Dynamic Promotion Analytics Using Predictive Consumer Acceptance Models
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Solution Overview
Problem
Merchants face difficulty in determining which promotional offers are most likely to be accepted by consumers, as existing methods lack effective analysis of consumer feedback and performance data to target specific subsets of consumers effectively.
Innovation Solution
A system and method that utilize historical and promotion program predictive models to generate predicted probabilities of consumer acceptance, combining consumer and promotion attributes to dynamically update and optimize offer presentation during a promotion program duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchants offer promotions to generate more business, then sales volume increases, but the difficulty of determining which consumers are most likely to accept promotions increases
Solution Approach 1:
The patent segments consumers into different subsets based on their likelihood of accepting promotions by analyzing consumer feedback and performance data. This segmentation allows merchants to target specific consumer groups with tailored promotions, resolving the complexity of determining acceptance likelihood by breaking down the consumer base into manageable segments with predictable behaviors.
Solution Approach 2:
The system uses consumer feedback and performance data from previous promotions to dynamically update predictive models. This feedback mechanism allows the system to learn from past promotion outcomes and improve its ability to predict consumer acceptance, reducing the complexity of determining which consumers are most likely to respond positively.
2Reliability
If merchants focus promotions on specific consumer subsets, then promotion acceptance rate increases, but the complexity of identifying target consumers increases
Solution Approach 1:
The system performs preliminary analysis of consumer feedback and performance data before launching promotions to pre-identify target consumer subsets. By conducting this analysis in advance, the system establishes predictive models that simplify the identification process during actual promotion campaigns, maintaining high acceptance rates while reducing operational complexity.
Solution Approach 2:
The patent changes the parameters used to identify target consumers by utilizing multiple consumer attributes and performance metrics rather than single-dimensional segmentation. This multi-parameter approach improves the accuracy of identifying receptive consumers while providing a systematic framework that manages the complexity of the identification process.
3Measurement precision
If merchants use historical data to predict consumer acceptance, then targeting accuracy improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary processing and modeling of historical consumer feedback data in advance, creating ready-to-use predictive models. This preliminary action allows the system to quickly query and apply pre-computed insights during promotion campaigns, maintaining high targeting accuracy while significantly reducing the time required for real-time analysis.
Solution Approach 2:
The patent implements dynamic predictive models that can be updated incrementally as new data becomes available, rather than requiring complete re-analysis. This dynamic approach allows the system to maintain high targeting accuracy by continuously learning from new feedback while minimizing the time investment required for each promotion decision through incremental updates.
Data Source
AI summary
A promotion program analytical system and method is disclosed. The promotion program analytical system and method selects a promotion program to offer to a consumer. Selection of the promotion program to present to the consumer includes determining a probability that the consumer will accept the promotion program. The probability of acceptance may be determined based on past performance data of similar promotion programs, and also past performance data on the promotion program itself when it is available.