Personalized Merchant Identifier Using Dynamic Transaction Data
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Solution Overview
Problem
Existing methods for personalizing customer experiences, particularly in matching merchants and customers based on location and payment options, are limited by reliance on static data and require manual interaction, leading to inefficiencies and inaccuracies.
Innovation Solution
A centralized system that collects customer and merchant data in near-real-time to deliver personalized recommendations for suitable merchants, using transaction data to derive merchant data elements and compare them with customer payment accessibility parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static historical data about merchants is used for personalization, then system complexity is reduced and ease of operation is improved, but measurement precision and reliability of merchant identification deteriorate due to data age
Solution Approach 1:
The system transitions from static historical data to dynamic near-real-time data collection. The centralized system continuously updates merchant data elements based on recent transaction data, ensuring that personalization is based on current merchant status rather than outdated information. This dynamic approach resolves the contradiction by maintaining ease of operation through automated data updates while significantly improving measurement precision.
Solution Approach 2:
The system changes the temporal parameter of data usage from historical/aged data to near-real-time data. By collecting and processing transaction data elements as they occur and using them to derive updated merchant data elements, the system maintains operational simplicity while improving the relevance and precision of merchant identification for personalization.
2Device complexity
If manual interaction with merchants is required to update data, then device complexity is reduced, but productivity and reliability of personalization deteriorate due to manual intervention delays
Solution Approach 1:
The system implements self-service by automatically collecting transaction data elements from merchants and deriving updated merchant data elements without requiring manual intervention. The centralized system autonomously processes this data to update personalization recommendations, significantly improving productivity while keeping device complexity manageable through standardized automated workflows.
Solution Approach 2:
The system replaces the mechanical/manual process of merchant data updates with an automated electronic system. Instead of manually interacting with merchants to update data, the system electronically collects transaction data elements, processes them through defined workflows, and automatically updates merchant profiles, thereby eliminating manual intervention delays and improving productivity.
3Device complexity
If merchants manually report parameter changes, then system complexity is reduced, but reliability of personalization deteriorates because merchants may inadvertently ignore notifications
Solution Approach 1:
The system implements a feedback mechanism where the centralized system collects transaction data elements, processes them to identify changes, and automatically updates merchant data elements. This closed-loop feedback system eliminates reliance on merchant-initiated notifications, ensuring reliable and consistent personalization updates without depending on merchant responsiveness or manual intervention.
Solution Approach 2:
The system replaces the unreliable manual notification and reporting mechanism with an automated electronic data collection and processing system. By continuously monitoring transaction data elements and automatically deriving updated merchant data, the system ensures reliable personalization updates independent of merchant actions, thereby improving reliability while maintaining acceptable system complexity.
4Reliability
If near-real-time data collection is implemented, then reliability and measurement precision of personalization are improved, but device complexity and loss of time increase
Solution Approach 1:
The system segments the data collection and processing function into a dedicated centralized system that handles transaction data elements separately from the personalization engine. This segmentation allows the centralized system to collect and process data in near-real-time, improving reliability, while the modular architecture manages complexity by separating concerns and allowing independent optimization of each component.
Solution Approach 2:
The centralized system acts as an intermediary between transaction data collection and personalization recommendation generation. It collects transaction data elements, processes them to derive updated merchant data elements, and then feeds this information to the personalization engine. This intermediary role enables near-real-time data processing to improve reliability while managing overall system complexity through a dedicated intermediate layer.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for a personalized merchant identifier. An example method includes receiving, by communications hardware, a set of transaction data elements, wherein the set of transaction data elements include an indication of a payment type or a merchant and deriving, by a personalization engine, a set of merchant data elements from the set of transaction data elements. The example method further includes receiving, by the communications hardware and from a user, one or more payment accessibility parameters and comparing, by the personalization engine, the one or more payment accessibility parameters to the set of merchant data elements to identify one or more compliant merchants, wherein the one or more compliant merchants comply with the one or more payment accessibility parameters. The example method further includes performing, by a notification circuitry and based on the one or more compliant merchants, an action.