Randomized Promotion Experimentation System for Consumer Behavior Analysis
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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
Implementing a forward-looking approach that involves administering test promotions to purposefully segmented subpopulations to gather actual revealed preferences, allowing for iterative testing and validation of promotion variables, thereby optimizing promotions for cost-effectiveness and return on investment.
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 ability to account for unanticipated events and individual consumer behavior deteriorates
Solution Approach 1:
The patent segments the population into multiple subpopulations based on shared characteristics, allowing test promotions to be administered to specific segments rather than the entire population. This segmentation enables precise measurement of consumer behavior within each segment while maintaining manageable data collection processes. The system creates distinct groups (subpopulations) that can be independently analyzed for their responses to promotional variables.
Solution Approach 2:
The patent transitions from traditional aggregate historical analysis to a multi-dimensional approach by introducing subpopulation segmentation as an additional dimension. Instead of analyzing promotion effectiveness across a single aggregate population, the system adds the dimension of population segments, enabling simultaneous analysis of multiple consumer groups with different characteristics and responses to promotional variables.
2Measurement precision
If test promotions are administered to purposefully segmented subpopulations, then consumer behavior insights are improved, but system complexity increases
Solution Approach 1:
The patent creates a universal experimentation system that can handle multiple subpopulations, promotion variables, and analysis types through a single integrated platform. The system is designed to be multi-functional, capable of administering test promotions across different subpopulations, tracking various consumer responses, and analyzing results for multiple promotional variables simultaneously, thereby managing complexity through consolidation rather than proliferation of separate systems.
Solution Approach 2:
The patent systematically varies promotion variables (such as discount levels, promotion types, timing) across different subpopulations to observe consumer responses. By changing these parameters in a controlled manner and measuring the effects, the system transforms complex consumer behavior analysis into manageable experimental data that can be analyzed to identify effective promotional strategies for different segments.
3Productivity
If iterative testing of promotion variables is conducted, then promotion optimization effectiveness is improved, but time and resource consumption increases
Solution Approach 1:
The patent employs preliminary action by administering test promotions to segmented subpopulations before launching full-scale promotional campaigns. This preliminary testing phase allows the system to identify effective promotion variables and optimize strategies in advance, reducing the risk of failed full-scale campaigns and minimizing time loss associated with ineffective promotions. The iterative testing occurs in a controlled, preliminary manner rather than waiting for full campaign results.
Data Source
AI summary
Methods and apparatus for conducting test promotions in a highly scalable and cost-effective manner using randomized experiment methodology are disclosed. Test promotions of interest are presented to visitors of a website in a randomized experiment manner wherein each page presents one test promotion of interest among other promotions. The other promotions presented in the same page may be randomized. The visitors' responses with respect to the test promotions of interest are then recorded and analyzed to determine the performance of each presented test promotion of interest.