Distribution Shift in Airline Customer Behavior during COVID-19
This work addresses the issue of dynamic customer behavior shifts for airlines during system shocks like COVID-19, which is incremental as it applies existing methods to a new context.
The paper tackles the problem of distribution shift in airline customer behavior during COVID-19, framing it as covariate shift and concept drift detection, and uses Fast Generalized Subset Scanning and Causal Forests to identify changes in customer behavior and attributes, with results demonstrated through qualitative analysis on simulated and real-world data.
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.