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.

Dataset

This dataset was created by Gautam Kumar

Contents

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.

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 Product Reviews

18 Years of Customer Ratings and Experiences

By Huggingface Hub [source]

About this dataset

The Amazon Reviews Polarity Dataset discloses eighteen years of customers' ratings and reviews from Amazon.com, offering an unparalleled trove of insight and knowledge. Drawing from the immense pool of over 35 million customer reviews, this dataset presents a broad spectrum of customer opinions on products they have bought or used. This invaluable data is a gold mine for improving products and services as it contains comprehensive information regarding customers' experiences with a product including ratings, titles, and plaintext content. At the same time, this dataset contains both customer-specific data along with product information which encourages deep analytics that could lead to great advances in providing tailored solutions for customers. Has your product been favored by the majority? Are there any aspects that need extra care? Use Amazon Reviews Polarity to gain deeper insights into what your customers want - explore now!

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

• Analyze customer ratings to identify trends: Take a look at how many customers have rated the same product or service with the same score (e.g., 4 stars). You can use this information to identify what customers like or don’t like about it by examining common sentiment throughout the reviews. Identifying these patterns can help you make decisions on which features of your products or services to emphasize in order to boost sales and satisfaction rates.

2 Review content analysis: Analyzing review content is one of the best ways to gauge customer sentiment toward specific features or aspects of a product/service. Using natural language processing tools such as Word2Vec, Latent Dirichlet Allocation (LDA), or even simple keyword search algorithms can quickly reveal general topics that are discussed in relation to your product/service across multiple reviews - allowing you quickly pinpoint areas that may need improvement for particular items within your lines of business.

3 Track associated scores over time: By tracking customer ratings overtime, you may be able to better understand when there has been an issue with something specific related to your product/service - such as negative response toward a feature that was introduced but didn’t seem popular among customers and was removed shortly after introduction.. This can save time and money by identifying issues before they become widespread concerns with larger sets of consumers who invest their money in using your company's item(s).

4 Visualize sentiment data over time graphs : Utilizing visualizations such as bar graphs can help identify trends across different categories quicker than raw numbers alone; combining both numeric values along with color differences associated between different scores allows you spot anomalies easier - allowing faster resolution times when trying figure out why certain spikes occurred where other stayed stable (or vice-versa) when comparing similar data points through time-series based visualization models

Research Ideas

• Developing a customer sentiment analysis system that can be used to quickly analyze the sentiment of reviews and identify any potential areas of improvement.
• Building a product recommendation service that takes into account the ratings and reviews of customers when recommending similar products they may be interested in purchasing.
• Training a machine learning model to accurately predict customers’ ratings on new products they have not yet tried and leverage this for further product development optimization initiatives

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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: train.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------| | label | The sentiment of the review, either positive or negative. (String) | | title | The title of the review. (String) ...

Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

Context

Amazon has become a house hold name now and has been around for quite sometime. It comes under the popular FAANG companies and a dream company for many. Today almost anything that you need today is available on Amazon. From groceries to electronics. But it has not only benefited the people purchasing from them. It has benefited those too who invested in the company back then and continue to do till today.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 1997 to 2020(August).

The Amazon Food Products Dataset is a large-scale collection of product listings, reviews, and metadata sourced from Amazon. This dataset is valuable for understanding consumer behaviour, analyzing product trends, and training machine learning models for recommendation systems and sentiment analysis. It includes various categories, providing insights into customer preferences, product ratings, and review sentiments.

Dataset Features

Each record in the dataset contains the following key fields:

• ProductId: Unique identifier for each product.
• UserId: Unique identifier for the reviewer.
• ProfileName: Display the name of the reviewer.
• HelpfulnessNumerator: Number of users who found the review helpful.
• HelpfulnessDenominator: Total number of users who rated the review’s helpfulness.
• Score: Product rating (1 to 5 stars).
• Time: Unix timestamp of the review.
• Summary: Short summary of the review.
• Text: Full text of the review.

Distribution

• Data Volume: 568454 rows and 9 columns.
• Format: CSV.
• Structure: Tabular format with numerical, categorical, and text data.

Usage

This dataset is ideal for a variety of applications:

• Sentiment Analysis: Training NLP models to predict sentiment based on reviews.
• Product Recommendation Systems: Building collaborative filtering models.
• Trend Analysis: Identifying popular products and customer preferences.
• Fake Review Detection: Detecting anomalous patterns in review behaviours.

Coverage

• Geographic Coverage: Global.
• Time Range: Multi-year dataset (over 10 years of reviews).
• Demographics: General Amazon shoppers; includes various age groups and customer segments.

License

CC0

Who Can Use It

• Data Scientists: For building machine learning models.
• Researchers: For academic analysis of customer behaviour.
• Businesses: For market insights and customer sentiment analysis.

Dataset

This dataset was created by pothula swaraj

Contents

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.

Dataset

This dataset was created by Yash Joshi

Released under Apache 2.0

Contents

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)

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.

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.

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.

📃 Dataset Summary

This dataset is a benchmark for query-product retrieval, designed to evaluate the performance of algorithms in retrieving relevant products based on user search queries. It is built from two Hugging Face datasets:

Amazon-C4 Amazon Reviews 2023

The dataset focuses on enhancing the retrieval of products by using structured product information (title, description, category) to generate search queries and assess relevance. The dataset contains two main parts:… See the full description on the dataset page: https://huggingface.co/datasets/apexlearningcurve/Amazon-Search-Benchmark.

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.

Analysis of ‘Amazon Product Reviews Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/amazon-product-reviews-datasete on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains 30K records of product reviews from amazon.com.

This dataset was created by PromptCloud and DataStock

Content

This dataset contains the following:

• Total Records Count: 43729

• Domain Name: amazon.com

• Date Range: 01st Jan 2020 - 31st Mar 2020

• File Extension: CSV

• Available Fields:
  -- Uniq Id,
  -- Crawl Timestamp,
  -- Billing Uniq Id,
  -- Rating,
  -- Review Title,
  -- Review Rating,
  -- Review Date,
  -- User Id,
  -- Brand,
  -- Category,
  -- Sub Category,
  -- Product Description,
  -- Asin,
  -- Url,
  -- Review Content,
  -- Verified Purchase,
  -- Helpful Review Count,
  -- Manufacturer Response

Acknowledgements

We wouldn't be here without the help of our in house teams at PromptCloud and DataStock. Who has put their heart and soul into this project like all other projects? We want to provide the best quality data and we will continue to do so.

Inspiration

The inspiration for these datasets came from research. Reviews are something that is important wit everybody across the globe. So we decided to come up with this dataset that shows us exactly how the user reviews help companies to better their products.

This dataset was created by PromptCloud and contains around 0 samples along with Billing Uniq Id, Verified Purchase, technical information and other features such as: - Crawl Timestamp - Manufacturer Response - and more.

How to use this dataset

• Analyze Helpful Review Count in relation to Sub Category
• Study the influence of Review Date on Product Description
• More datasets

Acknowledgements

If you use this dataset in your research, please credit PromptCloud

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

2. Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.

3. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can obtain relevant ecommerce data swiftly and consistently.

4. Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.

5. Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.

Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.

To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

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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

About this Dataset

You will get an active email list for real and active buyers who make regular purchases through Amazon and other e-commerce sites. This email list contains 100% original email address. You can also use these emails to increase visits to your website, blog, or YouTube channel. I offer you now, a great treasure to use whenever you want.

So don't waste your time and start boosting your ecommerce business online.

The buyers will be from:

United States of America Canada Europe Union

$ There are no duplicate emails $ No fake IDs $ Audiences ready to buy

Category

Markets

Keywords

market,emails,email ma,list,buyer

Row Count

70150

Price

$90.00