Promotion Offering System Consumer Targeting
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Solution Overview
Problem
Merchants face challenges in efficiently selecting a subset of consumers for promotions due to the unwieldy size of data generated from similar promotions, making it difficult to analyze and target the right audience effectively.
Innovation Solution
A promotion offering system and method that selects an initial set of consumers for a promotion based on a representative distribution across multiple consumer attributes, generates probabilities of acceptance using feedback, and adjusts the selection process to optimize targeting for both pre-feature and full-feature promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If merchants analyze data from similar promotions to select target consumers, then promotion targeting accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the consumer selection process into multiple stages: first selecting an initial set of consumers with representative distribution across attribute values, then using feedback from this initial set to generate probabilities of acceptance, and finally selecting subsequent sets based on these probabilities. This segmentation breaks down the complex task of analyzing all consumer data into manageable steps, reducing overall data processing complexity while maintaining targeting accuracy.
Solution Approach 2:
The patent performs preliminary action by first selecting an initial set of consumers with representative distribution before analyzing the full consumer base. This initial selection creates a manageable subset that provides representative feedback, which is then used to guide subsequent selections. This preliminary action reduces the complexity of analyzing all consumer data at once while still achieving accurate targeting.
2Reliability
If merchants select a representative distribution of consumers across multiple attribute values, then feedback reliability is improved, but selection time increases
Solution Approach 1:
The patent applies partial action by selecting only enough consumers in the initial set to achieve representative distribution across attribute values, rather than analyzing all consumers. This partial selection provides sufficient feedback reliability to generate meaningful probabilities of acceptance, reducing selection time while maintaining the reliability needed for accurate subsequent targeting.
3Productivity
If merchants use feedback to generate probabilities of acceptance for subsequent consumer selection, then promotion effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback by collecting responses from consumers in the initial set and using this feedback to generate probabilities of acceptance for subsequent consumer selections. This feedback loop continuously improves promotion effectiveness by refining the selection criteria based on actual consumer responses, while the systematic approach keeps system complexity manageable through structured processing steps.
Data Source
AI summary
A promotion offering system and method is disclosed. The promotion offering system and method selects consumers for a test promotion that has one or more attributes, and is configured to generate test data for multiple consumer groupings. The promotion offering system and method may use the test data in analyzing whether to send a promotion to a consumer. For example, the promotion offering system and method may use one algorithm to generate a list of ranked promotions, and may use the test data in order to adjust the list of ranked promotions (such as replacing a highest ranked promotion with another promotion).