Location-Based Market Recommendations via Effective Area of Influence
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Solution Overview
Problem
Current systems for harnessing transaction history data are limited in fully utilizing location-based information to provide effective market recommendations, failing to accurately determine consumer preferences and demand.
Innovation Solution
A method and system that identifies a cardholder's Effective Area of Influence (EAI) by analyzing transaction data, using reverse geo-coding to map locations to statistical areas, determining the subregion with the highest number of transactions, and accessing geographically classified statistics to generate location-based market recommendations for merchants and issuers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transaction history data is stored and analyzed, then consumer behavior tracking capability is improved, but the ability to generate actionable market recommendations remains insufficient
Solution Approach 1:
The patent segments consumer behavior data into distinct categories including transaction history, location data, time stamps, and merchant information. This segmentation allows for targeted analysis of specific behavior patterns rather than treating all data uniformly, enabling more precise market recommendations.
Solution Approach 2:
The patent adds geographical location as a new dimension to traditional transaction history analysis. By incorporating location data with coordinates, distance calculations, and spatial relationships, the system transforms one-dimensional transaction records into multi-dimensional consumer behavior profiles, enabling location-based market insights.
2Loss of information
If basic transaction history is reviewed, then buying pattern understanding is improved, but consumer preference accuracy deteriorates
Solution Approach 1:
The patent introduces location data as an intermediary element that connects transaction history with consumer preferences. Location information acts as a mediator that provides context to buying patterns, revealing preferences for specific areas, types of establishments, and spatial behaviors that cannot be determined from transaction records alone.
Solution Approach 2:
The patent performs preliminary location-based analysis and segmentation of consumer data before generating market recommendations. By pre-processing data to identify location patterns, preferred areas, and spatial behaviors in advance, the system prepares refined consumer profiles that improve the accuracy of subsequent preference measurements and recommendations.
3Loss of information
If transaction locations are tracked, then consumer routine understanding is improved, but actionable market insight capability deteriorates
Solution Approach 1:
The patent dynamically processes location data to identify patterns in consumer routines such as frequent visits, preferred time periods, and habitual paths. The system adapts its analysis based on detected patterns, transforming static location tracking into dynamic insights about consumer behavior rhythms and preferences.
Solution Approach 2:
The patent implements feedback loops where location-based consumer routine analysis informs market recommendations, which in turn can influence future consumer behavior. The system continuously refines its understanding by comparing predicted behaviors with actual location data, improving the accuracy of actionable market insights over time.
Data Source
AI summary
A computer-implemented method is disclosed. The method includes using reverse geo-coding to determine user transaction locations for a user, determining a number of user transactions for the user that correspond to each of a plurality of statistical area levels, determining a subdivision of each of the plurality of statistical area levels that has the highest number of domestic card present transactions for the user, identifying an effective area of influence (EAI) for the user, based on a determination of a statistical area level that has the highest number of domestic card present transactions for the user, and accessing geographically classified statistics from public data sources related to one or more of the plurality of the statistical area levels. A location based market recommendation is generated based on the geographically classified statistics and the effective area of influence.