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Distribution Shift in Airline Customer Behavior during COVID-19

Abhinav Garg, Naman Shukla, Lavanya Marla, Sriram Somanchi

arXiv 29 Nov 2021 · Machine Learning · 1 citations (OpenAlex)

arXiv:2111.14938 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

14
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Edward III McFowland, Skyler Speakman, and Daniel B. Neill Fast generalized subset scan for anomalous pattern detection0.81142100%
2Susan Athey, Julie Tibshirani, and Stefan Wager (2019) Generalized random forests0.73732100%
3Naman Shukla, Arinbjörn Kolbeinsson, Ken Otwell, Lavanya Marla, and… (2019) Dynamic pricing for airline ancillaries with customer context self0.5112250%
4Susan Athey and Guido W. Imbens (2019) Machine learning methods that economists should know about0.40511100%
5Robert H. Berk and Douglas H. Jones (1979) Goodness-of-fit test statistics that dominate the kolmogorov statistics0.40511100%
6Adam Bockelie and Peter Belobaba (2017) Incorporating ancillary services in airline passenger choice models0.40511100%
7Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi (2016) Analytics for an online retailer: Demand forecasting and price optimization0.40511100%
8Paul W. Holland (1986) Statistics and causal inference0.40511100%
9Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and… (2009) Dataset Shift in Machine Learning0.40511100%
10Jagdish Sheth (2020) Impact of covid-19 on consumer behavior: Will the old habits return or die?0.40511100%

Showing the top 10 of 14 scored citations.