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Distribution Shift in Airline Customer Behavior during COVID-19

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  • Abhinav Garg
  • Naman Shukla
  • Lavanya Marla
  • Sriram Somanchi

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.

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  • Abhinav Garg & Naman Shukla & Lavanya Marla & Sriram Somanchi, 2021. "Distribution Shift in Airline Customer Behavior during COVID-19," Papers 2111.14938, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2111.14938
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    1. P. A. W Lewis & G. S. Shedler, 1979. "Simulation of nonhomogeneous poisson processes by thinning," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 26(3), pages 403-413, September.
    2. Hausman, Jerry & McFadden, Daniel, 1984. "Specification Tests for the Multinomial Logit Model," Econometrica, Econometric Society, vol. 52(5), pages 1219-1240, September.
    3. Sheth, Jagdish, 2020. "Impact of Covid-19 on consumer behavior: Will the old habits return or die?," Journal of Business Research, Elsevier, vol. 117(C), pages 280-283.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. P. A. W. Lewis & G. S. Shedler, 1979. "Simulation of Nonhomogeneous Poisson Processes with Degree-Two Exponential Polynomial Rate Function," Operations Research, INFORMS, vol. 27(5), pages 1026-1040, October.
    7. Adam Bockelie & Peter Belobaba, 2017. "Incorporating ancillary services in airline passenger choice models," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 553-568, December.
    8. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
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