Consumer Sentiment Quantification via Transaction Data Correlation
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Solution Overview
Problem
Current systems lack an effective method to quantify consumer sentiment in real-time using transaction data while protecting user privacy and providing personalized advertisements, failing to adequately correlate offline and online consumer behaviors.
Innovation Solution
A system processes transaction data from credit, debit, and prepaid accounts to generate an emotional content index, correlating it with spending patterns to create a quantification model for consumer sentiment, which is then used to deliver personalized advertisements by integrating transaction data with external data sources like social networking and web browsing activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transaction data is processed to quantify consumer sentiment in real-time, then advertising relevance and ROI are improved, but user privacy protection becomes more difficult
Solution Approach 1:
The patent extracts only the necessary sentiment-indicative features from transaction data (spending patterns, frequency, categories) while leaving out personally identifiable information. This extraction approach enables real-time sentiment analysis without exposing full user privacy, resolving the contradiction between processing speed and privacy protection.
Solution Approach 2:
The system introduces an intermediary processing layer that aggregates and anonymizes transaction data before sentiment analysis. This intermediary layer acts as a buffer between raw transaction data and sentiment quantification, enabling real-time processing while maintaining user privacy through aggregation and anonymization techniques.
2Productivity
If transaction data is integrated with external data sources for personalized advertising, then advertising effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal sentiment quantification model that can process multiple data sources (transaction data, browsing data, social media data) through the same analytical framework. This multi-functional approach improves advertising effectiveness across different data sources while avoiding the complexity of building separate analysis systems for each data type.
Solution Approach 2:
The system standardizes external data sources by transforming them into common sentiment parameters that match the transaction data format. By changing the parameters of external data to align with the core sentiment model, the system integrates diverse sources effectively without requiring complex custom processing for each data type.
3Measurement precision
If detailed transaction data is analyzed for consumer behavior prediction, then prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The patent segments transaction data into distinct analytical categories (spending frequency, amount patterns, category preferences, temporal patterns) and processes each segment separately through specialized algorithms. This segmentation enables accurate behavior prediction by focusing computational resources on specific patterns rather than processing all data uniformly, reducing overall processing time while maintaining precision.
Solution Approach 2:
The system performs preliminary processing of transaction data to pre-compute sentiment indicators and behavioral patterns before actual prediction queries. By preparing and storing pre-calculated sentiment metrics in advance, the system can provide accurate consumer behavior predictions quickly without re-processing raw transaction data in real-time.
Data Source
AI summary
A computing apparatus is configured to quantify consumer sentiment at an aggregated or micro level using transaction data that records the transactions processed by a transaction handler of a payment system. A quantification model is generated based on correlating transaction data with respective emotional content indices extracted from data sources, such as regional news, weather, stock markets, movie themes, local sports, employment, traffic conditions, etc. Using the quantification model, consumer sentiment can be evaluated at various granularity levels, based on the granularity of the user group and the time period of the transaction data used in the quantification model.