Distribution Shift in Airline Customer Behavior during COVID-19
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 pa...
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| creator | Garg, Abhinav Shukla, Naman Marla, Lavanya Somanchi, Sriram |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2111.14938 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2111.14938</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computational Engineering, Finance, and Science ; Computer Science - Learning</subject><creationdate>2021-11</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2111.14938$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2111.14938$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Garg, Abhinav</creatorcontrib><creatorcontrib>Shukla, Naman</creatorcontrib><creatorcontrib>Marla, Lavanya</creatorcontrib><creatorcontrib>Somanchi, Sriram</creatorcontrib><title>Distribution Shift in Airline Customer Behavior during COVID-19</title><description>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.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computational Engineering, Finance, and Science</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAUQGEvDKjwAEz4BRJ8r-PYmVBJ-alUqQMVa3Tj2PRKbYKcpIK3R5ROZzvSJ8QdqLxwxqgHSt98yhEAcigq7a7F44rHKXE7Tzz08n3PcZLcyyWnA_dB1vM4DceQ5FPY04mHJLs5cf8p6-3HepVBdSOuIh3GcHvpQuxennf1W7bZvq7r5Saj0roMWwAMqExJDqMPvlBOqegUku-0NRQ1OI8e0GIoyVSx7CpbWN96Y7SPeiHu_7dnQfOV-Ejpp_mTNGeJ_gUl40JH</recordid><startdate>20211129</startdate><enddate>20211129</enddate><creator>Garg, Abhinav</creator><creator>Shukla, Naman</creator><creator>Marla, Lavanya</creator><creator>Somanchi, Sriram</creator><scope>ADEOX</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20211129</creationdate><title>Distribution Shift in Airline Customer Behavior during COVID-19</title><author>Garg, Abhinav ; Shukla, Naman ; Marla, Lavanya ; Somanchi, Sriram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-2b112e2056a82fcec40800f802acd375af318c2c1272e6a59f6d9747cbc553cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computational Engineering, Finance, and Science</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Garg, Abhinav</creatorcontrib><creatorcontrib>Shukla, Naman</creatorcontrib><creatorcontrib>Marla, Lavanya</creatorcontrib><creatorcontrib>Somanchi, Sriram</creatorcontrib><collection>arXiv Economics</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Garg, Abhinav</au><au>Shukla, Naman</au><au>Marla, Lavanya</au><au>Somanchi, Sriram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distribution Shift in Airline Customer Behavior during COVID-19</atitle><date>2021-11-29</date><risdate>2021</risdate><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.</abstract><doi>10.48550/arxiv.2111.14938</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computational Engineering, Finance, and Science Computer Science - Learning |
| title | Distribution Shift in Airline Customer Behavior during COVID-19 |
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