Automated Customer Intent Analysis via Neural Vector Clustering
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Solution Overview
Problem
Current methods for analyzing customer interactions are inefficient due to the reactive and hurried nature of customer service representative notes, which are often incomplete or skipped, and the time-consuming process of analyzing lengthy conversations to identify customer intent, making it difficult to understand and standardize actionable information.
Innovation Solution
A computerized method using advanced text processing, vectorization, and clustering techniques to automatically generate customer intent information and a hierarchy of customer issues from unstructured interaction transcripts and agent notes, leveraging neural networks to convert text into multidimensional vectors and cluster similar interactions for standardized analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customer service representatives manually analyze and note customer interactions, then customer insights can be captured, but the analysis process becomes time-consuming and notes are often incomplete or skipped due to workload pressure
Solution Approach 1:
The patent replaces the manual mechanical process of CSR note-taking with an automated neural network system that processes interaction transcripts. The system uses natural language processing to automatically generate interaction summaries and extract customer intents, eliminating the need for manual analysis while preserving all customer insights.
Solution Approach 2:
The system enables self-service by allowing the interaction analysis process to perform itself automatically. The neural network independently processes transcripts, generates summaries, extracts intents, and creates issue hierarchies without human intervention, freeing CSRs from this repetitive task while maintaining comprehensive information capture.
2Loss of information
If customer service representatives take detailed notes during interactions, then more customer information is captured, but the interaction conclusion is delayed and agents cannot handle the next customer quickly
Solution Approach 1:
The system performs preliminary action by automatically generating interaction summaries and extracting customer intents in real-time or near-real-time during or immediately after the interaction. This preliminary processing captures all necessary customer details without requiring the agent to spend additional time on note-taking, allowing the agent to conclude the interaction and move to the next customer promptly.
3Measurement precision
If customer interactions are analyzed offline in detail, then comprehensive customer insights are obtained, but the process becomes tedious and time-consuming due to lengthy conversations with non-actionable elements
Solution Approach 1:
The system applies extraction by using the neural network to identify and extract only the actionable customer information from lengthy interactions. The model filters out non-actionable elements such as greetings, pauses, and small talk, focusing specifically on extracting customer intents and issues. This extraction process maintains high measurement precision for intent identification while dramatically reducing the effective analysis time by processing entire transcripts efficiently.
Solution Approach 2:
The system changes parameters by transforming the analysis approach from manual sequential reading to automated parallel processing. The neural network processes the entire interaction transcript simultaneously, using learned patterns to quickly identify intents and issues, thereby changing the time parameter from hours of manual analysis to seconds of automated processing while maintaining or improving accuracy.
4Productivity
If customer service representatives standardize their notes, then analysis efficiency improves, but the standardization process itself requires significant time and effort
Solution Approach 1:
The system replaces the mechanical process of manual standardization with automated natural language processing. The neural network inherently produces standardized output in the form of structured interaction summaries and extracted intents, eliminating the need for separate standardization efforts while maintaining high analysis efficiency.
Data Source
AI summary
Methods and apparatuses are described for automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues. A server captures computer text segments including a first portion comprising a transcript of an interaction and a second portion comprising notes about the interaction. The server generates interaction embeddings corresponding to the first portion of the computer text segment for a trained neural network. The server executes the neural network using the interaction embeddings to generate an interaction summary for each computer text segment. The server converts each interaction summary into a multidimensional vector and aggregates the multidimensional vectors into clusters based upon a similarity measure. The server aligns the clusters of vectors with attributes of the interaction summaries to generate a hierarchical mapping of customer issues.