Promotion Optimization System Using Segmented Subpopulations
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Solution Overview
Problem
Current promotion optimization methods rely on backward-looking, aggregate historical data, which fails to account for unanticipated events and individual consumer behavior, leading to inefficient promotion strategies and potential long-term negative impacts on brand equity.
Innovation Solution
Implementing a forward-looking promotion optimization approach that involves administering test promotions to purposefully segmented subpopulations, tracking actual revealed preferences, and iteratively refining promotion strategies based on real-time consumer responses to optimize promotions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If backward-looking aggregate historical data is used for promotion optimization, then data collection is simple, but the promotion strategy effectiveness is insufficient
Solution Approach 1:
The patent segments the general consumer population into distinct subpopulations based on demographic, geographic, and behavioral characteristics. This segmentation enables targeted test promotions to be administered to specific subpopulations, allowing for more accurate measurement of promotion effectiveness while maintaining manageable data collection processes.
Solution Approach 2:
The patent implements preliminary action by conducting test promotions on segmented subpopulations before launching full-scale promotions. This advance testing allows optimization of promotion strategies based on real consumer responses, improving the reliability of promotion effectiveness while maintaining systematic data collection.
2Reliability
If test promotions are administered to purposefully segmented subpopulations, then promotion strategy effectiveness is improved, but system complexity increases
Solution Approach 1:
The system automatically segments consumers into subpopulations using predefined demographic, geographic, and behavioral criteria. This automated segmentation process manages complexity by using clear, objective criteria rather than requiring manual classification, thereby maintaining system effectiveness while controlling complexity.
Solution Approach 2:
The system implements feedback loops where consumer responses to test promotions are automatically collected, analyzed, and used to refine segmentation criteria and optimize future promotion strategies. This automated feedback mechanism manages system complexity by using standardized data collection and analysis procedures.
3Productivity
If iterative refinement of promotion strategies is implemented, then promotion return is optimized, but time and resources are consumed
Solution Approach 1:
The patent applies partial action by conducting test promotions on representative subpopulations rather than the entire population. This allows iterative refinement of promotion strategies with reduced time and resource investment, while still achieving optimal promotion returns through statistically valid results from segmented testing.
Solution Approach 2:
By conducting preliminary test promotions on segmented subpopulations before full-scale implementation, the system identifies optimal promotion strategies in advance. This preliminary action reduces the time and resources needed for full-scale promotions by eliminating the need for extensive post-implementation adjustments.
Data Source
AI summary
Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables and automatically incorporating constraints on segmented subpopulations. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.