which includes data retrieval, preprocessing, encoding, and machine learning models to predict key metrics and moments in conversations, employing neural networks and named entity recognition for data normalization and encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentiment analysis is used to analyze conversations, then some signal of customer emotion is obtained, but the accuracy correlation to actual customer survey scores is weak
Solution Approach 1:
The system transforms the approach by changing from simple sentiment analysis to predicting specific quantitative metrics (NPS, CSAT, Sales Conversion, FCR) directly. This parameter change enables accurate correlation with actual customer survey scores by focusing on metric prediction rather than generic sentiment detection.
Solution Approach 2:
The patent replaces traditional sentiment analysis mechanisms with a machine learning model architecture that directly predicts key conversational metrics. This substitution uses trained models with normalized data sources to achieve high-quality accurate predictions correlated with actual survey scores.
2Loss of information
If traditional analysis methods are used, then conversation data can be processed, but key moments and inflection points cannot be identified
Solution Approach 1:
The system introduces an intermediary component - a trained machine learning model architecture - that processes normalized conversation data to identify key moments and inflection points. This intermediary transforms raw conversation data into actionable insights about critical conversation moments.
Solution Approach 2:
The system performs preliminary normalization of data sources before analysis, preparing the data in advance with proper formatting and standardization. This preliminary action enables the model to accurately identify key moments and inflection points during the actual analysis phase.
3Productivity
If modern machine learning techniques are applied, then analysis capability is enhanced, but model architecture fails to utilize normalized data sources effectively
Solution Approach 1:
The system creates equipotentiality by ensuring all data sources are normalized to a common standard before being fed into the machine learning model. This uniform normalization enables the model architecture to effectively utilize all inputs, achieving high prediction accuracy across different data sources.
Data Source
AI summary
A system and a method are disclosed for alerting a manager device to an occurrence of an event an agent device during a conversation between the agent device and an external party. N an embodiment, a processor receives transcript data during a conversation between the agent device and the external party. The processor normalizing the transcript data, and inputs the normalized transcript data into a machine learning model, the machine learning model trained to identify an inflection point in the conversation. The processor receives, as output from the machine learning model, a measure of notability of the normalized transcript data. The processor determines whether the measure of notability corresponds to an inflection point, and, responsive to determining that the measure of notability corresponds to an inflection point, alerts the manager device.