Developing an eCommerce Pricing Model Using Rank Centrality
In recent years, eCommerce websites have become a popular alternative to traditional marketplaces, providing convenience to customers to order products from home and have them shipped. As a result, competition between sellers on the eCommerce websites has intensified in recent years, making a pricin...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156941 |
| _version_ | 1824458306398191616 |
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| author | Tong, Kevin C. |
| author2 | Shah, Devavrat |
| author_facet | Shah, Devavrat Tong, Kevin C. |
| author_sort | Tong, Kevin C. |
| collection | MIT |
| description | In recent years, eCommerce websites have become a popular alternative to traditional marketplaces, providing convenience to customers to order products from home and have them shipped. As a result, competition between sellers on the eCommerce websites has intensified in recent years, making a pricing strategy necessary to perform well in this marketplace.
This paper attempts to model eCommerce competition between different sellers using the principle of Rank Centrality, and uses neural networks to accurately predict the winning seller on eCommerce websites, such as Amazon, based on factors including pricing, seller rating, and shipping guarantees for each seller. Using this prediction, a pricing strategy is formed to maximize sales volume and profits on these sites. This strategy is then implemented and evaluated as part of a 6-month internship with Spero Goods. |
| first_indexed | 2025-02-19T04:23:48Z |
| format | Thesis |
| id | mit-1721.1/156941 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-02-19T04:23:48Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1569412024-09-25T03:17:32Z Developing an eCommerce Pricing Model Using Rank Centrality Tong, Kevin C. Shah, Devavrat Xia, Lei Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science In recent years, eCommerce websites have become a popular alternative to traditional marketplaces, providing convenience to customers to order products from home and have them shipped. As a result, competition between sellers on the eCommerce websites has intensified in recent years, making a pricing strategy necessary to perform well in this marketplace. This paper attempts to model eCommerce competition between different sellers using the principle of Rank Centrality, and uses neural networks to accurately predict the winning seller on eCommerce websites, such as Amazon, based on factors including pricing, seller rating, and shipping guarantees for each seller. Using this prediction, a pricing strategy is formed to maximize sales volume and profits on these sites. This strategy is then implemented and evaluated as part of a 6-month internship with Spero Goods. M.Eng. 2024-09-24T18:21:56Z 2024-09-24T18:21:56Z 2024-05 2024-07-11T14:37:44.219Z Thesis https://hdl.handle.net/1721.1/156941 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Tong, Kevin C. Developing an eCommerce Pricing Model Using Rank Centrality |
| title | Developing an eCommerce Pricing Model Using Rank Centrality |
| title_full | Developing an eCommerce Pricing Model Using Rank Centrality |
| title_fullStr | Developing an eCommerce Pricing Model Using Rank Centrality |
| title_full_unstemmed | Developing an eCommerce Pricing Model Using Rank Centrality |
| title_short | Developing an eCommerce Pricing Model Using Rank Centrality |
| title_sort | developing an ecommerce pricing model using rank centrality |
| url | https://hdl.handle.net/1721.1/156941 |
| work_keys_str_mv | AT tongkevinc developinganecommercepricingmodelusingrankcentrality |