Promotion Selection System Using Consumer Profiles
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Solution Overview
Problem
Existing promotion systems fail to accurately select promotions that interest consumers, often presenting irrelevant offers before relevant ones.
Innovation Solution
A system and method that utilize consumer profiles, including deal types and favorite locations, to intelligently select and arrange promotions in electronic correspondence, matching consumer interests by associating promotions with consumer-focused deal types and geographic proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If promotions are selected using basic criteria without consumer profile analysis, then the system complexity is low, but the relevance of promotions to consumer interests deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing consumer profile information (preferences, demographics, past behavior) before the promotion selection process. This advance preparation enables more accurate matching without adding complexity to the actual selection moment, as the consumer profile is already established and ready for comparison with promotion criteria.
Solution Approach 2:
The consumer profile acts as an intermediary between the consumer and the promotion selection system. Instead of directly analyzing consumer behavior at the moment of promotion delivery, the system uses the pre-established consumer profile as a mediator to match promotions with consumer interests, thereby improving selection accuracy without proportionally increasing system complexity.
2Manufacturing precision
If multiple consumer profile features are analyzed to select promotions, then the relevance of promotions to consumer interests is improved, but the computational complexity and processing time worsen
Solution Approach 1:
The consumer profile is segmented into distinct features (demographics, preferences, past behavior, location) that can be independently analyzed and weighted. This segmentation allows the system to process multiple profile aspects separately and combine them systematically, improving selection accuracy while managing computational complexity through modular processing of each profile segment.
Solution Approach 2:
The system changes parameters by assigning different weights to various consumer profile features based on their relevance to specific promotion types. This parameter adjustment enables the system to optimize the analysis process by focusing computational resources on the most influential profile features for each promotion, thereby improving accuracy without proportionally increasing overall processing complexity.
3Adaptability or versatility
If consumer profile data is collected and analyzed, then the personalization of promotions is improved, but the data processing requirements and system resources worsen
Solution Approach 1:
The system performs preliminary action by collecting and organizing consumer profile data in advance, structuring it in a format optimized for rapid matching with promotion criteria. This advance preparation reduces the computational resources required during actual promotion delivery, as the data is already processed and ready for efficient comparison rather than requiring intensive real-time analysis.
Data Source
AI summary
In a promotion offering system, a consumer is more likely to purchase a promotion offering if the consumer finds the promotion to be interesting or fits a need of the consumer. In order to provide a more intelligent selection process for selecting promotions that are desirable to the consumer, a method and a promotion offering system for implementing the method are provided that takes into consideration a number of different factors associated with a consumer, including locations and deal types that are known or predicted to be of interest to the consumer, when determining one or more promotions to present to the consumer.