Algorithmic advancements in the practice of revenue management
Author(s)
Amar, Jonathan Z.(Jonathan Zalman Aron Yaich)![Thumbnail](/bitstream/handle/1721.1/130724/1251803824-MIT.pdf.jpg?sequence=4&isAllowed=y)
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Other Contributors
Massachusetts Institute of Technology. Operations Research Center.
Advisor
Nikolaos Trichakis.
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In recent years, firms have been personalizing the customer experience by recommending specific products, and simultaneously these customers have raised their expectations in terms of personalization. To support these efforts, the firm must conceptualize its understanding of the market and the way customers interact with their products. This requires some modeling of how customers value each product and their attributes. The first part of the dissertation is dedicated to showing how one can go from data to personalization in retail applications. The first chapter's contribution is methodological, as we have worked closely with our partner beer retailer on providing store-specific assortment optimization. Using an efficient estimation procedure for choice models, conjointly with a novel application of collaborative filtering, we learn a demand model which is store specific and reliable, using a cautious validation procedure. Once armed with our model, we leverage continuous optimization techniques coupled with technical advances, to produce at scale personalized assortments which generate higher revenue subject to multiple business constraints. The second chapter considers a different setting relevant to new e-retailers which lack the data to inform their personalization. These usually rely on questionnaires to extract information. We incorporate the personalization task into the questionnaire design, which is driven by the product recommendation objective. We provide a framework for extending extant utility-estimation questionnaires, and additionally provide a direct approach leveraging robust optimization for tractability. We support our work with numerical simulations and theoretical justification in simplified settings, promising practical gains for personalization. While we have acknowledged data uncertainty in the first part, the second part of the dissertation is focused on the study of uncertainty in modern markets, and how to address it. The third chapter considers the canonical Network Revenue Management (NRM) problem. More specifically, we take the perspective of a monopoly seller which offers multiple products which consume capacitated resources. Given demand forecasts at the granularity of products may be unreliable, in cases where demand is highly volatile or sporadic -e.g. the airline, hotel industries-, we provide a distribution free algorithm for NRM, which is essentially robust to market uncertainties. By analyzing our algorithm's performance through the primal-dual schema, we establish its asymptotic optimality under the competitive lens. We benchmark our algorithm by showing that in regimes where the market is potentially rapidly changing, we outperform state of the art methods. Finally in the fourth chapter, we analyzed the problem faced by budget constrained e- advertisers. Ad-slots are allocated using a second price auction, that is the highest bidder wins the auction paying the second highest price. In this case, the advertiser is faced with a bidding decision, not knowing how much they will need to pay if they win. Considering the price uncertainty at the time of bid, which is specific to this modern market, we provide a methodology for converting plethora of knapsack algorithms to bidding strategies, implementable for this setting where the price is unknown. We show near-optimality of the bidding strategies, which in turn have substantial potential impact for e-advertisers.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, February, 2021 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 179-184).
Date issued
2021Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of ManagementPublisher
Massachusetts Institute of Technology
Keywords
Operations Research Center.