The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

客户关系管理 (CRM) 计划的成功最终取决于公司识别和利用客户差异的能力——当公司试图管理新客户时，这是一项非常困难的任务，因为他们只观察到了第一次购买。对于这些客户，缺乏重复观察对推断他们之间未观察到的差异构成了结构性挑战。这就是我们所说的 CRM 的“冷启动”问题，即公司在建立关系之初就试图对客户进行推断时，难以利用现有数据。我们通过开发一个利用采集时收集的信息的概率机器学习建模框架来提出冷启动问题的解决方案。该模型的主要方面是它灵活地捕捉潜在维度，这些维度控制在收购时观察到的行为以及未来购买倾向和使用深度指数族对营销行为做出反应。该模型可以与各种需求规格相集成，并且足够灵活以捕获广泛的异质结构。我们在零售环境中验证了我们的方法，并凭经验证明了该模型在识别高价值客户以及对营销活动最敏感的客户第一次购买后的能力。该模型可以与各种需求规格相集成，并且足够灵活以捕获广泛的异质结构。我们在零售环境中验证了我们的方法，并凭经验证明了该模型在识别高价值客户以及对营销活动最敏感的客户第一次购买后的能力。该模型可以与各种需求规格相集成，并且足够灵活以捕获广泛的异质结构。我们在零售环境中验证了我们的方法，并凭经验证明了该模型在识别高价值客户以及对营销活动最敏感的客户第一次购买后的能力。