{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105086"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105086","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic pricing for airline ancillaries with customer context","abstract":"Ancillaries in the travel industry have become a major source of income and profitability. However, conventional pricing strategies are based on poorly optimized business rules that do not respond to changing market conditions. This study describes the dynamic pricing model that we have developed in conjunction with Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy. In this study, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Additionally, we propose a meta-learning approach for synchronous deployment of multiple models. This approach is currently under production with our partner airline. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.","abstract_html":"Ancillaries in the travel industry have become a major source of income and profitability. However, conventional pricing strategies are based on poorly optimized business rules that do not respond to changing market conditions. This study describes the dynamic pricing model that we have developed in conjunction with Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy. In this study, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Additionally, we propose a meta-learning approach for synchronous deployment of multiple models. This approach is currently under production with our partner airline. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.","abstract_has_math":false,"creators":["Shukla, Naman"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Marla, Lavanya"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:36:09Z","date_published":"2019-08-23T20:36:09Z","updated_at":"2026-07-22T22:24:44Z","subjects":["dynamic pricing, airline ancillaries, contextual pricing, deep neural networks, classification, reinforcement learning, machine learning, multi armed bandit."],"languages":["en"],"rights":["Copyright 2019 Naman Shukla"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105086","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Marla, Lavanya"]},{"key":"dc:creator","label":"Author","values":["Shukla, Naman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:36:09Z","2021-08-24T09:15:20Z","2019-04-26","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["dynamic pricing, airline ancillaries, contextual pricing, deep neural networks, classification, reinforcement learning, machine learning, multi armed bandit."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Naman Shukla"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105086"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ancillaries in the travel industry have become a major source of income and profitability. However, conventional pricing strategies are based on poorly optimized business rules that do not respond to changing market conditions. This study describes the dynamic pricing model that we have developed in conjunction with Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy. In this study, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Additionally, we propose a meta-learning approach for synchronous deployment of multiple models. This approach is currently under production with our partner airline. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Naman Shukla, accepted the attached license on 2019-04-24 at 12:54.","The student, Naman Shukla, submitted this Thesis for approval on 2019-04-24 at 13:09.","This Thesis was approved for publication on 2019-04-26 at 08:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13881 on 2019-08-22 at 15:08:18","Made available in DSpace on 2019-08-23T20:36:09Z (GMT). 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However, conventional pricing strategies are based on poorly optimized business rules that do not respond to changing market conditions. This study describes the dynamic pricing model that we have developed in conjunction with Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy. In this study, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Additionally, we propose a meta-learning approach for synchronous deployment of multiple models. This approach is currently under production with our partner airline. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Naman Shukla, accepted the attached license on 2019-04-24 at 12:54.","The student, Naman Shukla, submitted this Thesis for approval on 2019-04-24 at 13:09.","This Thesis was approved for publication on 2019-04-26 at 08:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13881 on 2019-08-22 at 15:08:18","Made available in DSpace on 2019-08-23T20:36:09Z (GMT). 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