Transaction Handler Personalizing Traveler Offers via Data Correlation
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Solution Overview
Problem
Current systems for providing offers to travelers based on transaction data lack efficiency in personalization and targeting, as they do not effectively integrate transaction data with non-transactional data and contextual information to predict user behavior and preferences.
Innovation Solution
A system that processes transaction data from credit, debit, and prepaid cards to generate personalized offers by correlating transaction data with non-transactional events and contextual information, using a transaction handler to provide targeted advertisements and loyalty programs, and utilizing a centralized data warehouse for analysis and prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems use only transaction data for providing offers, then data processing is simple, but personalization accuracy and targeting effectiveness are insufficient
Solution Approach 1:
The patent combines multiple data sources including transaction data, non-transactional events, and contextual information into a unified offer generation system. This integration allows the system to leverage diverse data types for more accurate personalization while managing complexity through a coordinated architecture of multiple components working together.
Solution Approach 2:
The offer generation system is designed to handle multiple types of data inputs and generate various types of offers across different channels. The system can process transactional and non-transactional data, adapt to different contextual scenarios, and deliver personalized offers through multiple delivery mechanisms, making it a multi-functional platform.
2Productivity
If the system integrates multiple data sources for offer generation, then personalization effectiveness improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing and analysis of data from multiple sources before actual offer generation. By pre-processing transaction data, non-transactional events, and contextual information, the system prepares structured inputs that can be quickly combined and evaluated when offer generation is triggered, reducing real-time processing delays.
Solution Approach 2:
The patent introduces intermediary components that facilitate efficient data integration and processing. These intermediaries act as buffers and coordinators between different data sources and the offer generation engine, enabling parallel processing and reducing bottlenecks in the data flow, thereby improving overall processing efficiency.
3Reliability
If the system uses comprehensive data analysis for predicting user behavior, then offer relevance improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the complex prediction task into multiple manageable components. The system divides data analysis into separate modules that process transaction data, non-transactional events, and contextual information independently, then combines their results. This segmentation reduces computational complexity by allowing parallel processing and specialized optimization of each segment.
Solution Approach 2:
The system applies different levels of analysis and processing to different data types based on their specific characteristics and contribution to prediction accuracy. Rather than uniformly processing all data with the same computational intensity, the system optimizes resource allocation by applying appropriate analytical depth to each data source, improving efficiency while maintaining prediction reliability.
Data Source
AI summary
A computing apparatus includes: a transaction handler configured to process transactions; a data warehouse configured to store transaction data recording the transactions and to store event data for travel related events; a pattern detector configured to identify correlation data relating transaction patterns in the transaction data and the events identified in the event data; a portal configured to, in response to an occurrence of a first event, provide merchants with a report of a predicted spending pattern, including the identification of a set of consumers, identified based on the correlation data and data identifying the first event; a score generator to compute a value score for a traveler based on transaction data of the traveler; and a data services platform configured to provide the value score to a hotelier in response to a transaction processed by the transaction handler to check in the traveler at the hotelier.