Loyalty Management Engine Maximizing Earnings
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Solution Overview
Problem
Loyalty program monitoring apps are limited in providing information and services, hindering users' ability to make informed purchasing decisions based on their loyalty point earnings.
Innovation Solution
A computerized method that collects loyalty data, account data, and transaction data to generate personalized purchase recommendations for maximizing loyalty earnings, displayed on a user interface, enhancing user experience and efficiency in travel planning and purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If loyalty program monitoring apps provide only basic loyalty point information, then the app complexity remains low, but users cannot make informed purchasing decisions based on loyalty earnings
Solution Approach 1:
The patent combines multiple data sources (loyalty program data, account data, transaction data) and multiple functions (monitoring, analyzing, recommending) into a single integrated loyalty management system. This merging allows the system to provide comprehensive purchasing decision information while managing complexity through unified architecture.
Solution Approach 2:
The loyalty management system is designed to perform multiple functions: monitoring loyalty points, analyzing spending patterns, generating purchase recommendations, and providing decision-support information. This multi-functionality enables the system to address various user needs within a single platform, reducing information loss without proportionally increasing perceived complexity.
2Measurement precision
If the loyalty management system collects and analyzes multiple data sources (loyalty data, account data, transaction data), then the accuracy of purchase recommendations improves, but the system complexity increases
Solution Approach 1:
The system segments data processing into distinct modules: loyalty data collection, account data collection, transaction data collection, analysis engine, and recommendation generator. Each module handles specific data types and processing tasks independently, then integrates results. This segmentation improves recommendation accuracy through comprehensive analysis while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary loyalty management system that sits between multiple data sources (airlines, hotels, merchants) and the user. This intermediary collects, standardizes, and processes data from various sources, transforming raw data into actionable recommendations. The intermediary layer simplifies the overall system architecture by providing a unified interface while enabling precise recommendations through comprehensive data analysis.
3Adaptability or versatility
If the system provides comprehensive purchase recommendations across multiple categories (travel, dining, shopping), then user versatility in making informed decisions improves, but the information processing requirements increase
Solution Approach 1:
The system applies local quality by providing customized recommendations based on individual user characteristics, spending patterns, and loyalty preferences. Rather than providing generic information across all categories, the system tailors recommendations to each user's specific context, improving versatility while optimizing computational resources by focusing analysis on relevant categories and merchants.
Solution Approach 2:
The system performs preliminary actions by pre-calculating recommendation scores, pre-segmenting merchants by category and loyalty value, and pre-processing transaction data into analyzable formats. This preliminary processing reduces computational requirements during real-time recommendation generation, enabling comprehensive multi-category coverage without excessive energy consumption during user interactions.
Data Source
AI summary
The disclosure facilitates loyalty earnings by generating purchase recommendations. Loyalty data is collected from a loyalty program data store. The loyalty data is associated with a loyalty profile of a user. Account data is collected from an account data store. The account data is associated with an account of the user that is linked to the loyalty profile of the user. Transaction data is collected from a transaction data store. The transaction data is associated with the account of the user. User recommendations are generated for prioritizing loyalty earnings on the loyalty profile based on the loyalty data, account data, and transaction data. After the recommendations are generated, the recommendations are caused to be displayed on a user interface associated with a computing device of the user. Combining the three types of collected data to generate the recommendations results in accurate recommendations that are tailored to the user to increase loyalty earnings.