Distribution Shift in Airline Customer Behavior during COVID-19

TL;DR

This paper investigates how COVID-19 caused distribution shifts in airline customer behavior, affecting personalized pricing models, and demonstrates methods to detect and analyze these changes using advanced statistical techniques.

Contribution

It introduces a framework for detecting and analyzing distribution shifts in airline customer behavior during COVID-19 using covariate shift detection and causal inference methods.

Findings

Identification of customer segments with changed behavior

Attributes influencing behavior change identified

Techniques demonstrated on real-world and simulated data

Abstract

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.