Transaction Data Segmentation for Targeted Offer Accuracy
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Solution Overview
Problem
Current systems for processing transaction data lack effective methods to provide personalized and targeted advertisements/offers to users based on their transaction patterns and location, failing to efficiently utilize real-time data for enhancing user experiences and merchant transactions.
Innovation Solution
A system that processes transaction data to generate aggregated spending profiles, correlates online and offline activities, and uses mobile applications to push offers to users based on their location, transaction history, and real-time shopping behavior, integrating AI for negotiating offers and providing real-time messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transaction data is processed to generate aggregated spending profiles and push targeted offers to users, then the accuracy and relevance of advertisements improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments transaction data into distinct categories (online transactions, offline transactions, shopping behavior patterns) and processes each segment separately to generate specific user profiles. This segmentation allows the system to manage complexity by handling data in organized chunks rather than as a monolithic dataset, enabling accurate targeted advertising through structured profile generation.
Solution Approach 2:
The patent introduces intermediary components including a transaction handler that mediates between payment processing systems and the offer delivery system, and a user profile generator that acts as an intermediary between raw transaction data and targeted offers. These intermediaries structure and process data systematically, reducing overall system complexity while maintaining high targeting accuracy.
2Loss of time
If real-time transaction data is analyzed and offers are pushed immediately to mobile devices, then the timeliness of promotions improves, but the data processing speed and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-processing transaction data in real-time, maintaining updated user spending profiles and behavior patterns before promotional opportunities arise. This allows the system to immediately push relevant offers when triggers occur without requiring intensive real-time analysis, thus maintaining timeliness while managing processing efficiency.
Solution Approach 2:
The patent implements continuous data collection and profile updating mechanisms that operate constantly in the background, ensuring user profiles are always current without requiring intensive batch processing. This continuous action maintains data freshness and timeliness of offers while distributing computational load efficiently over time rather than concentrating it in intensive processing cycles.
3Adaptability or versatility
If comprehensive user transaction data is collected and analyzed, then the personalization of offers improves, but the amount of data to be processed and stored increases
Solution Approach 1:
The system extracts only the most relevant features and patterns from comprehensive transaction data to create condensed user profiles. Instead of processing and storing all raw transaction details, the system extracts key spending patterns, preferences, and behavior indicators, achieving high personalization with reduced data volume through selective extraction of essential information.
Solution Approach 2:
The patent applies local quality by creating specialized, context-specific user profiles that contain only the data relevant to particular offer types or merchant categories. Rather than maintaining one comprehensive profile for all scenarios, the system generates tailored profile views with appropriate data granularity for each specific personalization context, reducing overall data storage requirements while maintaining personalization quality.
Data Source
AI summary
A computing apparatus is configured to formulate and adjust offers to users of mobile devices that are configured to capture identification information of products, such as UPC codes. The transaction data of the user, the activities of the user capturing the identification information of products, the location of the user, and the user's reactions to the offers are used to incrementally adjust the offers according to offer rules specified by the merchants. The mobile devices can be used to initiate a checkout process for purchasing items identified by the captured identification information of the products from the physical retail store at which the user is currently located, or via an online store associated with an offer presented via the mobile device.