Comprehensive dataset tracking Amazon Prime member spending patterns from 2019-2024, including comparison with non-Prime customers and demographic breakdowns

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

This dataset contains longitudinal purchases data from 5027 Amazon.com users in the US, spanning 2018 through 2022: amazon-purchases.csv It also includes demographic data and other consumer level variables for each user with data in the dataset. These consumer level variables were collected through an online survey and are included in survey.csv fields.csv describes the columns in the survey.csv file, where fields/survey columns correspond to survey questions. The dataset also contains the survey instrument used to collect the data. More details about the survey questions and possible responses, and the format in which they were presented can be found by viewing the survey instrument. A 'Survey ResponseID' column is present in both the amazon-purchases.csv and survey.csv files. It links a user's survey responses to their Amazon.com purchases. The 'Survey ResponseID' was randomly generated at the time of data collection. amazon-purchases.csv Each row in this file corresponds to an Amazon order. Each such row has the following columns: Survey ResponseID Order date Shipping address state Purchase price per unit Quantity ASIN/ISBN (Product Code) Title Category The data were exported by the Amazon users from Amazon.com and shared by users with their informed consent. PII and other information not listed above were stripped from the data. This processing occurred on users' machines before sharing with researchers.

Unlock a wealth of business insights with our expansive dataset, meticulously tailored for both Amazon and non-Amazon sellers. Boasting over 1 million contacts, this comprehensive resource is characterized by unparalleled verification precision, ensuring the inclusion of verified emails and direct dials for decision-makers across the spectrum.

Unique Features: - Unrivaled Scale: 1M+ Contacts: A vast reservoir of contacts, offering a rich tapestry of data for comprehensive analysis. - Verification Precision: Rigorous validation processes guarantee accurate and up-to-date information, with a focus on verified emails and direct dials.

Data Sourcing: - Multi-Faceted Approach: We employ an advanced methodology, combining cutting-edge web scraping techniques, access to public records, and strategic partnerships with trusted data providers. This multi-faceted approach ensures a robust and diverse dataset. - Reliability Assurance: Regular updates and continuous monitoring practices are in place to maintain the highest standards of data quality, providing users with a dependable foundation for their strategic initiatives.

Primary Use-Cases: - Market Research: Gain deep insights into market trends, customer behavior, and competitive landscapes. - Lead Generation: Target decision-makers with precision, enhancing conversion rates. - Marketing Campaigns: Craft tailored strategies based on comprehensive data, ensuring maximum impact. - Competitive Analysis: Evaluate market positioning and identify strategic opportunities through detailed competitor insights.

Integration with Broader Offering: - Diverse Data Portfolio: Seamlessly integrates into our comprehensive data catalog, enhancing our commitment to providing a diverse, accurate, and scalable range of datasets. - Complementary Advantages: This dataset synergizes with our broader offering, providing users with a holistic solution for their data needs.

Coverage: - Global Reach: Encompassing multiple industries and countries, our dataset offers a global perspective for businesses seeking to expand their reach and explore new markets. - Strategic Expansion: Equip your business with the tools needed to navigate global markets confidently, with insights tailored to your expansion strategies.

Scale and Quality Indicators: - Superior Data Quality: Rigorous validation processes ensure the highest standards of precision and reliability. - Scalability: Adaptable to diverse business needs, accommodating various use cases and scenarios.

Target Audience: - E-commerce Players: Elevate your market presence and competitiveness in the dynamic e-commerce landscape. - Marketing Agencies: Craft targeted campaigns with confidence, backed by comprehensive and reliable data. - Business Intelligence Professionals: Gain deep market insights to inform strategic planning and decision-making.

Unveiling Opportunities: - Catalyst for Growth: Discover new markets and unearth business prospects. - Competitive Edge: Outpace competition by utilizing insights from our curated dataset.

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.

Operating-Expenses 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 serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

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.

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.

Recommender systems based on matrix factorization act as black-box models and are unable to explain the recommended items. After adding the neighborhood algorithm, the explainability is measured by the user's neighborhood recommendation, but the subjective explicit preference of the target user is ignored. To better combine the latent factors from matrix factorization and the target user's explicit preferences, an explainable recommender system based on reconstructed explanatory factors and multi-modal matrix factorization (ERS-REFMMF) is proposed. ERS-REFMMF is a two-layer model, and the underlying model decomposes the multi-modal scoring matrix to get the rich latent features of the user and the item based on the method of Funk-SVD, in which the multi-modal scoring matrix consists of the original matrix and the preference features and sentiment scores exhibited by users in the reviews corresponding to the ratings. The set of candidate items is obtained based on the latent features, and the explainability is reconstructed based on the subjective preference of the target user and the real recognition level of the neighbors. The upper layer is the multi-objective high-performance recommendation stage, in which the candidate set is optimized by a multi-objective evolutionary algorithm to bring the user a final recommendation list that is accurate, recallable, diverse, and interpretable, in which the accuracy and recall are represented by F1-measure. Experimental results on three real datasets from Amazon show that the proposed model is competitive compared to existing recommendation methods in both stages.

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)

Short-Term-Investments 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 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 provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

SUMMARY:

Vumonic provides its clients email receipt datasets on weekly, monthly, or quarterly subscriptions, for any online consumer vertical. We gain consent-based access to our users' email inboxes through our own proprietary apps, from which we gather and extract all the email receipts and put them into a structured format for consumption of our clients. We currently have over 1M users in our India panel.

If you are not familiar with email receipt data, it provides item and user-level transaction information (all PII-wiped), which allows for deep granular analysis of things like marketshare, growth, competitive intelligence, and more.

VERTICALS:

• Ecommerce (Amazon, Flipkart, Myntra, Nykaa)
• Taxi (Uber, Ola)
• Food Delivery (Swiggy, Zomato)
• OTT (Netflix, Amazon Prime Video, Disney+)
• Appstore (Apple App Store and Google Playstore)
• OTA (Expedia, Booking.com, GoIbibo)
• E-wallets (PhonePe, PayTM)
• Education (Byju's, Unacademy)

PRICING/QUOTE:

Our email receipt data is priced market-rate based on the requirement. To give a quote, all we need to know is:

• what vertical you are interested in
• how often do you wish to receive the data, and
• do you want any backdata (e.g. from 2019 onwards)

Send us over this info and we can answer any questions you have, provide sample, and more.

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

We introduce PDMX: a Public Domain MusicXML dataset for symbolic music processing, including over 250k musical scores in MusicXML format. PDMX is the largest publicly available, copyright-free MusicXML dataset in existence. PDMX includes genre, tag, description, and popularity metadata for every file.

These Census Tract-level datasets described here provide de-identified diagnosis data for customers of three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) that have filed a claim for anxiety medications in 2015 and 2016. The data also includes the number of enrolled members in the three participating managed care organizations in 2015 and 2016.

Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

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 dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

This data set provides de-identified population data for diabetes and hyperlipidemia comorbidity prevalence. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and 2016 calendar years.

Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This repository includes HRLDAS Noah-MP model output generated as part of Bieri et al. (2025) - Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): Impact on Amazon dry-season transpiration.

These data are distributed in two different formats: Raw model output files and subsetted files that include data for a specific variable. All files are .nc format (NetCDF) and aggregated into .tar files to facilitate download. Given the size of these datasets, Globus transfer is the best way to download them.

Raw model output for four model experiments is available: FD (control), GW, SOIL, and ROOT. See the associated publication for information on the different experiments. These data span an approximately 20 year period from 01 Jun 2000 to 31 Dec 2019. The data have a spatial resolution of 4 km and a temporal frequency of 3 hours. These data are for a domain in the southern Amazon basin (see Figure 1 in the associated publication). Data for each experiment is available as a .tar file which includes 3-hourly NetCDF files. All default Noah-MP output variables are included in each file. As a result, the .tar files are quite large and may take many hours or even days to transfer depending on your network speed and local configurations. These files are named 'noahmp_output_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT).

Subsetted model output at a daily temporal resolution for all four model experiments is also available. These .tar files include the following variables: water table depth (ZWT), latent heat flux (LH), sensible heat flux (HFX), soil moisture (SOIL_M), canopy evaporation (ECAN), ground evaporation (EDIR), transpiration (ETRAN), rainfall rate at the surface (QRAIN), and two variables that are specific to the ROOT experiment: ROOTACTIVITY (root activity function) and GWRD (active root water uptake depth). There is one file for each variable within the tarred files. These files are named 'noahmp_output_subset_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT).

Finally, there is a sample dataset with raw 3-hourly output from the ROOT experiment for one day. The purpose of this sample dataset is to allow users to confirm if these data meet their needs before initiating a full transfer via Globus. This file is named 'noahmp_output_sample_ROOT.tar'.

The README.txt file provides information on the Noah-MP output variables in these datasets, among other specifications.

Information on HRLDAS Noah-MP and names/definitions of model output variables that are useful in working with these data are available here: http://dx.doi.org/10.5065/ew8g-yr95. Note that some output variables may be listed in this document under a different variable name, so searching for the long name (e.g. 'baseflow' instead of 'QRF') is recommended. Information on additional output variables that were added to the model as part of this study is available here: https://github.com/bieri2/bieri-et-al-2025-EGU-GMD/tree/DynaRoot. Model code, configuration files, and forcing data used to carry out the model simulations are linked in the related resources section.