Centralized Data Warehouse for Real-Time Reward Correlation
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Solution Overview
Problem
Current systems for tracking and rewarding user interactions with advertisements lack efficiency in correlating online activities with offline transactions and providing personalized offers in real-time, leading to suboptimal user engagement and revenue generation for advertisers.
Innovation Solution
A system that processes transaction data from various payment methods to generate user profiles, correlating online activities with offline transactions and providing personalized advertisements and offers through a centralized data warehouse, enabling real-time redemption and reward processing across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transaction data is processed through multiple separate systems for online and offline activities, then data coverage is comprehensive, but system complexity increases and real-time correlation efficiency decreases
Solution Approach 1:
The patent combines online activity tracking and offline transaction processing into a single centralized data warehouse system. This unified architecture eliminates the need for multiple separate systems to exchange data, reducing system complexity while maintaining comprehensive data coverage. The centralized system processes both online clickstream data and offline transaction data through a single correlation engine, achieving real-time correlation without the overhead of inter-system communication.
2Productivity
If user interaction tracking is performed in real-time across all channels, then user engagement improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of transaction data by pre-segmenting users into clusters based on their purchase behaviors and preferences before real-time correlation is needed. This pre-clustering approach allows the system to quickly match online activities with the appropriate user segments without performing complex analysis in real-time, significantly reducing processing time while maintaining the ability to deliver personalized offers immediately.
3Adaptability or versatility
If personalized offers are generated for each user based on detailed transaction analysis, then advertising effectiveness increases, but computational complexity and processing overhead increase
Solution Approach 1:
The patent segments the user base into distinct clusters based on purchase behaviors, preferences, and demographics. Instead of generating personalized offers for each individual user through complex real-time analysis, the system creates representative profiles for each cluster. When a user's activity is tracked, the system identifies their cluster and delivers offers tailored to that segment's characteristics. This approach maintains high personalization capability while dramatically reducing computational complexity by processing at the cluster level rather than the individual level.
4Measurement precision
If comprehensive transaction data is collected from multiple sources, then user profiling accuracy improves, but data integration complexity and storage requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from comprehensive transaction data for user profiling purposes. Rather than storing and processing all raw transaction details, the system identifies and extracts key attributes such as purchase frequency, average transaction value, preferred product categories, and temporal patterns. This extraction approach maintains high user profiling accuracy by focusing on the most predictive features while reducing data volume and simplifying storage requirements.
Data Source
AI summary
A system includes a transaction handler, a data warehouse to store transaction data recording transactions processed at the transaction handler and to store account data identifying an account of a user, and a portal to receive a user selection of a first portion of an announcement of a reward. The portal tracks user interactions associated with the announcement to determine whether the user is qualified for the reward. Upon a determination that the user has completed the user interactions for the reward, the transaction handler provides the reward to the account of the user via statement credit.