Coupon Issuing System Using Genetic Algorithm ROI Prediction
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Solution Overview
Problem
Existing coupon issuing strategies often result in sub-optimal returns on investment (ROI) due to unsuitable distribution methods, lacking customization for target user groups, and ineffective use of data for determining optimal strategies.
Innovation Solution
A method and system that determine a predicted value of a group indicator for each target user, group them using various strategies, and predict ROI for candidate coupon issuing strategies using a genetic algorithm, allowing for customized coupon values and optimal strategy selection based on user features and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined coupon issuing strategies are selected based on prior experience, then the implementation process is simple, but the ROI may be sub-optimal
Solution Approach 1:
The system changes the parameters of coupon issuing strategies by using machine learning models to predict user group indicators and ROIs, transforming static predetermined strategies into dynamic, data-driven strategies that adapt to user characteristics and historical consumption patterns
Solution Approach 2:
The system implements feedback mechanisms by using historical consumption data to train prediction models, which then inform coupon issuing strategies. The predicted ROIs provide feedback on strategy effectiveness, enabling continuous optimization of coupon distribution
2Productivity
If coupons are distributed uniformly to all users, then the distribution process is efficient, but the economic loss increases due to unsuitable strategies
Solution Approach 1:
The system segments users into different groups based on predicted group indicator values, allowing differentiated coupon strategies for different user segments. This segmentation enables efficient resource allocation by matching coupon values to user characteristics and purchase likelihood
Solution Approach 2:
The system applies local quality by assigning different coupon values to different user groups based on their specific characteristics and predicted behaviors, rather than applying a uniform coupon strategy to all users
3Reliability
If multiple grouping strategies and candidate strategies are evaluated using machine learning models, then the ROI optimization improves, but the system complexity increases
Solution Approach 1:
The system implements self-service by using automated machine learning models to evaluate multiple grouping strategies and candidate coupon strategies, eliminating the need for manual analysis and selection of optimal strategies
Solution Approach 2:
The system replaces manual mechanical processes of strategy selection with automated computational models including gradient boosting decision trees and genetic algorithms, which efficiently evaluate and optimize coupon issuing strategies
Data Source
AI summary
A method for issuing coupons to a plurality of target users is provided. For each of the plurality of target users, the method may include determining a predicted value of a group indicator of the target user in a predetermined period. The method may further include grouping the plurality of target users using a plurality of grouping strategies. For each of the grouping strategies, the method may further include determining a candidate coupon issuing strategy. For each of the candidate coupon issuing strategies, the method may further include obtaining user feature information of one or more target users in each group corresponding to the candidate coupon issuing strategy. For each of the candidate coupon issuing strategies, the method may further include predicting an ROI of the candidate coupon issuing strategy based on an RO prediction model and the corresponding user feature information.