Merchant Recommendation Engine Using Transaction Volume Analysis
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Solution Overview
Problem
Existing merchant recommendation systems require significant human effort to determine customer interests and rank merchants, making them inefficient in providing relevant recommendations without explicit user input.
Innovation Solution
A computer-implemented method that generates a cardholder profile based on transaction data to recommend merchants by analyzing transaction volumes and life stages, eliminating the need for explicit user interest input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known recommendation systems require users to affirmatively indicate interests and rank merchants, then recommendation accuracy can be improved, but user effort and system complexity increase significantly
Solution Approach 1:
The system automatically generates merchant recommendations by analyzing transaction data without requiring users to manually indicate interests or rank merchants. The processor examines purchase patterns, categories, and frequencies to autonomously determine merchant rankings, allowing the system to serve itself rather than requiring active user participation.
Solution Approach 2:
The patent replaces the mechanical system of manual user input (surveys, rankings, interest declarations) with an automated data analysis system. The processor substitutes human effort by algorithmically analyzing transaction records to infer user preferences and generate recommendations, eliminating the need for direct user engagement in the recommendation process.
2Loss of information
If manual surveys and interest declarations are used to generate recommendations, then user preferences can be captured, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary analysis of transaction data to capture user preferences before recommendations are needed. By continuously analyzing purchase patterns, categories, and spending behaviors in the background, the system pre-processes information so that recommendations can be generated immediately without requiring users to spend time completing surveys or declarations at the moment of need.
Solution Approach 2:
The patent replaces time-consuming manual surveys and interest declarations with automated analysis of existing transaction data. The processor continuously monitors and analyzes purchase patterns, extracting user preferences from actual behavior rather than requiring users to spend time articulating their interests, thereby capturing preference information more efficiently.
3Extent of automation
If transaction data analysis is used to generate recommendations, then automation and efficiency improve, but data processing complexity increases
Solution Approach 1:
The system segments the complex task of merchant recommendation into distinct analytical components: analyzing transaction categories, evaluating purchase frequencies, determining spending patterns, and ranking merchants based on multiple independent factors. This segmentation allows the processor to handle complexity in manageable modules rather than attempting to process all factors simultaneously, reducing overall system complexity while maintaining high automation.
Data Source
AI summary
A computer-implemented method for recommending a merchant based on transaction data is provided. The method is implemented using an analyzer computing device in communication with one or more memory devices. The method includes generating a profile indicating a stage of life of a cardholder, based at least in part on first transaction data stored in the one or more memory devices. The first transaction data is associated with one or more purchases made by the cardholder through a payment network. The method additionally includes retrieving, from the one or more memory devices, second transaction data associated with a plurality of sales from a first merchant, determining a transaction volume associated with the first merchant, and generating a recommendation for the cardholder to purchase goods from the first merchant, based at least in part on the determined transaction volume and the profile.