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 Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

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.

Comprehensive dataset analyzing Amazon's daily package shipping volume, including methodology, seasonal variations, and growth trends from 2020-2025

This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as "forest" or "grassland" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin. These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/).

This dataset represents spatial aggregates of the MODIS/Terra Surface Reflectance 8-Day L3 Global 500m ISIN Grid (MOD09A1), as provided by the EROS Data Center (EDC). THis product is available for multiple days.

The University of New Hampshire's (UNH) Earth Science Information Partner (ESIP), EOS-WEBSTER (http://eos-earthdata.sr.unh.edu/), has created this MODIS surface reflectance aggregate product from MOD09A1 8-day 500m surface reflectance tiles. MOD09A1 data are provided every 8 days as a gridded level-3 product in the Integerized Sinusoidal projection. UNH has aggregated multiple tiles to create a large-region surface reflectance product, which can be spatially subset within the EOS-WEBSTER Search & Retrieve data ordering tool. Our system has also reprojected these data from the original Integerized Sinusoidal projection to Geographic, with a pixel resolution of 15 arc seconds.

The aggregate products contain all the layers from the original input products plus an additional layer created by EOS-WEBSTER, which provides a look-up code to map each pixel in the aggregate back to its original input tile.

These aggregate products allow the users of EOS-WEBSTER to subset MODIS 8-day surface reflectance data across tile boundaries and to customize the spatial region of interest using an on-line GUI interface. Data are also provided in a generic binary format (BSQ) with detailed header information, which can be read into most image or data processing applications. EOS-WEBSTER has broken the original 12 layers in the input tiles into 6 logical holdings so that a user may order 1 or more of the band sets (see below). For instance, the user may only wish to order the 7 reflectance bands and not all the other supporting data which are provided with the original data-this significantly cuts down on the data volume.

Version-4 input products were used to create these output files. Please see the Global Change Master Directory (GCMD) to learn more about the MOD09A1 input data (http://gcmd.nasa.gov/getdif.htm?MOD09A13).

This MODIS surface reflectance product may be used to generate land- related products or used as input to global and regional climate models and surface energy balance models. These data also may be used for land cover characterization.

Data Set Characteristics: Output format: BSQ with header,netCDF,HDF-EOS,GEOTIFF, ASCII Grid Logical Data Holdings: Surface Reflectance: 7 surface reflectance bands Surface Reflectance QC: 1 bit field band Sun/Sensor Geometry: 3 bands State Flags: 1 bit field band Day-of-Year: 1 band Input Tile Code: 1 band

This data set contains proportional estimates for the vegetative cover types of woody vegetation, herbaceous vegetation, and bare ground over the Amazon Basin for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of MODIS 32-day composites were used to create the vegetation cover types using the Vegetation Continuous Fields (VCF) (Hansen et al., 2002) approach which shows how much of a land cover such as "forest" or "grassland" exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.The original MODIS products are 500-m spatial resolution and are derived from 2000-2001 data products. The data were resampled to 1-km resolution for the regional study under this project, and provided as 3 separate cover type files in ENVI and GeoTIFF file formats that are provided in six zipped files. These products are registered to the rest of the regional data sets over the Amazon basin. These data are also available for download from the Global Land Cover Facility Website (http://modis.umiacs.umd.edu/).

Introduction

Amazon, Alibaba, and eBay: The Giants of E-commerce

Three companies have revolutionised how we shop online: Amazon, Alibaba, and eBay. Their origins, growth, and impact on global commerce are remarkable: - Amazon: Founded by Jeff Bezos in 1994, Amazon began as an online bookstore. It rapidly expanded its product range, invested heavily in technology and logistics, and introduced groundbreaking services like Amazon Prime and Amazon Web Services (AWS). Today, Amazon is a leader in e-commerce, cloud computing, and innovation. - Alibaba: Founded by Jack Ma in 1999, Alibaba aimed to connect Chinese manufacturers with international buyers. Through platforms like Alibaba.com, Taobao, and Tmall, it transformed e-commerce in China and became a global player in digital payments and financial services through Ant Group. - eBay: Started by Pierre Omidyar in 1995 as an online auction site, eBay quickly became a popular platform for buying and selling a wide variety of goods. It pioneered consumer-to-consumer (C2C) commerce, fostered a vibrant online community, and expanded globally.

These companies have distinct strengths and growth trajectories: - Amazon leads in technological innovation and customer-centric services. - Alibaba dominates the Chinese market and is influential in digital payments. - eBay pioneered C2C commerce and maintains a strong global presence.

Together, Amazon, Alibaba, and eBay have shaped the modern e-commerce landscape, democratised commerce, and continue to influence how we buy and sell goods around the world.

Amazon, Alibaba, and eBay, their origins, sales, growth, and impact:

Amazon

Founding and Early Days

• Founded: July 5, 1994
• Founder: Jeff Bezos
• Headquarters: Seattle, Washington, USA

Amazon began as an online bookstore. Jeff Bezos, who was then a Wall Street hedge fund executive, decided to capitalise on the growth of the internet in the 1990s. He left his job, moved to Seattle, and started Amazon in his garage.

Growth and Expansion

• Initial Growth: The company's initial business model allowed it to offer more books than traditional bookstores. The first book sold was "Fluid Concepts and Creative Analogies" by Douglas Hofstadter.
• Diversification: By the late 1990s, Amazon expanded its product line to include electronics, toys, and household goods. This diversification helped Amazon grow rapidly.
• Technology and Logistics: Amazon invested heavily in technology and logistics to improve its supply chain efficiency. It built extensive warehousing and delivery infrastructure, which became critical to its success.

Key Milestones

• Amazon Prime: Launched in 2005, this subscription service offers free two-day shipping, streaming services, and other benefits. It became a significant driver of customer loyalty.
• AWS (Amazon Web Services): Started in 2006, AWS provides cloud computing services and has become a major revenue source for Amazon, establishing it as a leader in the tech industry.
• Acquisitions: Amazon acquired several companies to bolster its offerings, including Zappos, Whole Foods Market, and Ring.

Impact

• E-commerce Leader: Amazon revolutionised online shopping with its vast selection, customer reviews, and recommendation algorithms.
• Economic Influence: The company's practices have influenced labour, retail, and technology sectors globally.
• Innovation: Amazon has pioneered in areas like AI with Alexa, cashier-less stores with Amazon Go, and drone delivery with Prime Air.

Visualisation

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A Markdown document with R code for the above chart, with a full chart explanation. link

Amazon Chart

• Significant Growth: Steady increase in quarterly sales revenue over the years.
• Seasonal Influence: Strong seasonal fluctuations, with Q4 consistently being the highest-performing quarter due to the holiday season.
• Revenue Variability: Expanding range of quarterly sales, reflecting business growth.
• Quarterly Performance: Consistent relative performance of each quarter, with Q4 standing out.

Alibaba

Founding and Early Days

• Founded: April 4, 1999
• Founder: Jack Ma and a team of 17 others
• Headquarters: Hangzhou, Zhejiang, China

Jack Ma, a former English teacher, founded Alibaba to connect Chinese manufacturers with international buyers. He aimed to support small and medium-sized enterprises (SMEs) in China by leveraging the internet.

Growth and Expansion

• Early Strategy: Alibaba started with Alibaba.com, a B2B marketplace that connected Chinese bu...

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool, and are in a Network Common Data Form (netCDF), which is a very common format used across the scientific community. These particular GFS files contain a comprehensive number of global atmosphere/land variables at a relatively high spatiotemporal resolution (approximately 13x13 km horizontal, vertical resolution of 127 levels, and hourly), are not only necessary for the NACC-Cloud tool to adequately drive community air quality applications (e.g., U.S. EPA’s Community Multiscale Air Quality model; https://www.epa.gov/cmaq), but can be very useful for a myriad of other applications in the Earth system modeling communities (e.g., atmosphere, hydrosphere, pedosphere, etc.). While many other data file and record formats are indeed available for Earth system and climate research (e.g., GRIB, HDF, GeoTIFF), the netCDF files here are advantageous to the larger community because of the comprehensive, high spatiotemporal information they contain, and because they are more scalable, appendable, shareable, self-describing, and community-friendly (i.e., many tools available to the community of users). Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/inputs/yyyymmdd/) for the 12Z cycle and includes 24-hr output for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).

Also available are netCDF formatted Global Land Surface Datasets (GLSDs) developed by Hung et al. (2024). The GLSDs are based on numerous satellite products, and have been gridded to match the GFS spatial resolution (~13x13 km). These GLSDs contain vegetation canopy data (e.g., land surface type, vegetation clumping index, leaf area index, vegetative canopy height, and green vegetation fraction) that are supplemental to and can be combined with the GFS meteorological netCDF data for various applications, including NOAA-ARL's canopy-app. The canopy data variables are climatological, based on satellite data from the year 2020, combined with GFS meteorology for the year 2022, and are created at a daily temporal resolution (/inputs/geo-files/gfs.canopy.t12z.2022mmdd.sfcf000.global.nc)

This data set contains proportional estimates for the vegetative cover types of tree cover, herbaceous vegetation, and bare ground over South America for the period 2000-2001. These products were derived from all seven bands of the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellite. A set of 500-m MOD09A1 Surface Reflectance 8-day minimum blue reflectance composites were used as input data. To reduce the presence of cloud shadows, The data were converted to 40-day composites using a second darkest albedo (sum of blue, green, and red bands), and the Vegetation Continuous Fields (VCF) algorithmn was utilized (Hansen et al., 2002). The VCF shows how much of a land cover such as forest or grassland exists anywhere on the land surface. The VCF product may depict areas of heterogeneous land cover better than traditional discrete classification schemes which shows where land cover types are concentrated.

There are three images provided in GeoTIFF format.

In the first quarter of 2025, revenues of Amazon Web Services (AWS) rose to 17 percent, a decrease from the previous three quarters. AWS is one of Amazon’s strongest revenue segments, generating over 115 billion U.S. dollars in 2024 net sales, up from 105 billion U.S. dollars in 2023. Amazon Web Services Amazon Web Services (AWS) provides on-demand cloud platforms and APIs through a pay-as-you-go-model to customers. AWS launched in 2002 providing general services and tools and produced its first cloud products in 2006. Today, more than 175 different cloud services for a variety of technologies and industries are released already. AWS ranks as one of the most popular public cloud infrastructure and platform services running applications worldwide in 2020, ahead of Microsoft Azure and Google cloud services. Cloud computing Cloud computing is essentially the delivery of online computing services to customers. As enterprises continually migrate their applications and data to the cloud instead of storing it on local machines, it becomes possible to access resources from different locations. Some of the key services of the AWS ecosystem for cloud applications include storage, database, security tools, and management tools. AWS is among the most popular cloud providers Some of the largest globally operating enterprises use AWS for their cloud services, including Netflix, BBC, and Baidu. Accordingly, AWS is one of the leading cloud providers in the global cloud market. Due to its continuously expanding portfolio of services and deepening of expertise, the company continues to be not only an important cloud service provider but also a business partner.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.