Advances in estimating heterogeneous treatment effects enable firms to personalize marketing mix elements and target individuals at an unmatched level of granularity, but feasibility constraints limit such personalization. In practice, firms choose which unique treatments to offer and which individuals to offer these treatments with the goal of maximizing profits: we call this the coarse personalization problem. We propose a two-step solution that makes segmentation and targeting decisions in concert. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes by utilizing treatment effects to choose which unique treatments to offer and who to assign to these treatments. 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 for promotions management, we find that our methodology outperforms extant approaches that segment on consumer characteristics or preferences and those that only search over a prespecified grid. Using our procedure, the firm recoups over 99.5% of its expected incremental profits under fully granular personalization while offering only five unique treatments. We conclude by discussing how coarse personalization arises in other domains.

翻译：异质性处理效应估计的进步使企业能够个性化营销组合要素，并以前所未有的粒度水平定位个体，但可行性约束限制了这种个性化。在实践中，企业选择提供哪些独特处理方案以及向哪些个体提供这些处理方案，其目标为利润最大化：我们将其称为粗粒度个性化问题。我们提出了一种整合细分与定向决策的两步解决方案。首先，企业通过估计条件平均处理效应进行个性化；其次，企业利用处理效应选择提供哪些独特处理方案并确定这些处理的分配对象，从而进行离散化处理。我们证明，结合现有的异质性处理效应估计机器学习工具与最优传输方法的新应用，可提供可行且高效的解决方案。基于促销管理领域大规模现场实验数据，我们发现，我们的方法优于现有基于消费者特征或偏好进行细分的方法，以及仅对预设网格进行搜索的方法。采用我们的流程，企业在仅提供五种独特处理方案的情况下，可恢复完全粒度个性化下预期增量利润的99.5%以上。最后，我们讨论了粗粒度个性化在其他领域的应用场景。