Proxy Campaigns for Uplift Model Training
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Solution Overview
Problem
Retailers face challenges in identifying and targeting discount-sensitive guests for promotional campaigns, as existing incremental sales modeling techniques often require historical or pilot campaign data, which limits their ability to model new campaigns effectively and can result in overfitting and reduced accuracy.
Innovation Solution
The system generates a proxy campaign using historical guest profile and campaign data to train an uplift model, allowing for the prediction of incremental sales and selection of guests likely to respond to promotions, even when historical campaigns are unavailable or infeasible, thereby avoiding overfitting and improving campaign efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical campaign data is used to train the uplift model, then the model can be trained with real-world data, but the model may overfit to specific historical campaigns and reduce accuracy for new campaigns
Solution Approach 1:
The patent creates a synthetic proxy campaign that copies the structural characteristics of real historical campaigns while using artificial data. This proxy campaign includes synthetic treatment and control groups with realistic purchase behaviors, allowing the model to learn general promotional effects without overfitting to specific historical campaign details. The synthetic data maintains the essential relationships between promotions, guest behavior, and sales while eliminating campaign-specific biases.
Solution Approach 2:
The patent transforms real historical campaign data into synthetic data by changing the data characteristics while preserving the underlying causal relationships. The synthetic proxy campaign uses modified parameters (artificial guest profiles, synthetic purchase patterns) that maintain the fundamental promotional mechanics but eliminate overfitting risks associated with specific historical campaigns.
2Measurement precision
If a pilot campaign is run to collect data for training, then the model can be trained on treatment and control group data, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent performs preliminary action by pre-generating the synthetic proxy campaign data before the actual model training. Instead of collecting data during a time-consuming pilot campaign, the system pre-creates the treatment and control groups with synthetic data that represents realistic promotional scenarios. This allows the model to be trained immediately without waiting for actual campaign data collection.
Solution Approach 2:
The patent uses disposable synthetic data that can be generated on-demand without requiring actual pilot campaigns. The synthetic proxy campaign acts as a temporary, discardable data source that provides sufficient training data without the time and resource commitments of real-world pilot campaigns.
3Manufacturing precision
If the uplift model is trained on specific historical campaign data, then the model can capture campaign-specific patterns, but it reduces the model's ability to generalize to different promotion types
Solution Approach 1:
The patent creates a universal synthetic proxy campaign that can serve multiple training purposes. The synthetic data is designed to represent general promotional mechanisms rather than specific campaign types, allowing the same training data to support predictions across different promotion scenarios. The proxy campaign structure can be adapted for various product categories, promotion types, and guest segments while maintaining consistent training quality.
Data Source
AI summary
In general, methods and system for modeling the incremental value of a response to different types of treatment are disclosed. Some examples include modeling discount sensitivity for specific guests. One aspect is a method for modeling incremental sales for a retail enterprise which includes generating a proxy campaign. In some embodiments, the proxy campaign is used to train an uplift model to predict an uplift score for each guest in response to a proposed campaign. In some embodiments, the uplift score is used to select guests for the proposed campaign.