Conversation Satisfaction Scoring Using Segmented NLP Analysis
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Solution Overview
Problem
Financial service providers face challenges in analyzing vast amounts of consumer data from digital conversations, particularly in online chat support, as existing solutions fail to collect, segment, and analyze conversation data streams for consumer sentiment, making it difficult to determine satisfaction levels.
Innovation Solution
A system integrating natural language processing algorithms with data science models to parse and analyze conversation data, segment it into contextual groupings, and determine user satisfaction scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If financial service providers use digital chat conversations for customer support, then operational efficiency and scalability are improved, but the ability to analyze consumer sentiment and determine satisfaction levels deteriorates
Solution Approach 1:
The patent replaces manual analysis of consumer sentiment with automated natural language processing algorithms. The system uses NLP models to parse conversation data, identify sentiment patterns, and generate satisfaction scores automatically, eliminating the need for human analysts to manually review each conversation for sentiment indicators.
Solution Approach 2:
The patent introduces an intermediary processing layer between the chat conversation and the satisfaction measurement. This intermediary consists of multiple processing stages including data collection, parsing, sentiment analysis models, and score generation, which together transform raw conversation data into actionable satisfaction metrics.
2Quantity of substance
If existing data analytics solutions are used, then some data processing capability is provided, but the ability to collect, segment, and analyze conversation data streams for consumer sentiment is insufficient
Solution Approach 1:
The patent segments the conversation data stream into discrete analytical components. The system divides the data collection process into separate modules for different data types (text, metadata), segments the analysis process into distinct NLP tasks (parsing, sentiment detection, score calculation), and organizes the output into structured satisfaction metrics, enabling precise measurement of consumer satisfaction.
Solution Approach 2:
The patent transforms raw conversation data into standardized satisfaction parameters. The system converts unstructured text conversations into quantifiable satisfaction scores and categorical sentiment indicators, changing the data parameters from raw textual representations to processed metrics that can be reliably measured and compared across different conversations.
Data Source
AI summary
A system for scoring user conversation satisfaction. The system comprises one or more memory devices storing instructions, and one or more processors configured to execute instructions to perform operations. The operations comprising receiving data corresponding to a conversation between the user and a third-party service provider. The operations further comprising parsing the data into conversation subsets, and analyzing each respective subset with a first model. The operations further comprising determining a user conversation satisfaction score based on the first model analyzed subset; storing, in a database, the parsed data subsets, and the determined conversation satisfaction score; and training a second model with the data stored in the database.