Discrete choice models and the assortment optimization problem are the fundamental aspects of the broader field of revenue management, which now spans a broad array of industries such as airlines, hotels and online advertising. The main focus here is to first study the consumer preferences and their substitution behavior when they are faced with multiple options, explain those observed behaviors with mathematical models and then identify an optimal set of options to offer to maximize revenues. This dissertation enriches the choice models and assortment optimization fields by studying the setting when such options are available in multitude, either to the sellers or to the consumers to choose from.
The first half of this dissertation focuses on the situation when sellers have access to a vast array of features to be chosen for products they want to offer. The second half of the dissertation focuses on the situation when customers are faced with a lot of options to choose from. This dissertation formulates concrete mathematical discrete choice models to tackle those situations, then studies the assortment optimization problem of maximizing the expected revenue resulting from these newly introduced choice models, and finally also designs efficient algorithms to solve them.
Chapter 1 explores discrete choice models which capture consumer behavior and choices when faced with a set of different alternatives, and the resulting assortment optimization problem along with the different existing algorithms for solving them as well as the existing challenges therein. Chapter 2 models and solves the problem when the sellers have access to a vast array of inventory of products. Chapter 3 models dynamic preferences of consumers and the choice overload phenomenon when the customers are faced with a lot of options, and solves the ensuing optimization problem. Chapter 4 showcases the applicability and effectiveness of such models and approaches on high dimensional data from a field experiment on Flipkart, the largest e-commerce firm in India.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-bq62-8534 |
| Date | January 2020 |
| Creators | Goutam, Kumar |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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