Parallel Experiment Promotion Optimization via Segmentation
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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
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, enabling the creation of cost-effective, high-return promotions.
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 distinct subpopulations based on consumer characteristics, behaviors, and responses. This segmentation allows the system to analyze individual consumer behavior patterns while maintaining manageable data structures, thereby preserving detailed information without overwhelming complexity in data collection and processing.
2Measurement precision
If test promotions are administered to purposefully segmented subpopulations, then promotion optimization precision is improved, but system complexity increases
Solution Approach 1:
The system divides the consumer population into segmented subpopulations based on relevant characteristics, allowing precise measurement of promotion effects within each segment while maintaining overall system manageability through modular analysis approaches.
Solution Approach 2:
The system performs preliminary segmentation and characterization of subpopulations before conducting promotion tests. This preliminary action prepares the data structure and analytical framework in advance, reducing the complexity burden during the actual experimentation and analysis phases.
3Productivity
If iterative testing of promotion variables is conducted, then promotion effectiveness is improved, but time and resource consumption increases
Solution Approach 1:
The system conducts preliminary analysis of consumer characteristics and segmentations before iterative testing begins. This preliminary work establishes a robust framework that guides subsequent iterations, reducing the time and resources needed for each testing cycle while maintaining high promotion effectiveness.
Solution Approach 2:
The system implements feedback mechanisms where results from each iteration of promotion testing inform and refine subsequent testing designs. This feedback loop enables continuous improvement of promotion effectiveness while optimizing resource allocation by focusing future tests on the most promising segments and variables.
Data Source
AI summary
Methods and apparatus for conducting test promotions in a highly scalable and cost-effective manner using parallel experiment methodology are disclosed. Test promotions are presented to visitors of a website in a parallel experiment manner wherein each page presents one test promotion to isolate the test promotions from one another. The visitors' responses with respect to the test promotion are then recorded and analyzed to determine the performance of the test promotion.