Among selected consumer electronics retailers worldwide, apple.com recorded the highest bounce rate in April 2024, at approximately **** percent. Rival samsung.com had a slightly lower bounce rate of nearly ** percent. Among selected consumer electronics e-tailers, huawei.com had the lowest bounce rate at ***** percent. Bounce rate is a marketing term used in web traffic analysis reflecting the percentage of visitors who enter the site and then leave without taking any further action like making a purchase or viewing other pages within the website ("bounce"). A sector with growth potential With one of the lowest online shopping cart abandonment rates globally in 2022, consumer electronics is a burgeoning e-commerce segment that places itself at the crossroads between technological progress and digital transformation. Boosted by the pandemic-induced surge in online shopping, the global market size of consumer electronics e-commerce was estimated at more than *** billion U.S. dollars in 2021 and forecast to nearly double less than five years later. Amazon and Apple lead the charts in electronics e-commerce With more than ** billion U.S. dollars in e-commerce net sales in the consumer electronics segment in 2022, apple.com was the uncontested industry leader. The global powerhouse surpassed e-commerce giants amazon.com and jd.com with more than *** billion U.S. dollars difference in online sales in the consumer electronics category.

Among the top 100 referring websites to Amazon in the United States, the majority of the traffic directed to Amazon came from news and media websites during Amazon Prime Days in 2019 and 2020. While directed traffic from both news and media websites and other sites stayed the same across the last two Amazon Prime Day events, share of traffic coming to Amazon from coupon and rebate websites saw an increase in October 2020 and reached to 16 percent, marking an increase of four percent from July 2019.

In December 2023, Amazon.com was the leading online shopping website in the United States. During the measured period, the sprawling platform accounted for over 45 percent of desktop traffic in the e-commerce and shopping subcategory. In second place on the list was eBay.com, with 9.22 percent of visitors. Walmart ranked third with a bit less than six percent of web traffic. Why customers browse on Amazon The main reason behind the outstanding online traffic to Amazon is user behavior throughout the customer journey. Amazon serves as a search engine for U.S. consumers, with 73 percent browsing it for inspiration and product discovery. Another 65 percent of U.S. shoppers landed on Amazon to look for products and compare products. In turn, Google is left third in the ranking of most used platforms. Generational differences In the beauty segment, the customer journey is more likely to start on Amazon among senior consumers. In the United States, 44 percent of Baby Boomers started their search of beauty products on the marketplace, while only 35 percent of Gen Z consumers reported doing the same.

In March 2024, Amazon.com had approximately 2.2 billion combined web visits, up from 2.1 billion visits in February. In the fourth quarter of 2024, Amazon’s net income amounted to approximately 20 billion U.S. dollars. Online retail in the United States Online retail in the United States is constantly growing. In the third quarter of 2023, e-commerce sales accounted for 15.6 percent of retail sales in the United States. During that quarter, U.S. retail e-commerce sales amounted to over 284 billion U.S. dollars. Amazon is the leading online store in the country, in terms of e-commerce net sales. Amazon.com generated around 130 billion U.S. dollars in online sales in 2022. Walmart ranked as the second-biggest online store, with revenues of 52 billion U.S. dollars. The king of Black Friday In 2023, Amazon ranked as U.S. shoppers' favorite place to go shopping during Black Friday, even surpassing in-store purchasing. Nearly six out of ten consumers chose Amazon as the number one place to go find the best Black Friday deals. Similar findings can be observed in the United Kingdom (UK), where Amazon is also ranked as the preferred Black Friday destination.

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.

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.

About This Data

This is a list of over 34,000 consumer reviews for Amazon products like the Kindle, Fire TV Stick, and more provided by Datafiniti's Product Database. The dataset includes basic product information, rating, review text, and more for each product.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to analyze Amazon’s most successful consumer electronics product launches; discover insights into consumer reviews and assist with machine learning models. E.g.:

• What are the most reviewed Amazon products?
• What are the initial and current number of customer reviews for each product?
• How do the reviews in the first 90 days after a product launch compare to the price of the product?
• How do the reviews in the first 90 days after a product launch compare to the days available for sale?
• Map the keywords in the review text against the review ratings to help train sentiment models.

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

You can access the full dataset by running the following query with Datafiniti’s Product API.

{ "query": "dateUpdated:[2017-09-01 TO *] AND brand:Amazon* AND categories:* AND primaryCategories:* AND name:* AND reviews:*", "format": "csv", "download": true }

**The total number of results may vary.*

Get this data and more by creating a free Datafiniti account or requesting a demo.

A complete list of live websites using the Login with Amazon technology, compiled through global website indexing conducted by WebTechSurvey.

About

From website:

Public Data Sets on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community, and like all AWS services, users pay only for the compute and storage they use for their own applications. An initial list of data sets is already available, and more will be added soon.

Previously, large data sets such as the mapping of the Human Genome and the US Census data required hours or days to locate, download, customize, and analyze. Now, 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. For example, users can produce or use prebuilt server images with tools and applications to analyze the data sets. By hosting this important and useful data with cost-efficient services such as Amazon EC2, AWS hopes to provide researchers across a variety of disciplines and industries with tools to enable more innovation, more quickly.

Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Lacking a 250m blue band, the EVI algorithm uses the 500m blue band to correct for residual atmospheric effects, with negligible spatial artifacts. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.

Discover a curated selection of Amazon's beauty and personal care products with insights derived from web-scraped data. Explore trending cosmetics, skincare essentials, haircare favorites, and wellness products tailored to enhance your self-care routine. Unveil popular brands, top-rated items, and hidden gems to inspire your beauty regimen.

Amazon data extracted from the following categories

• Makeup
• Skincare
• Haircare
• Fragance
• Foot and nail care
• Oral care

The real-time index database market is experiencing robust growth, driven by the increasing demand for real-time insights across diverse sectors. The market's expansion is fueled by the proliferation of data-intensive applications, particularly in finance, e-commerce, and IoT. Businesses are increasingly reliant on immediate data analysis for informed decision-making, optimized operations, and improved customer experiences. The surge in the adoption of cloud-based solutions and the growing sophistication of analytics tools are key factors contributing to the market's upward trajectory. Major players like Elastic, Amazon Web Services, and Splunk are leading the innovation, offering scalable and highly performant solutions to address the growing complexity and volume of real-time data. Competition is intense, with companies continuously striving to enhance their offerings with features such as advanced analytics capabilities, enhanced security, and improved integration with other enterprise systems. While the market presents significant opportunities, challenges remain. The complexities of managing and analyzing real-time data streams, along with the associated infrastructure costs, can present hurdles for adoption. Ensuring data security and compliance with industry regulations also poses considerable challenges for businesses. However, ongoing advancements in database technology, coupled with the decreasing cost of cloud computing resources, are mitigating these concerns and opening up new avenues for growth. The market is expected to witness continuous innovation, with the emergence of new technologies and approaches to further improve the efficiency and scalability of real-time index databases. This will drive the market toward greater adoption across various industries and contribute to its sustained expansion in the coming years. We estimate a market size of $15 billion in 2025, with a CAGR of 15% over the forecast period (2025-2033).

The global Big Data Analysis Software market is experiencing robust growth, driven by the increasing volume of data generated across various sectors and the rising need for extracting actionable insights. The market size in 2025 is estimated at $50 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This significant expansion is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, the emergence of advanced analytics techniques, such as machine learning and artificial intelligence, enhances the ability to derive meaningful predictions and improve decision-making. Industry verticals like banking, manufacturing, and government are leading the adoption, leveraging big data analytics for risk management, process optimization, and improved customer service. However, challenges such as data security concerns, the need for skilled professionals, and the complexity of integrating diverse data sources are acting as restraints. The market segmentation reveals strong growth in cloud-based solutions, reflecting the shift towards flexible and readily available software infrastructure. Significant regional variations exist, with North America and Europe currently holding the largest market shares, though Asia-Pacific is projected to witness accelerated growth due to increasing digitalization and technological advancements. The competitive landscape is characterized by a mix of established players like IBM, Google, and Amazon Web Services, alongside specialized software providers such as Qlucore and Atlas.ti. These companies are continuously innovating to provide comprehensive solutions that cater to the evolving needs of businesses. The future of the Big Data Analysis Software market hinges on advancements in data visualization, enhanced integration capabilities, and the development of user-friendly interfaces. The market is likely to see further consolidation as companies strive to offer end-to-end analytics solutions, including data ingestion, processing, analysis, and visualization. The continued focus on addressing data security and privacy concerns will also play a critical role in shaping the market trajectory. The forecast suggests that by 2033, the market will surpass $150 billion, showcasing the transformative potential of big data analytics across various sectors globally.

The Amazon Web Services (AWS) channel partner market is experiencing robust growth, driven by increasing cloud adoption across diverse sectors like large enterprises and SMEs. The market's expansion is fueled by the rising demand for specialized AWS services, including migration, consulting, and managed services. The diverse partner ecosystem, encompassing resellers, service providers, and agents, contributes significantly to AWS's market reach and penetration. While precise figures are unavailable, a logical estimation based on the prevalence of cloud services and the high market penetration of AWS suggests a substantial market size. Considering a conservative estimate for the base year (2025) market size of $50 billion USD, and a CAGR of 25% (reflecting the rapid growth of the cloud market and AWS’s dominance), we can project significant expansion in the coming years. This growth is particularly noticeable in regions like North America and Europe, which have robust IT infrastructure and early adoption rates of cloud technologies. However, growth in the Asia-Pacific region is also expected to accelerate due to increasing digitalization initiatives in key markets like China and India. The success of AWS channel partners depends heavily on their ability to deliver specialized expertise and value-added services to clients, enabling seamless cloud adoption and effective cost optimization. The market faces certain restraints, such as the complexity of AWS services requiring high levels of partner skill and the competitive landscape characterized by numerous other cloud providers. Successfully navigating this necessitates continuous investment in training, skill development, and strategic partnerships to maintain a competitive edge. The segmentation of the partner ecosystem into various types (reseller, service provider, agent) reflects the varied needs of different client segments. Large enterprises often seek comprehensive managed services, while SMEs may prioritize cost-effective solutions offered by resellers. The list of prominent players signifies the highly competitive nature of the market, with established players like Salesforce and emerging ones vying for market share. Future growth will hinge on partners adapting to evolving technologies, expanding their service offerings, and strategically focusing on specific niche markets to capitalize on emerging opportunities within the rapidly changing cloud landscape.

The Data Warehouse Software market is experiencing robust growth, driven by the increasing need for businesses to analyze large volumes of data for better decision-making. The market, estimated at $50 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility, attracting both large enterprises and SMEs. Furthermore, the rising demand for real-time analytics and business intelligence (BI) tools is pushing organizations to invest in sophisticated data warehousing solutions. The increasing complexity of data, along with the need for advanced data governance and security, also contribute to market growth. Segment-wise, cloud-based solutions are outpacing on-premise deployments due to their inherent advantages. Geographically, North America currently holds the largest market share, followed by Europe and Asia Pacific, with the latter exhibiting high growth potential due to increasing digitalization and technological advancements in emerging economies like India and China. However, challenges such as the high initial investment cost for implementing data warehouses and the need for skilled professionals to manage these systems could potentially restrain market growth to some extent. Despite these potential restraints, the long-term outlook for the Data Warehouse Software market remains highly positive. The continued proliferation of data, coupled with the increasing adoption of advanced analytical techniques like machine learning and artificial intelligence, will further drive demand for robust and scalable data warehousing solutions. The competitive landscape is characterized by both established technology giants like Amazon Web Services, Microsoft, and Google, and specialized data warehouse providers. This competition fosters innovation and drives down prices, ultimately benefiting end-users. The market is expected to see increased consolidation through mergers and acquisitions as companies strive to enhance their offerings and expand their market reach. The focus on integrating data warehousing solutions with other enterprise software applications, such as CRM and ERP systems, is another key trend shaping the market's trajectory.

The global Big Data in E-commerce market is experiencing robust growth, driven by the increasing volume of e-commerce transactions and the need for businesses to leverage data for improved decision-making, personalized marketing, and enhanced customer experiences. Let's assume, for illustrative purposes, a 2025 market size of $50 billion and a Compound Annual Growth Rate (CAGR) of 15% for the forecast period 2025-2033. This implies significant expansion, reaching an estimated market value of approximately $150 billion by 2033. Key drivers include the proliferation of mobile commerce, the rise of omnichannel strategies, and the increasing adoption of advanced analytics technologies like AI and machine learning to extract actionable insights from vast datasets. Furthermore, the growing demand for real-time data processing and predictive analytics for inventory management, fraud detection, and personalized recommendations fuels this expansion. While data security concerns and the complexity of implementing Big Data solutions present challenges, the overall market trajectory indicates a promising future for Big Data applications in the e-commerce sector. The competitive landscape comprises established technology giants like Amazon Web Services, Microsoft, and IBM, alongside specialized Big Data analytics providers, creating a dynamic market with opportunities for innovation and consolidation. The segment analysis (specific segments not provided) is crucial for identifying high-growth areas within this market. For example, segments focused on real-time analytics for customer experience or AI-powered predictive modeling for marketing campaigns are likely to witness particularly strong growth. Regional variations in e-commerce adoption and technological infrastructure also influence market dynamics. North America and Europe currently hold substantial market share but regions like Asia-Pacific are showing rapid growth potential, due to the expanding e-commerce ecosystem and increasing digital literacy. The continued development and refinement of Big Data technologies, coupled with the growing sophistication of e-commerce businesses in utilizing data-driven strategies, will ensure a sustained expansion of this market in the coming years.

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

The Advanced Analytics Enablement market is experiencing robust growth, driven by the increasing adoption of data-driven decision-making across industries. The market, estimated at $150 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching a significant market value by 2033. This expansion is fueled by several key factors. The proliferation of big data and the need for organizations, both SMEs and large enterprises, to extract actionable insights are major drivers. Furthermore, advancements in predictive analytics, clustering algorithms, and sophisticated statistical features are enhancing the capabilities of advanced analytics platforms, making them more accessible and effective. Growing demand for improved operational efficiency, risk mitigation, and enhanced customer experiences are further bolstering market growth. While data security concerns and the need for skilled professionals represent potential restraints, the overall market outlook remains positive. Segmentation analysis reveals a significant demand across diverse applications, with both SMEs and large enterprises actively adopting advanced analytics solutions. Predictive analytics currently holds the largest segment share, reflecting its critical role in forecasting and strategic planning. However, the adoption of other segments like clustering, calculations and statistical features is rapidly growing as organizations seek more comprehensive data analysis capabilities. Geographically, North America currently dominates the market due to early adoption and a well-established technological infrastructure. However, Asia-Pacific is expected to witness the fastest growth rate during the forecast period driven by increasing digitalization and economic growth in countries like China and India. The competitive landscape comprises a mix of established players like IBM, Amazon Web Services, and Deloitte, along with specialized analytics firms and emerging technology providers. This competitive dynamic will likely fuel innovation and drive further market expansion.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.