Region Division for Transaction Code Placement Prediction
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Solution Overview
Problem
Marketing teams face challenges in identifying regions with higher conversion rates for offline transaction codes, as existing methods lack efficiency and objectivity in selecting suitable regions for implementing promotional campaigns.
Innovation Solution
A big data-based method is employed to recommend target transaction code setting regions by dividing areas into sub-regions, analyzing association features, and using prediction algorithms to determine optimal settings, incorporating GeoHash algorithms and graph propagation algorithms for data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to select regions for transaction code settings, then the process is simple to operate, but the selection accuracy and campaign effectiveness are low
Solution Approach 1:
The patent divides the target region into multiple sub-regions and further segments them into to-be-tested sub-regions and label sub-regions. This segmentation allows the system to analyze different regions with different strategies, improving selection accuracy by treating each sub-region as an independent unit for prediction and testing.
Solution Approach 2:
The patent performs preliminary actions by first dividing regions, then obtaining association features, and conducting predictions before actually selecting regions for transaction code settings. This preliminary analysis phase enables data-driven decision-making that improves accuracy while managing complexity through structured preprocessing.
2Measurement precision
If more regions are tested with transaction codes to identify high-conversion areas, then the accuracy of region selection improves, but the time and resources required for testing increase
Solution Approach 1:
The patent applies partial action by selecting only certain to-be-tested sub-regions for actual transaction code settings based on predicted effect values. Instead of testing all regions, the system predicts outcomes and selectively tests only those regions with high predicted conversion rates, reducing testing time while maintaining accuracy.
Solution Approach 2:
The patent implements feedback mechanisms by obtaining actual effect values from tested regions and using them to update the prediction algorithm and association features. This feedback loop continuously improves prediction accuracy, allowing the system to make better region selections with less testing over time.
3Measurement precision
If the prediction algorithm is continuously updated with new data, then the prediction accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent updates the prediction algorithm and association features only when necessary, such as when new data becomes available or when prediction accuracy needs improvement. This selective updating approach maintains accuracy while avoiding unnecessary computational overhead that would reduce processing efficiency.
Data Source
AI summary
Implementations of the present specification disclose a method and a system for recommending a target transaction code setting region. The method includes the following: dividing a target region to obtain multiple sub-regions, where the multiple sub-regions include one or more label sub-regions with known target transaction code setting effects and one or more sample sub-regions with unknown target transaction code setting effects; obtaining an association feature between the multiple sub-regions; obtaining predicted effect values of setting a target transaction code in the one or more sample sub-regions by using a prediction algorithm based on at least estimated effect values of setting a target transaction code in the one or more label sub-regions and the association feature; and determining at least one recommended region for setting a target transaction code from the one or more sample sub-regions based on at least the one or more predicted effect values.