Uplift Modeling via Multi-Model Lift Selection
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Solution Overview
Problem
Traditional machine learning models for uplift prediction are inaccurate, particularly when trained with small sample sizes, as they fail to accurately differentiate between individuals who would purchase a product with or without direct marketing, leading to wasteful resource allocation.
Innovation Solution
Training multiple machine learning models of different types on a training dataset, determining their lift using a validation dataset, and selecting the model with the highest lift to predict the likelihood of action, which includes predicting the difference in purchase likelihood with and without treatment, and applying this model to make targeted predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for uplift prediction, then the model can be applied to determine purchase likelihood, but the predictions are inaccurate and fail to differentiate between individuals who would purchase with or without direct marketing
Solution Approach 1:
The patent segments the prediction task into two separate models: one predicting purchase likelihood with treatment (P(buy|treatment)) and another predicting purchase likelihood without treatment (P(buy|no treatment)). By dividing the problem into these two independent models and then subtracting their predictions, the system achieves more accurate uplift estimation than a single traditional model could provide.
Solution Approach 2:
The patent changes the parameter being predicted from simple purchase likelihood to the difference in purchase likelihood (uplift). By transforming the prediction target from P(buy) to P(buy|treatment) - P(buy|no treatment), the model directly outputs the incremental value of treatment, improving both accuracy and reliability for decision-making.
2Productivity
If traditional machine learning models are used, then predictions can be generated, but resource allocation becomes wasteful by targeting both individuals who would purchase anyway and those who need direct marketing
Solution Approach 1:
The patent extracts the incremental value of treatment by isolating and calculating P(buy|treatment) - P(buy|no treatment). This extraction process identifies only those individuals whose purchase behavior is genuinely influenced by treatment, separating them from those who would purchase regardless. This enables precise targeting and eliminates waste on individuals who would have purchased anyway.
3Adaptability or versatility
If two independent models are built for treatment and control groups, then uplift can be calculated by subtracting predictions, but the approach is fragile when trained with small sample sizes
Solution Approach 1:
The patent makes the machine learning model universal by training it to predict purchase likelihood under different treatment conditions rather than building separate models for treatment and control groups. The single model learns from both groups during training and can predict outcomes for any individual under any treatment scenario, providing both flexibility and stability while avoiding the fragility of separate small-sample models.
Data Source
AI summary
A method includes training a plurality of different types of machine learning models using a training dataset to produce a set of trained machine learning models and determining a lift of each trained machine learning model in the set of trained machine learning models using a validation dataset. The method also includes selecting a trained machine learning model from the set of trained machine learning models that has a highest lift of the set of trained machine learning models and predicting a likelihood that a person will perform an action by applying the selected trained machine learning model to data about the person.