Promotion Scoring Model Using Pairwise Offer Regression
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Solution Overview
Problem
Existing promotion optimization methods rely on backward-looking, aggregate historical data, failing to account for unanticipated events and individual consumer behavior, leading to inefficient and potentially harmful promotion strategies.
Innovation Solution
Implement a forward-looking approach using machine learning to analyze revealed consumer preferences from purposefully segmented subpopulations, iteratively testing promotions to identify optimal variables and generate effective general promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If backward-looking aggregate historical data is used for promotion optimization, then implementation simplicity is maintained, but prediction accuracy and ability to account for unanticipated events deteriorates
Solution Approach 1:
The patent segments the consumer population into distinct subpopulations based on behavioral characteristics and divides historical data into event-specific segments. This allows the system to analyze consumer responses to unanticipated events within specific segments while maintaining overall system manageability. The segmentation enables accurate prediction by focusing on relevant subsets of data rather than treating all consumers uniformly.
Solution Approach 2:
The system performs preliminary analysis of historical data to identify unanticipated events and their impact on consumer behavior before developing promotion strategies. By pre-processing the data to extract event-specific insights and consumer response patterns, the system prepares accurate predictions in advance, improving both prediction accuracy and implementation efficiency.
2Device complexity
If aggregate historical data is used without segmentation, then data processing complexity is reduced, but ability to identify individual consumer behavior patterns deteriorates
Solution Approach 1:
The patent implements segmentation at two levels: dividing consumers into behavioral subpopulations and dividing historical data into event-specific segments. This dual segmentation preserves individual consumer behavior patterns by analyzing them within relevant segments while keeping data processing manageable through focused analysis of segmented data rather than processing all data uniformly.
Solution Approach 2:
The system applies different analysis methods and criteria to different segments of consumers and events. By tailoring the analysis to local characteristics of each segment rather than applying a uniform approach, the system preserves important behavioral information while maintaining processing efficiency through targeted analysis.
3Productivity
If traditional promotion methods are used, then immediate sales impact may be achieved, but long-term brand equity and consumer trust deteriorates
Solution Approach 1:
The patent implements a feedback mechanism that uses historical data analysis to evaluate how promotions affect different consumer segments. By monitoring consumer responses and incorporating this feedback into future promotion decisions, the system optimizes for both immediate sales impact and long-term brand equity, avoiding strategies that may hurt consumer trust while maintaining effectiveness.
Solution Approach 2:
The system dynamically adjusts promotion parameters based on segment-specific insights derived from historical event analysis. By changing promotion characteristics to match the preferences and responses of different consumer segments, the system achieves effective sales impact while maintaining brand equity through personalized, consumer-centric approaches.
Data Source
AI summary
Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.