Applying web scraping for e-commerce retailers
The rapid growth of e-commerce marketplaces has introduced new opportunities for smaller, emerging retailers to compete with large brands. Designing an optimal pricing and channel strategy across various marketplaces requires a comprehensive view of the market. Current e-commerce market research sol...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181139 |
| _version_ | 1824454241914191872 |
|---|---|
| author | Lim, Yi |
| author2 | Jun Zhao |
| author_facet | Jun Zhao Lim, Yi |
| author_sort | Lim, Yi |
| collection | NTU |
| description | The rapid growth of e-commerce marketplaces has introduced new opportunities for smaller, emerging retailers to compete with large brands. Designing an optimal pricing and channel strategy across various marketplaces requires a comprehensive view of the market. Current e-commerce market research solutions offer limited success in generating actionable insights for smaller retailers.
This project builds on existing solutions, demonstrating the effectiveness of web scraping to extract large amounts of market data for analysis. Using Python web scraping libraries such as Selenium WebDriver and Beautiful Soup, product data was scraped from five major e-commerce marketplaces -- Amazon, ASOS, JDSports, Farfetch, and Footlocker. The scraped data was visualized using Tableau to provide insights in areas including price distribution across marketplaces, competitors' pricing and product strategies, and consumer sentiment.
The insights generated provide e-commerce retailers with a comprehensive understanding of marketplace dynamics to determine an optimal pricing and channel strategy. The proposed solution achieves a greater depth of analysis over existing solutions. The advantages of scalability and resource-efficiency makes this project highly applicable to smaller retailers seeking to compete in the e-commerce market. |
| first_indexed | 2025-02-19T03:19:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181139 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:19:11Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1811392024-11-15T12:50:10Z Applying web scraping for e-commerce retailers Lim, Yi Jun Zhao College of Computing and Data Science junzhao@ntu.edu.sg Computer and Information Science The rapid growth of e-commerce marketplaces has introduced new opportunities for smaller, emerging retailers to compete with large brands. Designing an optimal pricing and channel strategy across various marketplaces requires a comprehensive view of the market. Current e-commerce market research solutions offer limited success in generating actionable insights for smaller retailers. This project builds on existing solutions, demonstrating the effectiveness of web scraping to extract large amounts of market data for analysis. Using Python web scraping libraries such as Selenium WebDriver and Beautiful Soup, product data was scraped from five major e-commerce marketplaces -- Amazon, ASOS, JDSports, Farfetch, and Footlocker. The scraped data was visualized using Tableau to provide insights in areas including price distribution across marketplaces, competitors' pricing and product strategies, and consumer sentiment. The insights generated provide e-commerce retailers with a comprehensive understanding of marketplace dynamics to determine an optimal pricing and channel strategy. The proposed solution achieves a greater depth of analysis over existing solutions. The advantages of scalability and resource-efficiency makes this project highly applicable to smaller retailers seeking to compete in the e-commerce market. Bachelor's degree 2024-11-15T12:50:10Z 2024-11-15T12:50:10Z 2024 Final Year Project (FYP) Lim, Y. (2024). Applying web scraping for e-commerce retailers. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181139 https://hdl.handle.net/10356/181139 en SCSE23-0296 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Lim, Yi Applying web scraping for e-commerce retailers |
| title | Applying web scraping for e-commerce retailers |
| title_full | Applying web scraping for e-commerce retailers |
| title_fullStr | Applying web scraping for e-commerce retailers |
| title_full_unstemmed | Applying web scraping for e-commerce retailers |
| title_short | Applying web scraping for e-commerce retailers |
| title_sort | applying web scraping for e commerce retailers |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/181139 |
| work_keys_str_mv | AT limyi applyingwebscrapingforecommerceretailers |