Loyalty Index Interface With Automatic Entity Ranking
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Solution Overview
Problem
Traditional user interfaces require users to manually search for and arrange information by metrics they may not understand, leading to increased mental load, computational processing, and memory/bandwidth consumption, especially when viewing loyalty indices.
Innovation Solution
A system that automatically calculates and presents a loyalty index on a user interface, sorting entities by exclusivity and stability loyalty factors, reducing computational load and improving human-machine interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually search for and arrange information by metrics, then users can access information, but mental load and computational processing increase
Solution Approach 1:
The system pre-calculates and pre-arranges information by relevant metrics (such as loyalty indices, transaction frequencies, or user preferences) before the user requests it. This preliminary organization eliminates the need for users to manually search and sort data, reducing both mental load and real-time computational processing while maintaining easy information access
2Loss of information
If users manually search for and arrange information, then users can find specific data, but time consumption increases
Solution Approach 1:
The system performs preliminary sorting and filtering of information by multiple relevant metrics simultaneously, creating pre-organized views that allow users to find specific data instantly without manual searching or sequential filtering operations that consume time
Solution Approach 2:
The system presents information in multi-dimensional views (such as combining loyalty index, transaction frequency, and category simultaneously) rather than requiring users to search through single-dimensional lists, enabling comprehensive information retrieval across multiple criteria at once
3Loss of information
If information is presented without automatic sorting, then all data is available, but user understanding of loyalty metrics becomes difficult
Solution Approach 1:
The system applies different presentation qualities to different parts of the data based on their relevance to loyalty metrics. High-loyalty entities are highlighted or positioned prominently, while maintaining the complete dataset. This local differentiation helps users understand loyalty relationships without losing data completeness
Data Source
AI summary
Examples provide a system, method, and computer storage device for automatically presenting a loyalty index for a user. A loyalty index is calculated using transaction details for a plurality of users for a time period. The calculation involves calculating a total entity transaction amount, a total user transaction amount, and an entity proportion for each entity per user. The exclusivity loyalty factor proportion is an average value of the entity proportion for each entity in the favorite entities list weighted by the total entity transaction amount of each user with that entity. The examples present each entity and corresponding exclusivity loyalty factor proportion as an index icon in a user interface and automatically move the index icons to a list in the user interface in descending order of exclusivity loyalty factor proportion.