In 2024, Amazon was expected to generate roughly 53 billion U.S. dollars in retail media ad revenue, which would be an increase of 16 percent on its 2023 result. Alibaba, on the other hand, expected a growth rate of 1.4 percent year on year, which would put the Chinese platform at 42 billion dollars in retail media ad revenue in the same year.

Dataset

This dataset was created by Ilhem Djenane

Released under Database: Open Database, Contents: Database Contents

Contents

Amazon's shopping app has seen a significant increase in monthly downloads worldwide since January 2015, reaching a peak of approximately 25 million downloads in August 2022. However, the company faces stiff competition from emerging players in the global market. Rising competition in mobile shopping While Amazon remains a major player, Chinese retailers are making significant inroads in the mobile shopping space. In 2024, Temu became the most downloaded shopping app worldwide with nearly 550 million downloads, followed by Shein with about 235 million. Despite this competition, Amazon continues to maintain a strong presence, particularly in the United States, where it ranks as the second-most used shopping app among millennials. Generational differences Amazon's influence on consumer spending varies significantly across generations, with Generation X leading the pack in overall expenditure. In 2023, a study of iOS users in the United States revealed that Gen X shoppers spend an average of 124.2 U.S. dollars across all departments on Amazon, outpacing both Millennials and Gen Z. This generational divide in spending habits reflects broader trends in online shopping preferences and brand loyalty.

Amazon is one of the most recognisable brands in the world, and the third largest by revenue. It was the fourth tech company to reach a $1 trillion market cap, and a market leader in e-commerce,...

YouTube has emerged as the dominant social media platform for driving traffic to Amazon.com, accounting for over 60 percent of referrals to the e-commerce platform between July and September 2025. Facebook.com and Reddit.com followed, contributing about 13 and eight percent of social media referrals respectively, while Reddit and WhatsApp rounded out the top five sources. Amazon's dominance Amazon's position as the leading online retailer in the United States is evident in its traffic and sales figures. In December 2023, Amazon still recorded an impressive 2.7 billion combined visits. The company's financial performance remains strong, with a net income of approximately 13.5 billion U.S. dollars in the second quarter of 2024, up from the previous quarters. Mobile presence Amazon's mobile presence continues to grow, with its shopping app downloads reaching a nine-year peak in August 2022 at approximately 25 million. As of July 2024, the Amazon Shopping app reached over 18 million downloads across iOS and Android platforms. That month, Amazon’s shopping app was the most popular app published by the e-commerce and tech giant.

Amazon is known as an e-commerce company, but in recent years, the retailer has invested in opening physical stores across the United States with more international expansion in mind. Amazon’s physical retail stores come in different formats, including Amazon Fresh grocery stores, Amazon Go, Amazon Books, Amazon 4 Star, and Amazon Pop-up. Typically, Amazon’s branded devices, books and other merchandise are available in these stores. In the fourth quarter of 2024, net sales from Amazon’s physical retailing amounted to nearly 5.8 billion U.S. dollars. Whole Foods acquisition and Amazon Fresh Amazon’s venture into brick-and-mortar grocery store retailing started with the acquisition of the Whole Foods Market in 2018. By 2017, just before it was bought out by Amazon, the supermarket Whole Foods had registered a net sales revenue of over 16 billion U.S. dollars. In addition to some 500 Whole Foods locations, Amazon’s grocery retail business is supported by Amazon Fresh with stores predominantly in the United States. Outside of the United States, Amazon opened its first Amazon Fresh stores in the United Kingdom in March 2021. Amazon’s retail portfolio Amazon has a diverse retail portfolio, both in terms of merchandise and the business models it offers across its platforms. While it started its e-commerce business as an online retailer acting as the first-party owner of the products on offer, third-party selling on the Amazon marketplace increasingly became the norm among online sellers, who often employ both models when working with Amazon. Since 2017, more than half of paid units of Amazon is attributed to third-party sellers using the Amazon marketplace to sell their products.

Dataset

This dataset was created by dreadheadhock099

Contents

Cart abandonment rates have been climbing steadily since 2014, after reaching an all-time high in 2013. In 2023, the share of online shopping carts that is being abandoned reached 70 percent for the first time since 2013. This is an increase of more than 10 percentage points compared to the start of the time period considered here. Mobiles vs. desktops When global consumers shop online, they spend considerably more when doing so on desktop computers. In December 2023, the average value of e-commerce purchases made through desktops was approximately 159 U.S. dollars. Purchases completed on mobiles and tablets were of comparable values, ranging between 100 and 105 U.S. dollars. Even though consumers spent more when conducting their shopping on computers, they were more inclined to add products to their shopping carts when using mobile devices. Ultimately, mobile devices provide a convenient and more accessible way to shop, but desktop computers remain the preferred choice for more expensive purchases. Where do consumers shop online? Across the globe, digital marketplaces are shoppers’ number-one online shopping destination. As of April 2024, some 29 percent of consumers voted marketplaces as their favorite e-commerce channel, followed by physical stores and retailer sites. Looking at which retailers’ global shoppers prefer to shop at, amazon.com emerged as the world's most popular online marketplace, based on share of visits. The U.S. portal accounted for around one-fifth of the global online marketplace's traffic in December 2023. Amazon's German and Japanese portal sites ranked third and fifth among the leading online marketplaces, further demonstrating Amazon's dominance over the market.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

These datasets contain 1.48 million question and answer pairs about products from Amazon.

Metadata includes

• question and answer text

• is the question binary (yes/no), and if so does it have a yes/no answer?

• timestamps

• product ID (to reference the review dataset)

Basic Statistics:

• Questions: 1.48 million

• Answers: 4,019,744

• Labeled yes/no questions: 309,419

• Number of unique products with questions: 191,185

From January to May 2025, the Amazon Shopping app was the most popular app published by the e-shopping and cloud computing giant, generating around 60 million downloads from iOS and Google Play Store users during the period. Second-ranked Amazon Prime Video was downloaded 6.88 million times from global users. The Amazon Photo app, which allows users to store and share photos, was downloaded 5.36 million times by users worldwide.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

Dataset

This dataset was created by Naqi Raza

Contents

Dataset

This dataset was created by Aditya Das

Released under MIT

Contents

Context

This dataset was created by PromptCloud and Datastock. This dataset has a 30 K record count. You can download the full dataset here.

Content

This dataset contains the following:

• Total Records Count: 85405 
• Domain Name :: yellowpages.com 
• Last Crawled: 21st Feb 2015 
• File Extension :: CSV
• Available Fields : -- Uniq Id, -- Url, -- Name, -- Street, -- Zip Code, -- City, -- State, -- Phone, -- Email, -- Website, -- Categories 

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset

This dataset was created by Kashmala Malik

Released under Apache 2.0

Contents

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

In 2025, Amazon's brand value increased by 50 percent, standing at 866 billion U.S. dollars. In 2023, the value recorded the only decline since 2015. In general, between 2006 and 2025, the figure had increased over 100-fold. Diversification is key Amazon ranked fourth among the leading brands worldwide in 2025. One of the brand's key growth drivers is its continued diversification of revenue streams. In addition to selling millions of products via its online platform, Amazon also moved beyond the realm of retail by launching the cloud platform Amazon Web Services (AWS), selling ad space, and extending its Prime subscription features. Spotlight on retail brands What makes Amazon stand out from many of the world's most valuable retail brands is its focus on e-commerce and the platform's extensive global availability. While U.S.-based competitors such as Walmart mainly operate in North America, Amazon serves millions of users across the globe. Zooming in on the Asia-Pacific region, the highest-valued retail brand in China was Pinduoduo in 2024.

Dataset

This dataset was created by Rosalind Xie

Contents

Background

A few weeks ago, a friend and I wanted to do some data analysis on some big data from Amazon. However, the volume & quality of data available let some things to be desired. So, I took an attempt at creating a dataset of my own and got a little closer. But, it overly relied on using a web driver and didn't scale very well. So, I rewrote it using data from SmartScout, and here we are.

Use

Given that all of this data is scraped from websites, it most definitely breaks a few terms of services. But, you can't make an omelet without first breaking a couple of eggs. So, please use this data with caution.

In order to get most of the data (10 million rows, 650 GiB) you'll need to get the CSVs from a torrent that I have posted here. If my computer is turned off or my ISP blocked the tracker that you use, send me a message on my website and I'll sort it out.