Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice because of the dynamic nature of customer buying patterns, particularly due to unanticipated system shocks such as COVID-19. We study the changes in customer behavior for a major airline during the COVID-19 pandemic by framing it as a covariate shift and concept drift detection problem. We identify which customers changed their travel and purchase behavior and the attributes affecting that change using (i) Fast Generalized Subset Scanning and (ii) Causal Forests. In our experiments with simulated and real-world data, we present how these two techniques can be used through qualitative analysis.

翻译：在定制(个性化)背景定价应用中传统的AI方法假定,在线定价时的数据分布与培训期间观察到的数据分布类似,但是,由于客户购买模式的动态性质,特别是由于COVID-19等意料之外的系统冲击,这一假设在实践中可能遭到违反。我们研究了COVID-19大流行期间大型航空公司客户行为的变化,将它描述为一种共变转移和概念漂移探测问题。我们用(一) 快速通用子集扫描和(二) Causal Forest,确定了哪些客户改变了旅行和购买行为以及影响这种变化的属性。我们在模拟和实际世界数据的实验中,介绍了这两种技术如何通过定性分析加以使用。