Personalization and targeting: how to experiment, learn & optimize
本文从因果推断视角将个性化营销形式化为测试与学习框架，综述数据限制、处理效应异质性、政策评估及伦理挑战，并展望通用机器学习、直接策略学习等新方法带来的研究趋势。
Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments. We formalize personalization as a causal inference problem embedded in the test and learn framework. We review key challenges and solutions that arise when personalization is approached through causal inference, including data limitations, treatment effect heterogeneity, policy evaluation, and ethical considerations. Finally, we identify emerging research trends stemming from new methodologies such as generic and double machine learning, direct policy learning, foundation models, and generative AI.