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:在估算各种治疗效果方面的进展方面,各公司能够将营销组合要素和针对个人的个人个人化,达到不相称的颗粒度,但可行性的限制限制了这种个性化。在实践上,公司选择提供哪些独特的治疗方法,以及哪些个人提供这些治疗方法,以达到最大利润的目标:我们称之为粗略的个人化问题。我们提出一个两步解决办法,使分解和有针对性地作出一致的决定。首先,公司个人化办法是估计有条件的平均治疗效果。第二,公司通过利用治疗效果来选择提供哪些独特的治疗方法,以及由谁分配这些治疗方法。我们表明,现有机器学习工具的组合是用来估计不同治疗效果和采用最佳运输方法的新应用,提供了一种可行和有效的解决办法。我们发现,利用大规模实地实验的数据来进行促进管理,我们的方法超越了有关消费者特点或偏好以及只对预定的网格进行搜索的延伸方法。利用我们的程序,在完全颗粒个人化下对预期的递增利润的99.5%以上进行重新组合。我们通过只提供五种独特的治疗,我们最后通过讨论个人化如何在其它领域形成共同分析个人化的方法来得出结论。</s>