Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp0147429c88q
Title: | Counting Stars: A Pipeline for Amazon Consumer and Product Analytics with Case Studies in Discretionary Products |
Authors: | Yin, Jennifer |
Advisors: | Wang, Mengdi |
Department: | Operations Research and Financial Engineering |
Class Year: | 2018 |
Abstract: | The rise of e-commerce in recent years has forced traditional retailers to change old business practices to keep up. In particular, many businesses have turned to big data in business analytics to keep up with changing consumer dynamics. Unsurprisingly, Amazon has reigned in both ecommerce and consumer analytics, accounting for 53% of the growth in US retail sales in 2017. To understand new consumer dynamics in a changing retail landscape, this thesis develops an analysis pipeline for Amazon products. Starting with the initial data collection, this pipeline scrapes the information for a product group and analyzes the reviews and ratings for each individual product to characterize consumer shopping trends for Amazon products. |
URI: | http://arks.princeton.edu/ark:/88435/dsp0147429c88q |
Type of Material: | Princeton University Senior Theses |
Language: | en |
Appears in Collections: | Operations Research and Financial Engineering, 2000-2019 |
Files in This Item:
File | Description | Size | Format | |
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YIN-JENNIFER-THESIS.pdf | 2.09 MB | Adobe PDF | Request a copy |
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