Paired Merchant Preference Index for Payment Network Analysis
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Solution Overview
Problem
Financial institutions lack insight into customer purchasing behavior, making it difficult to target merchant offers effectively and retain customers, as existing systems do not adequately analyze transaction data to identify preferred merchant combinations.
Innovation Solution
A system that calculates a paired merchant preference index by analyzing transaction data from a payment network, using a transactions database, transaction indexing component, and preference index component to determine the strength of association between merchant pairs, allowing issuers to select strongly associated merchants for targeted offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transaction data is collected and analyzed to identify customer purchasing behavior patterns, then customer insight and targeting capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the analysis into two distinct components: a transaction indexing component that organizes raw transaction data by merchant identifiers, and a preference index component that calculates association metrics. This segmentation allows each component to specialize in one task, reducing overall system complexity while enabling comprehensive customer behavior analysis through the coordinated operation of these modular components.
Solution Approach 2:
The transaction indexing component serves as an intermediary between the raw transaction data and the preference index calculation. It processes and structures the data in a format suitable for analysis, acting as a mediator that simplifies the subsequent preference index computation by pre-organizing transaction records by merchant identifier and payment card identifier.
2Measurement precision
If paired merchant preference index is calculated to identify strongly associated merchant pairs, then offer targeting precision is improved, but computational resources and processing time increase
Solution Approach 1:
The transaction indexing component performs preliminary action by pre-processing and organizing transaction data before the preference index calculation begins. It creates an indexed structure that groups transactions by merchant identifier and payment card identifier, so that when the preference index component needs to calculate associations between merchant pairs, the data is already in the optimal format, significantly reducing computation time.
Solution Approach 2:
The system calculates preference indices for specific merchant pairs that are relevant to offer targeting, rather than computing all possible combinations. This partial action approach focuses computational resources on the most valuable associations while avoiding unnecessary calculations, balancing precision with processing efficiency.
3Measurement precision
If comprehensive transaction data is stored and processed, then analysis accuracy is improved, but data storage requirements and system resource consumption increase
Solution Approach 1:
The transaction indexing component extracts only the essential elements needed for preference index calculation: merchant identifiers and payment card identifiers. Rather than storing and processing all transaction details, the system extracts and indexes only the key fields required for analyzing merchant associations, significantly reducing storage requirements while maintaining analysis accuracy.
Solution Approach 2:
The system segments the transaction data into indexed categories by merchant identifier and payment card identifier. This segmentation allows the preference index component to efficiently query and analyze only the relevant subsets of data needed for specific merchant pair comparisons, rather than processing the entire transaction database, thus reducing computational and storage overhead.
Data Source
AI summary
A system for determining a preference index comprises a transactions database storing transaction data indicative of a plurality of transactions carried out over a payment network, the transaction data comprising, for respective transactions, payment card identifiers, merchant identifiers, merchant category codes, issuer identifiers, payment device types, geographical locations, transaction amounts, transaction times and transaction dates. The system also comprises a transaction indexing component in communication with the transactions database, the transaction indexing component being configured to generate a database query to query the transactions database to retrieve transactions for a plurality of payment cards within a predetermined time window; and a preference index component. The preference index component is configured to compute a paired merchant preference index for a first merchant matching criterion and a second merchant matching criterion by: generating, from at least a subset of the retrieved transactions, a set of payment card identifiers of payment cards which meet both the first merchant matching criterion and the second merchant matching criterion; generating paired data for the first merchant matching criterion and the second merchant matching criterion, the paired data comprising respective pairs of transaction values for respective members of the set of payment card identifiers; and computing at least one aggregate quantity using the paired data.