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.

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 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,...

According to forecasts, net sales of electrical products on Amazon are forecast at over *** billion U.S. dollars. With a compound annual growth rate of **** percent, this figure is expected to exceed *** billion dollars by 2026. Yet, the category expected to grow the strongest on the e-commerce platform is health and beauty.

This dataset includes 861 rows of books on the Amazon store in 2001.

The book's ISBN, Title, Weekly Sales, and Weekly Average Rank are provided.

Potential analysis can be implemented on the relationship between book rank and book sales. Even though the dataset is a bit "old", it still provides a certain vision to understand how Amazon has ranked each book.

With a good predictive model, we can even predict a book's potential sale on Amazon just given its rank.

In 2024, Amazon Web Services (AWS) generated ****** billion US dollars with its cloud services. From 2013 until today, the annual revenue of AWS cloud computing and hosting solutions continually increased.

Amazon—additional information Amazon.com went online in 1995, initially as a book store, and achieved almost immediate success. In 1998, the store expanded to include a music and video store and different other products, such as apparel and consumer electronics, in the following years. The company is the undisputed leader of the e-retail market in the United States, ranking ahead of walmart.com and apple.com in terms of revenue. Amazon Web Services In 2006, AWS launched as a cloud computing platform to provide online services. Amazon Elastic Compute Cloud and Amazon S3, which provide large virtual computing capacity, are the most well-known of these services. The company has dozens of locations in ** different regions across the world and is continually expanding its global infrastructure to ensure low latency through proximity to the user. From these data centers, Amazon is offering more than *** fully featured services to its global customer base. Video streaming service Netflix is one of AWS’s largest customers, using Amazon’s services to store their content on servers throughout the world. Among its more than *********** active users, AWS also lists other well-known organizations from various industries, such as Disney, the UK Ministry of Justice, Kellogg’s, Guardian News and Media, and the European Space Agency.

View quarterly updates and historical trends for The Amazon.com Inc (AMZN) - AWS Revenue. from United States. Source: Fiscal.ai. Track economic data with …

During the second quarter 2025, Amazon generated total net sales of nearly *** billion U.S. dollars, surpassing the *** billion U.S. dollars in the same quarter of 2024. From books to billions Launched in 1995 in the United States as an online bookshop, Amazon has since grown into an international e-commerce giant. In April 2023 worldwide visits to amazon.com amounted to over *** billion considering both desktop and mobile traffic. Prime time in the U.S. Although a global company, Amazon truly thrives in the United States where the company is the leading e-commerce platform by sales value. In the North American country, the number of subscribers using Amazon Prime services has been growing steadily over the last several years and is forecast to reach new heights in 2024.

Qualitative content analysis was conducted through three data types: annual reports, earnings calls and shareholder meetings, and terms of service agreements or “user contracts.” In addition to these three categories of data, summary statistics of revenues and assets were derived from financial data and leaked internal and court documents were examined. Data were obtained through the firms, from the Security and Exchange Commission’s (SEC) EDGAR system, Wharton WRDS, ToS agreements from firms and through the Internet Archive “Wayback Machine,” and leaked documents from secondary grey literature and Harvard’s “fbarchive.” I apply qualitative content analysis (QCA) as my method of inquiry using Atlas.ti qualitative data analysis software to help code and analyze study data.The categories of textual data are divided into user-targeted ToS agreements and investor-targeted reports. The latter category is divided into annual reports: 10-Ks, annual meetings termed “annual reports,” and earnings calls: follow up calls, 10-Qs, and other quarterly earnings presentations and reports, termed “earnings reports.” I began with conceptual analysis before moving to a relational analysis. I use a hybrid iterative deductive and inductive method of inquiry. I used an open, adaptive, coding process to inductively investigate the data and build a coding system. Financial data were used to summarize asset holdings, market capitalization, and revenue and earnings before interest, taxes, depreciation, and amortization (EBITDA). Financial statements, such as 8-Ks, and leaked internal documents underwent unstructured analysis to search for anomalous data. The structured content analysis approach outlined here was applied to annual reports, earnings calls, and Terms of Service (ToS) data for both cases. A total of 521 documents were reviewed, 268 in the three document categories revealing 22,652 quotations from three primary theme and three concept codes. I used Atlas.ti qualitative data analysis (QDA) software to apply a non-hierarchical coding structure to the data. Three primary theme concepts from the literature were applied to the data: “user,” “data,” and “value,” with variations of these themes used in search terms. These three primary theme concepts were applied in various combinations and new concepts were used after initial analysis. For example, an inductive analysis found that artificial intelligence (AI) was a frequently used relevant concept in the data and a consultation of theory and the literature links the concept to the “value” theme. The resulting adjusted concepts used were “user engagement,” “user data,” and “AI,” with a multitude of related search terms.

Amazon Brands and Exclusives

Dataset from "Amazon Puts Its Own 'Brands' First Above Better-Rated Products"

About this dataset

This dataset contains information on the quality and sales of products on Amazon, as well as on the percentage of time that Amazon's own products are featured in the Buy Box. It also includes data on the top searches and generic searches on Amazon, as well as on the percentage of panelists who ranked each trait as very important or somewhat important when choosing a product on Amazon

Research Ideas

• Determine which features are most important to customers when they are shopping on Amazon.
• Understand how Amazon's own products compare to other products in terms of quality and sales.
• Study how Amazon's marketing and ranking algorithms work, in order to optimize product listings on the site

Acknowledgements

Acknowledgements The datasets used in this article were provided by The Markup

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: combined_queries_with_source.csv | Column name | Description | |:----------------|:-----------------------------------------| | search_term | The search term used on Amazon. (String) | | source | The source of the search term. (String) |

File: quality_and_sales_comparisons.csv | Column name | Description | |:-------------------------------------|:----------------------------------------------------------------------------------------------| | search_term | The search term used on Amazon. (String) | | position_first_amazon | The position of the first Amazon product in the search results. (Integer) | | position_first_non_amazon | The position of the first non-Amazon product in the search results. (Integer) | | position_first_wholly_non_amazon | The position of the first wholly non-Amazon product in the search results. (Integer) | | amazon_stars | The average star rating for Amazon products in the search results. (Float) | | amazon_reviews | The average number of reviews for Amazon products in the search results. (Integer) | | non_amazon_stars | The average star rating for non-Amazon products in the search results. (Float) | | non_amazon_reviews | The average number of reviews for non-Amazon products in the search results. (Integer) | | wnon_amazon_stars | The average star rating for wholly non-Amazon products in the search results. (Float) | | wnon_amazon_reviews | The average number of reviews for wholly non-Amazon products in the search results. (Integer) |

File: amazon_trademarked_brands.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------| | Word Mark | The word mark of the product. (String) | | Goods and Services | The goods and services associated with the product. (String) | | Filing Date | The date on which the product was filed. (Date) |

File: fig2-scatter.csv | Column name | Description | |:-------------------|:----------------------------------------------------------------------------------------------------| | **** | | | Category | The category of the product. (String) | | Perc Products | The percentage of products in the category that are sponsored. (Float) | | Perc #1 spot | The percentage of products in the category that are in the #1 spot in the search results. (Float) | | Perc first row | The percentage of products in the category that are in the first row of the search results. (Float) |

File: fig3a-heatmap_amzn.csv | Column name | Description | |:-----...

"Amazon" pardavėjų asociacija financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

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)

Amazon reported $19.17B in Operating Profit for its fiscal quarter ending in June of 2025. Data for Amazon | AMZN - Operating Profit including historical, tables and charts were last updated by Trading Economics this last September in 2025.

This dataset contains product prices from Amazon UK, with a focus on price prediction. With a good amount of data on what price points sell the most, you can train machine learning models to predict the optimal price for a product based on its features and product name.

If you find this dataset useful, make sure to show your appreciation by upvoting! ❤️✨

Inspirations

This dataset is a superset of my Amazon UK product price dataset. Another inspiration is this competition that awareded 100K Prize Money

What To Do?

• Your objective is to create a prediction model that will assist sellers in pricing their products within the optimal price range to generate the most sales.
• The dataset includes various data points, such as the number of reviews, rating, best seller status, and items sold last month.
• You can select specific factors (e.g., over 100 reviews = optimal price for the product) and then divide the dataset into products priced optimally vs products priced unoptimally.
• By utilizing techniques like vectorizing product names and features, you can train a model to provide the optimal price for a product, which sellers or businesses might find valuable.

How to know if a product sells?

• I would prefer to use the number of reviews as a metric to determine if a product sells. More reviews = more sales, right?
• According to one source only 1-2% of buyers leave a review
• So if we multiply the reviews for a product by 50x, then we would get a good understanding how many units has sold.
• If we then multiple the product price by number of units sold, we'd get the total revenue generated by the product

How is this useful?

• Sellers and businesses can leverage your model to determine the optimal price for their products, thereby maximizing sales.
• Businesses can assess the profitability of a product and plan their supply chain accordingly.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

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.

This Excel-based dashboard visualizes Amazon sales performance using KPIs such as Total Sales, Profit, and Quantity Sold. Built with Pivot Tables, Slicers, and Charts, the dashboard includes regional breakdowns, product performance, and trend analysis. Suitable for beginners learning Sales Operations and Excel Dashboarding.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

Operator Financial Figures provided by ch-aviation include annual revenue figures (broken down to passenger, cargo, other, and total revenue), operating and net profit as well as employee numbers for each financial year including the financial year end date.

The data set is updated monthly.

The sample dataset shows financial figures for Swiss, Alaska Airlines, and Horizon Air from 2011.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=operator_financial_figures/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/operator-financial-figures/

HBO originally launched Max at a time when almost every cable TV conglomerate was releasing their own streaming service, to compete with Netflix and Amazon Prime Video. In Warner Bros case, it had...