In the United States, consumers from Generation X are more likely to use Amazon Prime services, a survey from 2024 revealed. Nearly ** percent of them were Amazon Prime subscribers, while boomers followed, as about ** percent reported to be Prime members.

The number of internet users in the United States who are Amazon Prime members is expected to grow in the upcoming years. By 2024, the number of Amazon Prime members in the United States is projected to reach more than *** million users, up from ***** million in 2022. Free shipping drives membership The primary draw for Amazon Prime members remains the free shipping advantage, with nearly ** percent of surveyed shoppers citing this as their main reason for using the service in 2024. The second most popular benefit is access to Amazon Prime Video, attracting about ** percent of users. These perks help explain the steady increase in membership numbers, as consumers find value in both the shopping and entertainment aspects of the service. Prime Day or desillusion day Amazon Prime Day, the company's annual shopping event, has become a significant driver of sales and member engagement. However, the latest edition met the expectations of roughly one-third of U.S. shoppers, with ** percent of them being disappointed with the available offerings.

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

Introduction

Amazon Music Statistics: Amazon Music is a popular music streaming platform, offering a variety of services, including Amazon Music Unlimited, Amazon Prime Music, and Amazon Music HD. As of 2023, Amazon Music boasts over 80 million songs in its catalog, providing a wide range of music options across genres. The service is available in more than 50 countries and is integrated with Amazon's smart devices, like Echo and Fire TV. Amazon Music Unlimited, the premium version of the service, offers access to an even larger selection of over 90 million songs. The platform also supports high-definition audio for subscribers of Amazon Music HD, with tracks available in lossless, CD-quality audio.

Amazon Music has seen steady growth, with recent reports suggesting that it has gained a significant share of the global streaming market, though it still trails behind competitors like Spotify and Apple Music. Additionally, Amazon Music offers personalized playlists and radio stations, enhancing the user experience through tailored recommendations. This article will discuss the important Amazon Music statistics and key trends.

Amazon Prime’s growth is what has been most impressive. They have managed to convert millions of customers into loyal subscribers at a very fast rate.

Introduction

Amazon Statistics: Amazon began as an online bookstore but has grown into the biggest online retailer worldwide, changing how people shop and do business online. Amazon now serves about 310 million customers globally and has more than 2 million active sellers on its site. In the 25 years since it was launched, Amazon has become the biggest online retailer in the world and a well-known name. Amazon is now another word for online shopping. It keeps growing by making new products, buying other companies, and offering different services to attract more customers.

Amazon aims to reach as many people as possible, and it’s doing a great job because there's something for everyone on its site. As online shopping becomes more popular, people turn to Amazon for almost everything, from everyday groceries to seasonal gifts. We shall shed more light on Amazon statistics through this article.

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 Overview

This dataset contains over 4,900 customer reviews from Amazon, including text-based feedback, star ratings, and helpfulness votes.

It can be used for:

• 📊 Sentiment Analysis
• 🧠 Text Classification (Positive/Negative)
• 🔍 Review Score Prediction (based on reviewText)
• 🤖 Building Recommendation Systems
• 🧮 Helpfulness Scoring Models

📌 Key Columns

• reviewText: Full written review
• overall: Star rating (1 to 5)
• summary: Short summary of the review
• helpful_yes: Number of users who found the review helpful
• total_vote: Total votes on helpfulness
• day_diff: Days since the review was written

This dataset is suitable for natural language processing (NLP) and supervised learning tasks.

📎 Note

This is a publicly available dataset for educational and research use.

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.

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.

Do you want to learn more about your brand's audience? Or perhaps you competitors?

With our Amazon User Profile API, you can get a list of verified past purchases of any user on Amazon with their profile URL. Our Online Reviews API can be used to extract customer reviews for any product on Amazon, including the author's profile URL. These URLs can in turn be used to retrieve some of the past purchases of these reviewers and can give you a clearer picture of the preferences of your target audience.

This data can be used to track customer loyalty and target audience for any brand that distributes using internet retail.

In 2024, Amazon Prime Day sales reached a massive $14.2 billion in comparison to $12.9 billion in 2023. This was a huge $1.3 billion increase.

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plaintext review. Number of reviews -> 568,454 Number of users -> 256,059 Number of products -> 74,258

Citation - J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews WWW, 2013.

According to the latest Amazon Prime statistics, about 81% of US internet users aged 18 to 34 have a paid Amazon Prime membership.

Amazon Prime’s global subscriber growth rate has accelerated over the last 5 years. Today Amazon currently has 200 million Amazon Prime members around the world.

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.

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

Over the first quarter 2024, Amazon counted approximately **** million unique users in Italy. The average number of unique users has increased since 2019, when the marketplace had been visited by **** million users.

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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('amazon_us_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

I roundup the latest Amazon Prime statistics which show just how big Amazon Prime has become and will continue to be.