Purchasing Intent Forecasting via Multi-Source Data Integration
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Solution Overview
Problem
Current methods fail to optimally integrate online behavior and additional information to accurately forecast purchasing intent and immediacy of specific product purchases, lacking in precision when evaluating entities' intentions to buy by category, make, model, year, brand, or reputation.
Innovation Solution
A system and method that derive purchasing intent probabilities by combining online actions with additional entity-related information, using comprehensive mathematical functions to generate purchasing intensity values, considering web searching behavior, demographic data, purchasing history, and geographic locations, and visually rendering map images to indicate relevant locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If online behavior data alone is used to forecast purchasing intent, then the system is simple to operate, but the measurement precision of purchasing intent is insufficient
Solution Approach 1:
The patent combines multiple data sources including online behavior data, demographic information, purchasing history, and geographic location data into a unified forecasting system. This merging of diverse data types enables comprehensive assessment of purchasing intent while maintaining system operability through automated integration processes.
Solution Approach 2:
The system creates a multi-functional platform that processes various types of data (online behavior, demographic, geographic, purchasing history) through a single integrated framework. This universal system can forecast purchasing intent across different product categories and entity types while maintaining consistent operational procedures.
2Measurement precision
If multiple data sources are integrated to improve purchasing intent forecast, then the measurement precision increases, but the device complexity increases
Solution Approach 1:
The patent segments the forecasting system into distinct functional modules: online behavior analysis module, demographic data module, purchasing history module, and geographic location module. Each module processes specific data types independently before integration, reducing overall system complexity while maintaining comprehensive forecasting capability.
Solution Approach 2:
The system introduces intermediary processing layers that standardize and normalize data from different sources before integration. These intermediaries handle data cleaning, format conversion, and compatibility adjustments, enabling seamless integration of diverse data sources without increasing operational complexity for end users.
3Measurement precision
If comprehensive mathematical functions are used to generate purchasing intensity values, then the measurement precision improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent transforms complex mathematical relationships into standardized parameters such as purchasing intensity scores and probability values. These transformed parameters simplify the interpretation of comprehensive mathematical functions while maintaining measurement precision, enabling easier detection and measurement of purchasing intent.
Solution Approach 2:
The system replaces manual analysis of complex mathematical functions with automated computational algorithms. This substitution eliminates the need for manual detection and measurement of purchasing intent, reducing difficulty while maintaining or improving precision through consistent algorithmic processing.
4Measurement precision
If geographic location data is integrated with online behavior, then the purchasing intent forecast accuracy improves, but the loss of information handling complexity increases
Solution Approach 1:
The patent adds geographic location as a spatial dimension to the traditional online behavior data framework. This dimensional expansion enables more precise purchasing intent forecasting by contextualizing online behavior with physical location information, while automated processing handles the increased data complexity.
Data Source
AI summary
A system and method for generating purchasing interest values in relation to purchasing a product or service, by category, brand, make, or model. The method includes associating a user identity with recordations of activity in requesting information from assets accessible by addressing universal resource locators, such as applying a web browser to render web pages addressable by registered domain names of the World Wide Web; additional information such as purchasing history, residence address and income level; and estimations of proximity and ease of travel between a geographic location associated with the user identity and a point of sales or services of a product or service type, category, brand, make, or model. The product or service may be related to or comprise an automobile. A map is rendered that separately associates geographic locations with individual user identities. Marketing communications are sent to electronic and/or postal addresses associated with user identities.