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

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

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

Dataset Features

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

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

Dataset Card for Amazon Products 2023 Arabic

  Dataset Summary

This dataset contains product metadata from Amazon, filtered to include only products that became available in 2023. The dataset is intended for use in semantic search applications and includes a variety of product categories.

Number of Rows: 117,243 Number of Columns: 17

  Data Source

The data is sourced from Amazon Reviews 2023. It includes product information across multiple categories, with… See the full description on the dataset page: https://huggingface.co/datasets/milistu/AMAZON-Products-2023-Arabic.

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.

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).

First of all, Amazon product datasets are indispensable for reverse engineering your rivals. For example, you can collect a list of keywords you already rank for or want to, and go through DataForSEO Amazon Products Database to find other sellers appearing as the top results for these terms.

Next, you can narrow down the scope of your contenders to those performing the best. To do so, you can filter out sellers who won the “Amazon’s Choice” and those whose products got listed multiple times on the first page.

Once you’ve compiled the final list of your challengers, Amazon Products Database will help you to quickly examine product titles, descriptions, prices, images, and other details that will let you grasp the main contributors to your competitors’ success. Once you’ve figured that out, you can start optimizing your product listings and pricing strategies to increase conversions.

However, the number of use cases for Amazon product data isn’t limited to competitor analysis. It can be applied to monitoring product rankings, running price comparisons, and more.

Gain access to a structured dataset featuring thousands of products listed on Amazon India. This dataset is ideal for e-commerce analytics, competitor research, pricing strategies, and market trend analysis.

Dataset Features:

• Product Details: Name, Brand, Category, and Unique ID

• Pricing Information: Current Price, Discounted Price, and Currency

• Availability & Ratings: Stock Status, Customer Ratings, and Reviews

• Seller Information: Seller Name and Fulfillment Details

• Additional Attributes: Product Description, Specifications, and Images

Dataset Specifications:

• Format: CSV

• Number of Records: 50,000+

• Delivery Time: 3 Days

• Price: $149.00

• Availability: Immediate

This dataset provides structured and actionable insights to support e-commerce businesses, pricing strategies, and product optimization. If you're looking for more datasets for e-commerce analysis, explore our E-commerce datasets for a broader selection.

Gain insights into Amazon’s beauty and personal care market with this comprehensive Amazon Beauty Products Dataset. Covering 47,000 records across skincare, haircare, and makeup, this dataset provides full ingredient lists, product descriptions, pricing, and availability. Ideal for researchers and businesses focused on ingredient transparency, beauty trend analysis, and competitive market insights. Perfect for applications in ingredient research, product development, and e-commerce analysis.

Access a rich Amazon Beauty & Cosmetics dataset with over 200,000+ product records, including detailed ingredients.
Explore more on our Beauty & Cosmetics Data page or view the full Amazon Beauty Dataset

Walmart product dataset featuring detailed ingredient information across categories like beauty, food, personal care, and more.
View Dataset →

The dataset includes the following fields:

• ASIN: Unique Amazon product identifier.
• Product Name and Description: Full titles and descriptions of each product.
• Price and Availability: Current pricing and stock status.
• Categories: Product type classification (e.g., skincare, haircare, makeup).
• Ingredients: Complete ingredient lists, ensuring transparency about product composition.
• Images: High-quality product images.
• Brand and Manufacturer Information: Details of the brand and manufacturer.
• Customer Ratings and Reviews: User-generated content for understanding product popularity and performance.

This dataset is invaluable for:

• Ingredient Analysis: Understanding popular ingredients in beauty products.
• Market Research: Analyzing trends in beauty products, such as ingredient types and product categories.
• Competitive Analysis: Assessing product offerings by brand, price, and ingredients.

Whether you’re focused on skincare, haircare, makeup, or other beauty categories, this dataset provides in-depth information for deep analysis. For any custom requirements or additional data needs, please feel free to reach out.

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

• 🚨 Your notebook can be here! 🚨!

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) ...

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.

Amazon Sales Report

Overview:

This dataset provides detailed sales data from Amazon, offering a comprehensive look at various product categories and their performance over time. It includes information on sales figures, order details, product categories, and customer demographics.

Features:

1. Order ID

Description: A unique identifier for each order placed on Amazon. This field helps to track individual orders and link related records.

2. Dates

Description: The date when the order was placed. This field is crucial for analyzing sales trends over time and identifying seasonal patterns.

3. Status

Description: The current status of the order (e.g., Shipped, Delivered, Pending). This field provides insight into the order fulfillment process and helps monitor order processing efficiency.

4. Fulfillment

Description: Indicates the method used to fulfill the order (e.g., Fulfilled by Amazon, Fulfilled by Seller). This feature helps in analyzing the performance of different fulfillment methods and their impact on customer satisfaction.

5. Sales Channel

Description: The channel through which the sale was made (e.g., Amazon Website, Mobile App). This field is useful for evaluating the effectiveness of different sales channels and understanding customer preferences.

6. Category

Description: The product category to which the purchased item belongs (e.g., Electronics, Clothing, Home Goods). This feature aids in analyzing sales performance across various product categories.

7. Ship Service Level

Description: The shipping service level selected for the order (e.g., Standard Shipping, Two-Day Shipping). This field helps to assess the impact of shipping options on delivery times and customer satisfaction.

8. Size

Description: The size of the product ordered (e.g., Small, Medium, Large). This feature is relevant for analyzing sales performance based on product size and understanding inventory requirements.

9. Carrier Status

Description: The status of the shipment with the carrier (e.g., In Transit, Delivered, Returned). This field provides insights into the shipping process and helps in monitoring delivery performance and handling returns.

Use Cases:

Sales Analysis:

Examine trends in sales over time, identify peak periods, and analyze performance by product category.

Customer Insights:

Explore customer demographics to understand purchasing behavior and preferences.

Inventory Management:

Assess which products are performing well and which are not, aiding in inventory and supply chain management.

Marketing Strategies:

Develop targeted marketing campaigns based on sales trends and customer profiles.

Data Source:

This dataset is a simulated collection of Amazon sales data and is intended for educational and analytical purposes.

Acknowledgments:

This dataset was created to facilitate data analysis and machine learning projects. It is ideal for practicing data manipulation, statistical analysis, and predictive modeling.

Access a comprehensive dataset of over 240,000 shoe product listings directly from Amazon UK. This dataset is ideal for researchers, e-commerce analysts, and AI developers looking to explore pricing trends, brand performance, product features, or build training data for retail-focused models.

All data is neatly packaged in a downloadable ZIP archive containing files in JSON format, making it easy to integrate with your preferred analytics or database tools.

🔎 Use Cases:

• Price and discount trend analysis

• Competitor benchmarking

• Product attribute extraction and modeling

• AI/ML training datasets (e.g., shoe recommendation systems)

• Retail assortment planning

🔄 Updates & Delivery:

This dataset is available as a static snapshot, but you can request weekly or monthly updates through the Crawl Feeds dashboard. Upon purchase, the data will be bundled and delivered via a direct download link.

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

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

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

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

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

Data Points Available:

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

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

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

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.

Dataset

This dataset was created by Ronaldo Nyamari

Contents

Amazon Product Description Dataset

This dataset is a cleaned version of Amazon Product Data. Cleaned by team at https://exnrt.com

421K Unique Examples Empty description rows are being removed. Description Smaller then 200 characters are removed Convert to Proper Format Remove non-ASCII characters from both column And few more techniques

  Original Dataset

This original dataset has 10 Million Examples. Original, Un-cleaned DataSet:… See the full description on the dataset page: https://huggingface.co/datasets/Ateeqq/Amazon-Product-Description.

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.

Unlock detailed insights with our Amazon UK Shoes Products Reviews Dataset, an invaluable resource for businesses, researchers, and data analysts. This dataset features comprehensive information, including product names, review texts, star ratings, and customer feedback for a wide range of shoe products available on Amazon UK.

Key Features:

• Extensive Coverage: Includes detailed reviews and ratings for various shoe products, helping you analyze customer preferences and trends.
  
  
• Structured Data: Available in easily accessible formats like product review dataset CSV, making it perfect for integration into your analytical workflows.
  
  
• Actionable Insights: Leverage this dataset for customer sentiment analysis, product optimization, and competitive benchmarking.

Why Choose the Amazon UK Shoes Products Reviews Dataset?

Whether you're delving into customer behavior, conducting market research, or improving product offerings, this dataset empowers you to make informed decisions. By working with a dataset enriched with real-world feedback, you can:

• Understand customer preferences: Dive into detailed reviews to uncover patterns in consumer likes and dislikes.
  
  
• Enhance product offerings: Identify gaps and opportunities in the market to better meet customer demands.
  
  
• Boost competitive analysis: Compare customer feedback across different brands and products.

Additional Datasets Available

Explore related datasets like the Amazon product review dataset, offering insights across various categories and regions. For specific needs, our curated product reviews dataset is tailored to help you gain a granular understanding of niche markets.

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.

Explore our extensive Amazon Product Dataset, featuring detailed information on prices, ratings, sales volume, and more.