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Title:Dynamic pricing for airline ancillaries with customer context
Author(s):Shukla, Naman
Advisor(s):Marla, Lavanya
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):dynamic pricing, airline ancillaries, contextual pricing, deep neural networks, classification, reinforcement learning, machine learning, multi armed bandit.
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.
Issue Date:2019-04-26
Type:Thesis
URI:http://hdl.handle.net/2142/105086
Rights Information:Copyright 2019 Naman Shukla
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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