Forward-Looking Promotion Optimization System
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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 damage to brand equity.
Innovation Solution
The development of a forward-looking promotion optimization system that uses intelligent design criteria to generate and test promotions across segmented consumer populations, analyzing actual revealed preferences to identify effective promotion variables and strategies.
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 measurement precision and reliability of promotion insights deteriorate
Solution Approach 1:
The patent segments consumer data from aggregate level to individual level, creating distinct consumer profiles with unique characteristics, preferences, and behaviors. This segmentation enables precise measurement of individual consumer responses to promotions while maintaining systematic data collection processes.
Solution Approach 2:
The patent implements feedback loops where individual consumer responses to promotions are collected, analyzed, and used to refine promotion strategies. This continuous feedback mechanism improves measurement precision by incorporating actual consumer behavior data rather than relying solely on historical aggregates.
2Device complexity
If backward-looking aggregate historical data is used, then system complexity is reduced, but the ability to account for unanticipated events and individual behavior deteriorates
Solution Approach 1:
The patent transforms the static, backward-looking system into a dynamic, forward-looking system that continuously adapts to new information. Individual consumer data is collected and analyzed in real-time, allowing the system to adapt to unanticipated events and changing consumer preferences while maintaining manageable complexity through automated processing.
Solution Approach 2:
The patent performs preliminary actions by segmenting consumers and establishing prediction models before promotions are executed. This advance preparation enables the system to account for various scenarios and unanticipated events by having pre-established consumer profiles and response patterns ready for analysis.
3Loss of time
If aggregate historical data analysis is used, then processing time is reduced, but productivity in identifying effective promotions deteriorates
Solution Approach 1:
The patent performs preliminary segmentation and profiling of consumers before promotion execution. By pre-organizing consumer data into segmented profiles with known characteristics and preferences, the system reduces processing time during actual promotion analysis while improving productivity through more targeted and effective promotion identification.
Data Source
AI summary
Systems and methods for the selection of the promotions are provided. A set of possible offers are initially received, each including a set of variables, with each variable having a set of possible values. These form a combination of variable values for each offer. A heuristic is applied to all possible offers to reduce the number of offers being considered. The combination of variable values for these reduced number offers is converted into a vector value, which is then scored, ranked and the top ranked offers are selected for inclusion in a promotional campaign. The remaining offers are then analyzed to select additional offers to include into the promotional campaign which maximizing a determinant for the selected offers using their vectors. All selected offers are administer in a promotional test campaign across many consumer segments. Feedback from the campaign may be collected to generate a “general” offer.