Selective Participant Targeting in Resource Conservation Programs
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Solution Overview
Problem
Existing resource conservation programs face challenges in efficiently recruiting and enrolling participants, as they often target all users equally, leading to high recruitment costs and variable reductions in resource usage.
Innovation Solution
A method to selectively target resource users for participation in conservation programs based on their likelihood of accepting offers and potential for reducing resource usage, using data on responsiveness, demographic, and behavioral indicators to predict expected reductions and optimize recruitment efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all resource users are targeted equally for recruitment, then recruitment coverage is maximized, but recruitment costs increase and resource usage reduction efficiency decreases
Solution Approach 1:
The patent applies local quality by differentiating recruitment strategies based on user characteristics. Instead of uniform targeting, the system identifies specific user segments with higher responsiveness and greater resource reduction potential, allocating recruitment resources selectively to these high-value segments while reducing or eliminating spending on low-potential users.
Solution Approach 2:
The system changes the parameter of user selection from random or uniform distribution to targeted selection based on measured responsiveness parameters and resource reduction potential. By using statistical models to predict user response to conservation offers, the system transforms recruitment from a blanket approach to a precision-targeted approach, improving efficiency while reducing costs.
2Productivity
If selective targeting based on responsiveness and resource reduction potential is implemented, then recruitment cost efficiency improves, but program complexity increases
Solution Approach 1:
The system employs self-service through automated statistical modeling and user segmentation. Rather than requiring manual analysis of user data by program administrators, the system automatically processes responsiveness data, calculates resource reduction potential, and identifies target users using standardized statistical models, thereby managing complexity through automation.
Solution Approach 2:
The system incorporates feedback loops where user responses to conservation offers are tracked and fed back into the statistical models. This continuous feedback refines the responsiveness measurements and improves the accuracy of future targeting, allowing the system to adapt and improve performance while maintaining manageable complexity through data-driven iteration.
Data Source
AI summary
A method and system for configuring a resource conservation program that receives information about a level of responsiveness for each of a plurality of users, receives information about resource usage for each of the plurality of users, determines an expected value, using a processor, for each of the plurality of users, using the received information about the level of responsiveness and the received information about resource usage, and configures the resource conservation program based on the determined expected value for each of the plurality of users.