论文标题
粗糙的个性化
Coarse Personalization
论文作者
论文摘要
随着估计异构治疗效果的进步，企业可以在颗粒水平上个性化和针对个人。但是，可行性约束限制了完全个性化。在实践中，公司选择个人细分市场，并为每个细分市场分配一种治疗，以最大程度地提高利润：我们将其称为粗糙的个性化问题。我们提出了一个两步解决方案，该解决方案同时做出分割和定位决策。首先，该公司通过估计有条件的平均治疗效果来个性化。其次，该公司可以使用治疗效果离散地选择提供哪些治疗及其细分。我们表明，可用的机器学习工具的组合，用于估计异质治疗效果和最佳运输方法的新颖应用提供了可行有效的解决方案。通过来自促销管理中的大规模现场实验的数据，我们发现我们的方法的表现优于现存的方法，这些方法细分了消费者特征，消费者偏好或仅在预先指定的网格上进行搜索的方法。使用我们的程序，该公司在完全个性化下的预期增量利润中收回了超过$ 99.5 \％的$ $，同时仅提供五个细分市场。我们通过讨论其他领域中的粗略个性化的结论。
With advances in estimating heterogeneous treatment effects, firms can personalize and target individuals at a granular level. However, feasibility constraints limit full personalization. In practice, firms choose segments of individuals and assign a treatment to each segment to maximize profits: We call this the coarse personalization problem. We propose a two-step solution that simultaneously makes segmentation and targeting decisions. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes using treatment effects to choose which treatments to offer and their segments. We show that a combination of available machine learning tools for estimating heterogeneous treatment effects and a novel application of optimal transport methods provides a viable and efficient solution. With data from a large-scale field experiment in promotions management, we find our methodology outperforms extant approaches that segment on consumer characteristics, consumer preferences, or those that only search over a prespecified grid. Using our procedure, the firm recoups over $99.5\%$ of its expected incremental profits under full personalization while offering only five segments. We conclude by discussing how coarse personalization arises in other domains.
