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dc.contributor.advisorGeorgia Perakis.en_US
dc.contributor.authorBaardman, Lennart.en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2019-10-04T21:31:43Z
dc.date.available2019-10-04T21:31:43Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122389
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 191-198).en_US
dc.description.abstractBig data and the internet are shifting the paradigm in promotional pricing and advertising. The amount of readily available data from both point-of-sale systems and web cookies has grown, enabling a shift from qualitative design to quantitative tools. In this work, we address how firms can utilize the power of analytics to maximize profits in both their offline and online channels. First, we consider an online setting, in which an advertiser can target ads to the customer in question. The goal of the advertiser is to determine how to target the right audience with their ads. We study this problem as a Multi-Armed Bandit problem with periodic budgets, and develop an Optimistic-Robust Learning algorithm with bounded expected regret. Practically, simulations on synthetic and real-world ad data show that the algorithm reduces regret by at least 10-20% compared to benchmarks.en_US
dc.description.abstractSecond, we consider an offline setting, in which a retailer can boost profits through the use of promotion vehicles such as flyers and commercials. The goal of the retailer is to decide how to schedule the right promotion vehicles for their products. We model the problem as a non-linear bipartite matching-type problem, and develop provably-good algorithms: a greedy algorithm and an approximate integer program of polynomial size. From a practical perspective, we test our methods on actual data and show potential profit increases of 2-9%. Third, we explore a supply chain setting, in which a supplier offers vendor funds to a retailer who promotionally prices the product to the customer. Vendor funds are trade deals in which a supplier offers a retailer a short-term discount on a specific product, encouraging the retailer to discount the product.en_US
dc.description.abstractWe model the problem as a bilevel optimization model and show that a pass-through constrained vendor fund mitigates forward-buying and coordinates the supply chain on the short term. Finally, we present a pilot study on the impact of promotional pricing on retail profits. We assess the potential impact of our promotion planning tool on historical data from a large retailer. Our results suggest a 9.94% profit improvement for the retailer.en_US
dc.description.statementofresponsibilityby Lennart Baardman.en_US
dc.format.extent198 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleAnalytics in promotional pricing and advertisingen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.identifier.oclc1120105762en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Centeren_US
dspace.imported2019-10-04T21:31:43Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentOperResen_US


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