Dynamic Group Population Adjustment for Targeted Promotions
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Solution Overview
Problem
Existing group studies often rely on static initial definitions that may become overly restrictive or broad as outcomes are discovered during the study, leading to inefficiencies and potential failures, as they do not allow for dynamic refinement based on observed characteristics and attributes.
Innovation Solution
A method and system for dynamically adjusting the population of a group by monitoring transactions, modeling redeeming customer data in multidimensional space, and incorporating similar customers into the group for targeted promotions, allowing for controlled expansion based on favorable outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the initial group population is defined with strict selection criteria, then the group becomes more targeted and relevant to the study objectives, but the group becomes too restrictive and may exclude customers with discovered relevant characteristics
Solution Approach 1:
The patent implements dynamic group population adjustment by continuously monitoring customer transactions during the study and automatically adding qualifying customers to the group. The group definition evolves from a static initial set to a dynamic set that adapts as new information becomes available, resolving the contradiction between initial precision and ongoing flexibility.
Solution Approach 2:
The system uses transaction monitoring and outcome analysis as feedback mechanisms to identify customers who exhibit characteristics correlated with desired study outcomes. This feedback loop enables the group population to be adjusted based on actual observed behavior rather than relying solely on initial assumptions, maintaining both precision and adaptability.
2Quantity of substance
If the initial group population is defined broadly, then more potential candidates are included, but the group becomes less targeted and includes customers not relevant to the study objectives
Solution Approach 1:
The patent segments the customer population into multiple categories: the initial group defined by prerequisites, customers monitored during the study, and customers identified for addition based on discovered characteristics. This segmentation allows the system to maintain a core targeted group while exploring broader populations, balancing quantity and precision through structured subdivision.
3Loss of time
If the group definition is based on initial assumptions, then the group formation is quick and simple, but the group may not capture characteristics discovered during the study that have higher correlation to desired outcomes
Solution Approach 1:
The patent performs preliminary actions by establishing the initial group based on available prerequisites and beginning transaction monitoring immediately. Rather than waiting for complete information before forming the group, the system acts on available data and continuously refines the population, reducing formation time while maintaining reliability through ongoing adjustment.
Data Source
AI summary
An initial small group of customers are randomly selected for participation in a campaign associated with a promotion. Transactions of the group are monitored for a redemption of the promotion. A redeeming member's transaction is modeled and mapped into a multidimensional space and transactions for customers not included in the group are modeled and mapped into the space. A neighborhood of the redeeming member and select ones of the customers not included in the group are detected as emerging from the space. Most similar customers to the redeeming member are determined from the neighborhood and a preconfigured number of the most similar customers are selected for inclusion within the group for the campaign. This process continues until a goal of the campaign is reached or the campaign is ended.