Consumer Association Database for Targeted Promotion Delivery
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Solution Overview
Problem
Current promotional systems lack efficiency in recommending targeted promotions to consumers, as they do not effectively leverage user data and consumer interactions to provide relevant offers, leading to inefficient marketing efforts and resource allocation.
Innovation Solution
A computer-executable method that retrieves user input and profile data from consumers to programmatically generate associations between users, determining promotion sharing associations based on profile features and prior activities, and automatically offering promotions to users with high association scores, utilizing a consumer association database and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If promotions are offered to all consumers through traditional promotional systems, then broad market coverage is achieved, but marketing efficiency and resource allocation deteriorate due to lack of targeting
Solution Approach 1:
The patent segments the consumer base into distinct groups based on profile features (demographics, preferences, behavior patterns) and creates consumer associations that link consumers with similar characteristics. This segmentation enables targeted promotion delivery to specific segments rather than blanket distribution to all consumers, thereby improving marketing efficiency while reducing wasted resource allocation on uninterested audiences.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing consumer profile data and pre-establishing consumer associations before promotions are launched. Consumer profiles are processed in advance to identify associations and predict interest levels, allowing the system to pre-determine which consumers are most likely to respond to specific promotions. This preliminary processing eliminates the need for real-time analysis during promotion delivery, improving efficiency and reducing resource consumption.
2Measurement precision
If consumer data is extensively analyzed to improve promotion targeting, then promotion relevance is improved, but system complexity increases due to data processing requirements
Solution Approach 1:
The patent segments consumer data into distinct profile features (demographics, preferences, behavior patterns) and organizes them into structured consumer associations. This segmentation transforms raw, complex data into manageable, categorized information that can be efficiently processed and queried, maintaining high measurement precision for promotion relevance while reducing the apparent system complexity through organized data structures.
Solution Approach 2:
The system creates simplified representations of consumer data through consumer associations and profile feature extractions. Instead of processing entire consumer profiles for each promotion evaluation, the system uses pre-extracted key features and association metrics as proxies. This copying approach maintains the essential information needed for accurate targeting while significantly reducing computational complexity during promotion delivery.
3Measurement precision
If automated consumer association generation is implemented, then promotion sharing accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-computing consumer associations and storing them in a consumer association database before promotion campaigns begin. Consumer profiles are analyzed in advance to identify associations based on shared characteristics, and these associations are cached for rapid retrieval during promotion delivery. This preliminary computation shifts the computational burden to an offline phase, maintaining high promotion sharing accuracy while reducing real-time computational requirements.
Solution Approach 2:
The system applies partial action by computing and storing only the most relevant consumer association features needed for promotion matching, rather than calculating all possible associations. The consumer association database stores pre-computed metrics and key association attributes that are sufficient for accurate promotion targeting without requiring complete exhaustive analysis. This approach achieves high accuracy while managing computational requirements through selective computation of essential association data.
Data Source
AI summary
Embodiments provide computer systems, computer-executable methods and one or more non-transitory computer-readable media for offering one or more promotions to consumers using a promotion and marketing service. User input may be received from a first consumer interface associated with a first consumer, the user input including an interest indication relating to a first promotion. An association between the first consumer and a second consumer for sharing of promotions may be programmatically retrieved or generated. Based on the association, it may be determined whether to offer the first promotion to the second consumer based on one or more characteristics associated with the second consumer. Based on a determination that the first promotion should be offered to the second consumer, an indication may be outputted, the indication configured to cause an impression of the first promotion to be generated on a second consumer interface associated with the second consumer.