1) Data Introduction • The Amazon Sales Dataset includes e-commerce product and consumer feedback data, including details on more than 1,000 products collected from Amazon's official website, discount prices, ratings, reviews, and categories.

2) Data Utilization (1) Amazon Sales Dataset has characteristics that: • The dataset includes a variety of product and review-related attributes, including product ID, product name, category, real and discounted prices, discount rates, ratings, rating numbers, product descriptions, user reviews, images, and product links. (2) Amazon Sales Dataset can be used to: • Product Rating and Review Analysis: Use rating and review data to analyze consumer satisfaction, popular products, review trends, and develop marketing strategies for each product. • Development of Price Policy and Recommendation System: Based on price information such as actual price, discount price, and discount rate, it can be used for price policy analysis, product recommendation system, consumer purchasing behavior prediction, etc.

In 2019, Amazon's retail e-commerce sales in the United States amounted to ***** billion U.S. dollars and are projected to surpass *** billion U.S. dollars in 2021. The platform is the biggest e-retailer in the United States, ahead of brick-and-mortar-based competitors Walmart and Target.

Dataset

This dataset was created by pothula swaraj

Contents

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost *** billion U.S. dollars, up from *** billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over *** billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately *** billion U.S. dollars was earned in North America compared to only roughly *** billion U.S. dollars internationally.

Amazon Products dataset to explore detailed product listings, pricing, reviews, and sales data. Popular use cases include competitive analysis, market trend forecasting, and e-commerce strategy optimization.

Use our Amazon Products dataset to explore detailed information on products across various categories, including pricing, reviews, ratings, and sales data. This dataset is ideal for e-commerce professionals, market analysts, and product managers looking to analyze market trends, optimize product listings, and refine competitive strategies.

Leverage this dataset to track pricing trends, assess customer feedback, and uncover popular product categories. Whether you're conducting competitive analysis, performing market research, or optimizing product strategies, the Amazon Products dataset provides key insights to stay ahead in the e-commerce landscape.

Dataset Features

• Title: The name or title of the product.
• seller_name: The name of the seller offering the product.
• Brand: The brand associated with the product.
• Description: A detailed description of the product, including key features.
• initial_price: The original price of the product before any discounts.
• final_price: The current price of the product after discounts.
• Currency: The currency in which the product is priced (e.g., GBP, USD).
• Availability: The stock status (e.g., in stock, out of stock).
• reviews_count: The total number of customer reviews.
• Categories: The specific category the product belongs to.
• asin: Amazon Standard Identification Number.
• buybox_seller: The seller currently winning the Amazon Buy Box.
• number_of_sellers: The number of sellers offering this product.
• root_bs_rank: The overall ranking of the product in the Amazon best-sellers list.
• answered_questions: The number of questions answered in the product Q&A section.
• domain: The website domain where the product is being sold.
• images_count: The number of images available for the product.
• URL: The link to the product page on Amazon.
• video_count: The number of videos available for the product.
• image_url: The URL of the primary image associated with the product.
• item_weight: The weight of the product.
• Rating: The average rating of the product based on customer reviews.
• product_dimensions: The dimensions of the product (e.g., length, width, height) and weight.
• seller_id: The unique identifier for the seller.
• date_first_available: The date when the product was first made available on Amazon.
• discount: Any discount applied to the product.
• model_number: The model number of the product.
• manufacturer: The company that manufactures the product.
• department: The department under which the product is categorized (e.g., Health & Household).
• plus_content: A flag indicating if the product has Amazon’s “Plus Content” (additional marketing content).
• upc: The Universal Product Code (UPC) associated with the product.
• video: URL(s) of any video content associated with the product.
• top_review: A summary or excerpt from the top customer review.
• variations: Different product variations (e.g., different sizes or flavors).
• delivery: Information on the delivery options (e.g., free delivery or Prime delivery).
• features: Key features or highlights of the product.
• format: The format of the product (e.g., powder, liquid).
• buybox_prices: Pricing details for the product, including the base and tiered prices.
• parent_asin: The ASIN of the parent product (if the product is part of a larger group of similar products).
• input_asin: The ASIN of the product as input for Amazon searches.
• ingredients: List of ingredients in the product (if applicable).
• origin_url: The source URL for product-related information or ingredients.
• bought_past_month: A flag indicating if the product was bought in the past month.
• is_available: Availability status of the product (True/False).
• root_bs_category: The broad product category (e.g., Health & Household).
• bs_category: The specific subcategory the product belongs to.
• bs_rank: The rank of the product in its specific subcategory.
• badge: Any badge or label the product has earned (e.g., Amazon's Choice).
• subcategory_rank: The rank of the product within its subcategory.
• amazon_choice: A flag indicating if the product has been selected as Amazon’s Choice.
• images: A list of URLs for additional product images.
• product_details: Detailed product specifications and features.
• prices_breakdown: A breakdown of the price, including any discounts or promotions.
• country_of_origin: The country where the product is made.
• from_the_brand: Information from the brand or manufact

1) Data Introduction • The Amazon Products Sales Dataset 2023 is a large e-commerce dataset that summarizes various product information in a tabular format, including product name, price, rating, discount information, images, and links by 142 major categories collected from Amazon's website.

2) Data Utilization (1) Amazon Products Sales Dataset 2023 has characteristics that: • Each row contains 10 key attributes, including product name, main/subcategory, image, Amazon link, rating, number of ratings, discount price, and actual price. • The data encompasses a wide range of products and is structured to enable multi-faceted analysis such as price policy, customer evaluation, and trend by category. (2) Amazon Products Sales Dataset 2023 can be used to: • Product Recommendation and Marketing Strategy: Use rating, price, and category data to develop a customized recommendation system, analyze popular products, and establish a category-specific marketing strategy. • Price and Discount Policy Analysis—Based on discounted prices and actual prices, ratings, reviews, etc., it can be applied to effective pricing policies, promotion strategies, market competitiveness analyses, and more.

In 2024, Amazon's net revenue from subscription services segment amounted to 44.37 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 200 million paying members worldwide at the end of 2020. The AWS category generated 107.56 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 638 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

Amazon revenue for the twelve months ending March 31, 2025 was $650.313B, a 10.08% increase year-over-year. Amazon annual revenue for 2024 was $637.959B, a 10.99% increase from 2023. Amazon annual revenue for 2023 was $574.785B, a 11.83% increase from 2022. Amazon annual revenue for 2022 was $513.983B, a 9.4% increase from 2021.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

In 2024, Amazon's total consolidated net sales revenue amounted to *** billion U.S. dollars, *** billion U.S. dollars of which were generated through international revenue channels. North America was the biggest operations segment, accumulating nearly *** billion U.S. dollars in net sales during the year. Sales activities Amazon appeals because it sells a wide range of products. Its departments include beauty, clothing, electronics, games and even wine, along with digital products and subscription services. In 2022, Amazon's largest revenue segment was online retail product sales with roughly *** billion U.S. dollars in global net sales. Retail third-party seller services ranked second with nearly *** billion U.S. dollars in sales. A weak spot Faster and more efficient delivery services come with a price. Data from the company's financial reports showed that Amazon's worldwide shipping costs amounted to a staggering **** billion U.S. dollars, up from **** billion U.S. dollars in 2021. Amazon's annual fulfillment expenses have also risen steadily, from **** billion U.S. dollars in 2021 to over ** billion U.S. dollars in 2022.

Amazon enjoyed staggering sales growth in United Kingdom over the past decade, taking net sales from roughly four billion to almost 33.6 billion U.S. dollars in 2023. That makes the UK the retail behemoth’s second biggest European market, sitting behind Germany where the company reported total net sales of about 37.6 billion U.S. dollars in 2023.

Amazon’s other UK presence Amazon runs 20 distribution services in the UK, where Amazon has its largest European logistics and fulfillment presence. Operating under the “Amazon UK Services” name, the retailer generated over two billion British pounds in 2018. This represented over 200 percent turnover growth since 2015.

Consumers have no problem shopping with Amazon

In proportion to the pace Amazon’s retail empire is expanding, worries are voiced within the industry about the monopoly held by the retailer, not to mention the privacy concerns revolving around Amazon’s own brand smart devices. Yet shoppers seem unfazed, as convenience and variety offered by the retailer convert more and more people into being Amazon shoppers. A recent survey conducted with UK shoppers found out that only a small share of consumers felt guilty about or actively chose not shopping with Amazon. In comparison, nearly one quarter of those surveyed said they “loved” shopping with Amazon.

• 500K+ Active Amazon Stores • 200K+ Seller Leads • Platforms USA, Germany, UK, Italy, France, Spain, CA • C-Suite/Marketing/Sales Contacts • FBA/Non-FBA Sellers • 15+ data points available for each prospect • Filter your leads by store size, niche, location, and many more • 100% manually researched and verified.

For over a decade, we have been manually collecting Amazon seller data from various data sources such as Amazon, Linkedin, Google, and others. We are specialized to get valid, and potential data so you may conduct ads and begin selling without hesitation.

We designed our data packages for all types of organizations, thus they are reasonably priced. We are always trying to reduce our prices to better suit all of your requirements.

So, if you’re looking to reach out to your targeted Amazon sellers, now is the greatest time to do so and offer your goods, services, and promotions. You can get your targeted Amazon Sellers List with seller contact information.

Alternatively, if you provide Amazon Seller Names or IDs, we will conduct Custom Research and deliver the customized list to you.

Data Points Available:

Full Name Linkedin URL Direct Email Generic Phone Number Business Name and Address Company Website Seller IDs and URLs Revenue Seller Review Count Niche FBA/Non-FBA Country and More

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)

Introduction

Amazon, Alibaba, and eBay: The Giants of E-commerce

Three companies have revolutionised how we shop online: Amazon, Alibaba, and eBay. Their origins, growth, and impact on global commerce are remarkable: - Amazon: Founded by Jeff Bezos in 1994, Amazon began as an online bookstore. It rapidly expanded its product range, invested heavily in technology and logistics, and introduced groundbreaking services like Amazon Prime and Amazon Web Services (AWS). Today, Amazon is a leader in e-commerce, cloud computing, and innovation. - Alibaba: Founded by Jack Ma in 1999, Alibaba aimed to connect Chinese manufacturers with international buyers. Through platforms like Alibaba.com, Taobao, and Tmall, it transformed e-commerce in China and became a global player in digital payments and financial services through Ant Group. - eBay: Started by Pierre Omidyar in 1995 as an online auction site, eBay quickly became a popular platform for buying and selling a wide variety of goods. It pioneered consumer-to-consumer (C2C) commerce, fostered a vibrant online community, and expanded globally.

These companies have distinct strengths and growth trajectories: - Amazon leads in technological innovation and customer-centric services. - Alibaba dominates the Chinese market and is influential in digital payments. - eBay pioneered C2C commerce and maintains a strong global presence.

Together, Amazon, Alibaba, and eBay have shaped the modern e-commerce landscape, democratised commerce, and continue to influence how we buy and sell goods around the world.

Amazon, Alibaba, and eBay, their origins, sales, growth, and impact:

Amazon

Founding and Early Days

• Founded: July 5, 1994
• Founder: Jeff Bezos
• Headquarters: Seattle, Washington, USA

Amazon began as an online bookstore. Jeff Bezos, who was then a Wall Street hedge fund executive, decided to capitalise on the growth of the internet in the 1990s. He left his job, moved to Seattle, and started Amazon in his garage.

Growth and Expansion

• Initial Growth: The company's initial business model allowed it to offer more books than traditional bookstores. The first book sold was "Fluid Concepts and Creative Analogies" by Douglas Hofstadter.
• Diversification: By the late 1990s, Amazon expanded its product line to include electronics, toys, and household goods. This diversification helped Amazon grow rapidly.
• Technology and Logistics: Amazon invested heavily in technology and logistics to improve its supply chain efficiency. It built extensive warehousing and delivery infrastructure, which became critical to its success.

Key Milestones

• Amazon Prime: Launched in 2005, this subscription service offers free two-day shipping, streaming services, and other benefits. It became a significant driver of customer loyalty.
• AWS (Amazon Web Services): Started in 2006, AWS provides cloud computing services and has become a major revenue source for Amazon, establishing it as a leader in the tech industry.
• Acquisitions: Amazon acquired several companies to bolster its offerings, including Zappos, Whole Foods Market, and Ring.

Impact

• E-commerce Leader: Amazon revolutionised online shopping with its vast selection, customer reviews, and recommendation algorithms.
• Economic Influence: The company's practices have influenced labour, retail, and technology sectors globally.
• Innovation: Amazon has pioneered in areas like AI with Alexa, cashier-less stores with Amazon Go, and drone delivery with Prime Air.

Visualisation

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fdeff29b30a7e890d46ecfd32b8e468cc%2FScreenshot%202024-06-05%2020.26.14.png?generation=1717615837865701&alt=media" alt="">

A Markdown document with R code for the above chart, with a full chart explanation. link

Amazon Chart

• Significant Growth: Steady increase in quarterly sales revenue over the years.
• Seasonal Influence: Strong seasonal fluctuations, with Q4 consistently being the highest-performing quarter due to the holiday season.
• Revenue Variability: Expanding range of quarterly sales, reflecting business growth.
• Quarterly Performance: Consistent relative performance of each quarter, with Q4 standing out.

Alibaba

Founding and Early Days

• Founded: April 4, 1999
• Founder: Jack Ma and a team of 17 others
• Headquarters: Hangzhou, Zhejiang, China

Jack Ma, a former English teacher, founded Alibaba to connect Chinese manufacturers with international buyers. He aimed to support small and medium-sized enterprises (SMEs) in China by leveraging the internet.

Growth and Expansion

• Early Strategy: Alibaba started with Alibaba.com, a B2B marketplace that connected Chinese bu...

Online shopping sales across India amounted to around ** billion U.S. dollars in 2021. The e-commerce market is likely to grow to over *** billion U.S. dollars by 2025. The e-commerce market in India is the fastest-growing market in the world. Online retail segments In fiscal year 2017, the retail market was led by electronics with a penetration rate of about ** percent. However, in terms of groceries, local offline vendors or kiranas continued to be the preferred choice for daily groceries due the ease of bargaining and benefitting from the ‘old-customer’ designation with extra rations as a gesture from the vendor. Nevertheless, the number of online shoppers in the country was estimated to increase to over *** million in 2025, up from around ** million in 2017. Impact of COVID-19 on the marketThe coronavirus outbreak in March 2020 caused a surge in prices across e-commerce platforms. Panic purchasing resulted in the shortage of sanitary and food items online as well as in physical stores across the country. As the online consumption continued to increase, unscrupulous sellers jacked up the prices on certain items. Amazon and Flipkart, the two e-commerce market leaders in India urged sellers and even blocked certain products to exercise responsible pricing. Manufacturers increased production in order to keep up with the supply of fast-moving items. With the uncertainty surrounding the impact of COVID-19, manufacturers and retailers will presumably have to work in unison to keep track of an unprecedented demand and supply scenario.

The combined number of full- and part-time employees of Amazon.com has increased significantly since 2017. Amazon’s headcount peaked in 2021 when the American multinational e-commerce company employed ********* full- and part-time employees, not counting external contractors. However, in 2024, the number dropped to *********. E-commerce crunch The workforce reduction of Amazon follows the mass layoffs hitting the entire e-commerce sector. With the full reopening of physical stores after the COVID-19 pandemic, online shopping demand decreased, leading online retailers to restructure their businesses, including personnel costs. Diversifying business With online retail sales growing slower due to recession and inflation, Amazon can still leverage other profitable revenue segments — from media subscriptions to server hosting and cloud services. On top of that, in 2023 Amazon monitored small enterprises operating in different fields and strategically invested in them, as disclosed startup acquisitions indicate.