Gain extensive insights with our Amazon datasets, encompassing detailed product information including pricing, reviews, ratings, brand names, product categories, sellers, ASINs, images, and much more. Ideal for market researchers, data analysts, and eCommerce professionals looking to excel in the competitive online marketplace. Over 425M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more

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.

Note: This dataset contains the 'Apparel' data from many of the datasets previously made available by Amazon for academic research purposes. The original source links are provided below: Dataset Readme, provided by Amazon: https://s3.amazonaws.com/amazon-reviews-pds/readme.html All Customer Review Datasets by Amazon: https://s3.amazonaws.com/amazon-reviews-pds/tsv/index.txt

Amazon Customer Reviews Dataset Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazon’s iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Content marketplace - Country code of the marketplace where the review was written customer_id - ID of the customer reviewed the product review_id - The unique ID of the review product_id - The unique ID of the product product_parent - ID of the parent category product_title - Title of the product product_category - Broad product category, here only 'Apparel' data is available star_rating - The 1-5 star rating of the review helpful_votes - Number of helpful votes total_votes - Number of total votes the review received vine - The review was written as part of the Vine program or not verified_purchase - The review is on a verified purchase review_headline - The title of the review review_body - The review text review_date - The date the review was written

License By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the Amazon.com Conditions of Use (https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. https://s3.amazonaws.com/amazon-reviews-pds/license.txt

Useful Links Provided by Amazon: https://s3.amazonaws.com/amazon-reviews-pds/readme.html Amazon Customer Review Available Datasets: https://s3.amazonaws.com/amazon-reviews-pds/tsv/index.txt

NOTE: This dataset is made available in Kaggle as the above links are no longer accessible

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 creator and donator: Zhi Liu, e-mail: liuzhi8673 '@' gmail.com, institution: National Engineering Research Center for E-Learning, Hubei Wuhan, China

Data Set Information:

dataset are derived from the customers reviews in Amazon Commerce Website for authorship identification. Most previous studies conducted the identification experiments for two to ten authors. But in the online context, reviews to be identified usually have more potential authors, and normally classification algorithms are not adapted to large number of target classes. To examine the robustness of classification algorithms, we identified 50 of the most active users (represented by a unique ID and username) who frequently posted reviews in these newsgroups. The number of reviews we collected for each author is 30.

Attribute Information:

attribution includes authors' linguistic style such as usage of digit, punctuation, words and sentences' length and usage frequency of words and so on

Unlock powerful insights with the Amazon Prime dataset, offering access to millions of records from any Amazon domain. This dataset provides comprehensive data points such as product titles, descriptions, exclusive Prime discounts, brand details, pricing (initial and discounted), availability, customer ratings, reviews, and product categories. Additionally, it includes unique identifiers like ASINs, images, and seller information, allowing you to analyze Prime offerings, trends, and customer preferences with precision. Use this dataset to optimize your eCommerce strategies by analyzing Prime-exclusive pricing strategies, identifying top-performing brands and products, and tracking customer sentiment through reviews and ratings. Gain valuable insights into consumer demand, seasonal trends, and the impact of Prime discounts to make data-driven decisions that enhance your inventory management, marketing campaigns, and pricing strategies. Whether you’re a retailer, marketer, data analyst, or researcher, the Amazon Prime dataset empowers you with the data needed to stay competitive in the dynamic eCommerce landscape. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, this dataset ensures seamless integration into your workflows.

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

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

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How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

• marketplace: 2 letter country code of the marketplace where the review was written.
• customer_id: Random identifier that can be used to aggregate reviews written by a single author.
• review_id: The unique ID of the review.
• product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
• product_parent: Random identifier that can be used to aggregate reviews for the same product.
• product_title: Title of the product.
• product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
• star_rating: The 1-5 star rating of the review.
• helpful_votes: Number of helpful votes.
• total_votes: Number of total votes the review received.
• vine: Review was written as part of the Vine program.
• verified_purchase: The review is on a verified purchase.
• review_headline: The title of the review.
• review_body: The review text.
• review_date: The date the review was written.

Dataset

This dataset was created by Joy Chakraborty

Released under Database: Open Database, Contents: Database Contents

Contents

It contains the following files:

A multidisciplinary repository of public data sets such as the Human Genome and US Census data that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. If you have a public domain or non-proprietary data set that you think is useful and interesting to the AWS community, please submit a request and the AWS team will review your submission and get back to you. Typically the data sets in the repository are between 1 GB to 1 TB in size (based on the Amazon EBS volume limit), but they can work with you to host larger data sets as well. You must have the right to make the data freely available.

DES is publishing the Amazon spend for state agencies collected through the Washington State Amazon Business account. The data set only includes closed orders. Any orders that are still in process or have been cancelled are not included. This data is for Fiscal Year 20 (July 1, 2019 to June 30, 2020)

Unlock powerful insights with the Amazon Electronics dataset, offering access to millions of records from any Amazon domain. This dataset provides comprehensive data points such as product titles, descriptions, brand details, pricing (initial and discounted), availability, customer ratings, reviews, and product categories. Additionally, it includes unique identifiers like ASINs, images, and seller information, allowing you to analyze product listings, trends, and customer preferences with precision. Use this dataset to optimize your eCommerce strategies by benchmarking competitor pricing, identifying top-performing brands, and tracking customer sentiment through reviews and ratings. Gain valuable insights into consumer demand, seasonal trends, and market gaps to make data-driven decisions that enhance your inventory management, marketing campaigns, and pricing strategies. Whether you’re a retailer, marketer, data analyst, or researcher, the Amazon Electronics dataset empowers you with the data needed to stay competitive in the dynamic eCommerce landscape. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, this dataset ensures seamless integration into your workflows.

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,...

Total-Cash-From-Operating-Activities Time Series for Amazon.com Inc. Amazon.com, Inc. engages in the retail sale of consumer products, advertising, and subscriptions service through online and physical stores in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It also manufactures and sells electronic devices, including Kindle, fire tablets, fire TVs, echo, ring, blink, and eero; and develops and produces media content. In addition, the company offers programs that enable sellers to sell their products in its stores; and programs that allow authors, independent publishers, musicians, filmmakers, Twitch streamers, skill and app developers, and others to publish and sell content. Further, it provides compute, storage, database, analytics, machine learning, and other services, as well as advertising services through programs, such as sponsored ads, display, and video advertising. Additionally, the company offers Amazon Prime, a membership program. The company's products offered through its stores include merchandise and content purchased for resale and products offered by third-party sellers. It also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

Buy Amazon datasets and get access to over 300 million records from any Amazon domain. Get insights on Amazon products, sellers, and reviews.

Buy Amazon Fashion datasets and get access to millions of records from any Amazon domain. Gain insights on fashion products, sellers, and customer reviews.

Operating-Income Time Series for Amazon.com Inc. Amazon.com, Inc. engages in the retail sale of consumer products, advertising, and subscriptions service through online and physical stores in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It also manufactures and sells electronic devices, including Kindle, fire tablets, fire TVs, echo, ring, blink, and eero; and develops and produces media content. In addition, the company offers programs that enable sellers to sell their products in its stores; and programs that allow authors, independent publishers, musicians, filmmakers, Twitch streamers, skill and app developers, and others to publish and sell content. Further, it provides compute, storage, database, analytics, machine learning, and other services, as well as advertising services through programs, such as sponsored ads, display, and video advertising. Additionally, the company offers Amazon Prime, a membership program. The company's products offered through its stores include merchandise and content purchased for resale and products offered by third-party sellers. It also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

This dataset, 'Amazon Stock Data and Key Affiliated Companies,' provides comprehensive daily stock data for Amazon (AMZN) and several companies that have significantly contributed to Amazon's business growth and success. The dataset includes historical data for key players such as Intel (INTC), FedEx (FDX), United Parcel Service (UPS), Salesforce (CRM), NVIDIA (NVDA), Visa (V), and Mastercard (MA).

The stock data spans over various years, capturing important trading metrics like open, close, high, low, and volume. Amazon, a global leader in e-commerce, cloud computing, and AI, has thrived with the support of these affiliated companies. From Intel's processors powering Amazon Web Services (AWS) to Salesforce's CRM solutions used by Amazon, and the logistics support provided by FedEx and UPS, each company plays a critical role.

This dataset can be used for financial analysis, stock market prediction models, correlation studies between Amazon and its key partners, or any other research involving the financial performance of these major corporations. Whether you're interested in understanding Amazon's stock trends or the interdependency of companies in its ecosystem, this dataset provides valuable insights.

This dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for learning how to train fastText for sentiment analysis.

PromptCloud extracted 41 thousand reviews of unlocked mobile phones sold on Amazon.co.uk to find out insights with respect to reviews, ratings, price and their relationships