Cardholder Transaction Profiling via Hierarchical Segmentation
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Solution Overview
Problem
Card issuers and merchants face challenges in accurately profiling customers to predict their future behavior and retain loyalty, as existing systems struggle to effectively monitor and analyze transaction data across various merchant networks and product hierarchies.
Innovation Solution
A computer-based method and system for transaction-based profiling of cardholders, utilizing SKU-level data and other hierarchical levels to analyze transaction patterns, enabling targeted marketing and loyalty program optimization by clustering cardholders based on common transactions and generating real-time profiles for increased customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer profiling methods are used, then implementation is simple, but prediction accuracy of future cardholder behavior is insufficient
Solution Approach 1:
The patent segments customer transaction data into multiple hierarchical levels including merchant category codes, product categories, and individual transaction details. This segmentation allows the system to analyze specific behavioral patterns at different granularities, improving prediction accuracy without overwhelming complexity through structured data organization.
Solution Approach 2:
The patent introduces a profile event loop as an intermediary component that processes transaction data through multiple stages: data reception, event generation, profile updating, and prediction generation. This intermediary structure manages system complexity by breaking down the complex prediction task into manageable sequential operations.
2Reliability
If real-time transaction monitoring is implemented, then customer behavior prediction is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-defining profile templates and prediction models before actual transaction processing. The system pre-establishes the structure for customer profiles including various behavioral attributes and prediction algorithms, allowing real-time transactions to be quickly matched and processed without requiring complex on-the-fly model construction.
Solution Approach 2:
The profile event loop operates continuously, maintaining persistent customer profiles that are updated in real-time as transactions occur. This continuous processing ensures that behavioral predictions are always based on the most current data without requiring periodic batch processing, thereby reducing time delays while maintaining reliability.
3Measurement precision
If detailed SKU-level transaction data is collected, then profiling accuracy is improved, but data storage and processing complexity increase
Solution Approach 1:
The patent segments detailed SKU-level transaction data into hierarchical categories including merchant category codes, product categories, and transaction attributes. This segmentation allows the system to retain granular detail for accurate profiling while organizing data in a structured manner that simplifies processing and analysis at different levels of abstraction.
Solution Approach 2:
The patent transforms detailed transaction data into a multi-dimensional profile space where customer behavior is represented across multiple attributes and time periods. This dimensional transformation allows the system to process detailed SKU-level data efficiently by projecting it into a standardized profile structure that facilitates comparison and prediction across different customers and timeframes.
Data Source
AI summary
A computer-based method for managing a profile for a cardholder is provided. The cardholder having an account associated with a payment card. The method includes electronically receiving, at the computer, transaction information for a cardholder for transactions with at least a first business entity and a second business entity, the transaction information including data representing each transaction initiated by the cardholder using the payment card and at least one subdivision within the first business entity, the transaction information further including data representing each transaction initiated by the cardholder using the payment card and the second business entity. The method also includes electronically storing the transaction information within the database, generating a profile of the stored transaction information for the cardholder, the profile including a type, a recency and a frequency of transactions initiated by the cardholder using the payment card and the at least one subdivision, grouping the cardholder into a single cluster with other cardholders registered within the payment card network based on the profile of the cardholder and the other cardholders, and outputting marketing information based on the cluster.