Promotion Matching System Using Segmentation and Behavioral 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 misallocation of resources.
Innovation Solution
The implementation of a forward-looking promotion optimization system that administers test promotions to purposefully segmented subpopulations, analyzing actual responses to identify statistically relevant trends and match consumer profiles with optimal promotional designs, using a performance score model and behavioral economic classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backward-looking aggregate historical data is used for promotion optimization, then data collection is simple, but promotion effectiveness is reduced due to inability to account for unanticipated events and individual consumer behavior
Solution Approach 1:
The patent segments the consumer population into distinct profiles based on behavioral characteristics and divides promotion testing into controlled experiments with specific treatment groups. This segmentation enables analysis of individual consumer behavior responses while maintaining manageable data collection through structured profiling and targeted experimentation rather than attempting to analyze all consumers uniformly.
Solution Approach 2:
The patent performs preliminary actions by pre-defining consumer profiles, pre-structuring promotion test designs, and pre-establishing measurement frameworks before conducting promotion experiments. This preliminary structuring enables the system to capture relevant individual behavior data efficiently during execution, resolving the contradiction between detailed analysis requirements and data collection simplicity.
2Measurement precision
If test promotions are administered to purposefully segmented subpopulations to identify statistically relevant trends, then promotion matching accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements segmentation by dividing the consumer population into distinct profiles with specific behavioral characteristics and administering targeted promotion tests to each segment. This enables precise measurement of promotion effectiveness for each consumer type while managing system complexity through structured segmentation frameworks and standardized testing protocols for each segment.
Solution Approach 2:
The patent changes parameters by systematically varying promotion variables (discount levels, offer types, timing) across different consumer profile segments and measuring responses. This parameter variation approach enables identification of statistically relevant trends and optimal matching while maintaining manageable complexity through controlled experimental design and systematic parameter testing.
3Productivity
If aggregate historical data is used, then resource allocation is simpler, but resource misallocation occurs due to inability to account for individual consumer behavior
Solution Approach 1:
The patent segments consumers into behavioral profiles and allocates promotion resources to each segment based on measured response patterns. This segmentation enables efficient resource allocation by identifying which consumer types respond best to which promotion types, improving productivity while managing complexity through structured profiling and targeted resource distribution rather than uniform allocation.
Solution Approach 2:
The patent implements feedback mechanisms by measuring actual consumer responses to promotion tests and using this feedback to refine resource allocation decisions. The system continuously learns from test results and adjusts promotion spending and targeting accordingly, improving resource allocation efficiency while managing analysis complexity through systematic feedback loops and iterative optimization.
Data Source
AI summary
Systems and methods for automated profile to promotion matching are provided. A plurality of segment variable value pairs that define a plurality of possible segments for a population of consumers are first identified, as are a plurality of promotion variable value pairs of a promotional design space. The variable value pairs are assembled into a plurality of test promotions by different permutations. The test promotions are administered, and results are collected. Trends between specific segment variable value pairs and promotion variable value pairs that result in statistically relevant shifts in the obtained responses are identified and used to define segments. The defined segments are then matched to promotion types (collected from a promotion repository) based upon likelihood of a positive result. The matches are then classified by a behavioral economic classification, which then may be outputted to a marketer to provide insights into consumer motivations.