Uplift modeling has been used effectively in fields such as marketing and customer retention, to target those customers who are more likely to respond due to the campaign or treatment. Essentially, it is a machine learning technique that predicts the gain from performing some action with respect to not taking it. A popular class of uplift models is the transformation approach that redefines the target variable with the original treatment indicator. These transformation approaches only need to train and predict the difference in outcomes directly. The main drawback of these approaches is that in general it does not use the information in the treatment indicator beyond the construction of the transformed outcome and usually is not efficient. In this paper, we design a novel transformed outcome for the case of the binary target variable and unlock the full value of the samples with zero outcome. From a practical perspective, our new approach is flexible and easy to use. Experimental results on synthetic and real-world datasets obviously show that our new approach outperforms the traditional one. At present, our new approach has already been applied to precision marketing in a China nation-wide financial holdings group.

翻译：提升建模已有效应用于市场营销和客户留存等领域，旨在精准定位更可能因特定活动或干预措施而响应的客户群体。本质上，这是一种通过机器学习技术预测执行某项行动相较于不执行所带来的增益。其中广受关注的模型类别是转化方法，该方法通过原始处理指示变量重新定义目标变量。这类转化方法仅需直接训练和预测结果差异，但其主要缺陷在于通常仅在构建转化结果时使用处理指示变量信息，导致建模效率低下。本文针对二值目标变量场景设计了一种新型转化结果，充分挖掘了零结果样本的全部价值。从实践角度看，本文提出的新方法具有灵活易用的特点。在合成数据集和真实数据集上的实验结果均表明，新方法显著优于传统方案。目前，该创新方法已成功应用于某全国性金融控股集团的精准营销实践。