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.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q1 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

Overview:

This dataset contains 1000 rows of synthetic online retail sales data, mimicking transactions from an e-commerce platform. It includes information about customer demographics, product details, purchase history, and (optional) reviews. This dataset is suitable for a variety of data analysis, data visualization and machine learning tasks, including but not limited to: customer segmentation, product recommendation, sales forecasting, market basket analysis, and exploring general e-commerce trends. The data was generated using the Python Faker library, ensuring realistic values and distributions, while maintaining no privacy concerns as it contains no real customer information.

Data Source:

This dataset is entirely synthetic. It was generated using the Python Faker library and does not represent any real individuals or transactions.

Data Content:

Potential Use Cases (Inspiration):

Customer Segmentation: Group customers based on demographics, purchasing behavior, and preferences.

Product Recommendation: Build a recommendation system to suggest products to customers based on their past purchases and browsing history.

Sales Forecasting: Predict future sales based on historical trends.

Market Basket Analysis: Identify products that are frequently purchased together.

Price Optimization: Analyze the relationship between price and demand.

Geographic Analysis: Explore sales patterns across different cities.

Time Series Analysis: Investigate sales trends over time.

Educational Purposes: Great for practicing data cleaning, EDA, feature engineering, and modeling.

This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

The revenue in the e-commerce market in the United States was forecast to continuously increase between 2025 and 2029 by in total 498.2 billion U.S. dollars (+37.16 percent). After the tenth consecutive increasing year, the revenue is estimated to reach 1.8 trillion U.S. dollars and therefore a new peak in 2029. Notably, the revenue of the e-commerce market was continuously increasing over the past years.Find other key market indicators concerning the average revenue per user (ARPU) and number of users. The Statista Market Insights cover a broad range of additional markets.

This dataset contains sales data for 10,000 transactions across a variety of electronic products and accessories. It includes key information such as transaction ID, product details (name, category, price), quantity sold, customer demographics (age, gender), payment method, discount applied, transaction date, region, and membership status. The data is designed for analyzing sales trends, customer behavior, and can be used for tasks such as sales forecasting, customer segmentation, and marketing analysis.

E-commerce sales of enterprises by NACE Rev. 2 activity

Note:- Only publicly available data can be worked upon

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Sales for electronics and appliances of U.S. electronic shopping and mail-order houses have increased since 2003. The most significant growth was reported in 2021, when e-commerce sales hit over 86 billion U.S. dollars, up from nearly 51 billion U.S. dollars in 2019. In 2022, e-commerce sales slightly decreased to 85.5 billion U.S. dollars.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

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In 2023, e-commerce comprised over **** percent of total retail sales in the United States. Forecasts suggest that this proportion will continue to rise steadily in the coming years, reaching approximately **** percent by 2027. Fashion fever The digital revolution has significantly changed how retail is done, impacting a wide range of product categories. Out of all e-commerce product categories, apparel and accessories are the most purchased online in the United States. As of February 2023, roughly ** percent of all fashion retail sales took place online. Furniture and home furnishing, as well as computer and consumer electronics, ranked second, with over ** percent of each product category purchased via the internet. The product categories that are least purchased online are office equipment and supplies (*** percent) and books, music, and video (*** percent). Shopping hotspots Amazon dominates the e-commerce industry in the United States, though other competitors still have significant market share. In December 2023, amazon.com was the most-visited e-commerce and shopping site in the United States. That month, around ** percent of all visits to e-commerce sites were made to Amazon. Other popular shopping sites include ebay.com, walmart.com, etsy.com, and target.com. The staggering proportion of online retail sales in the country attributed to Amazon is quite remarkable. In 2023, Amazon's website accounted for almost half of all online computer and consumer electronics sales. Similarly, nearly one-third of online fashion purchases in the country were made on Amazon.

This driver measures the value of retail sales conducted online in the United States. Data is sourced from the US Census Bureau and is presented in 2017 dollars.

United States - E-Commerce Retail Sales was 300226.00000 Mil. of $ in January of 2025, according to the United States Federal Reserve. Historically, United States - E-Commerce Retail Sales reached a record high of 300357.00000 in October of 2024 and a record low of 4467.00000 in October of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - E-Commerce Retail Sales - last updated from the United States Federal Reserve on June of 2025.

From October to December 2024, U.S. retail e-commerce sales amounted to roughly *** billion U.S. dollars, marking an increase compared to the previous quarter. Overall, retail e-commerce sales outdid the quarterly sales records registered in 2020. E-commerce in the post-pandemic era During the second quarter of 2020, as COVID-19 spread across the globe, the U.S.'s quarterly e-commerce revenue reached *** billion for the first time in history. In 2021, online retail sales account for**** percent of total retail in the United States. Clothing and accessories, including footwear, is one of the largest B2C e-commerce merchandise categories. Retail e-commerce sales in the United States are estimated from samples used for the Monthly Retail Trade Survey and exclude online travel services, ticket sales agencies, and financial brokers. Latest trend? Quick commerce Shoppers expect fast delivery of their purchases, especially when it comes to grocery products. This segment of the e-commerce industry goes under quick commerce and is expected to generate increasing revenue in the next years. Major quick commerce companies like Instacart or Uber Eat operate in the United States, where the quick commerce market is forecast to hit nearly ** billion U.S. dollars by 2027.

China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data was reported at 25.400 % in Jun 2017. This records an increase from the previous number of 18.180 % for Dec 2016. China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data is updated quarterly, averaging 25.650 % from Dec 2010 (Median) to Jun 2017, with 14 observations. The data reached an all-time high of 36.000 % in Dec 2011 and a record low of -13.700 % in Dec 2015. China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data remains active status in CEIC and is reported by China e-business Research Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICG: E-commerce: Business Sales Revenue.

A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.

Canada Retail E-Commerce Sales data was reported at 4,425,564.000 CAD th in Dec 2022. This records a decrease from the previous number of 4,499,923.000 CAD th for Nov 2022. Canada Retail E-Commerce Sales data is updated monthly, averaging 1,878,200.500 CAD th from Jan 2016 (Median) to Dec 2022, with 84 observations. The data reached an all-time high of 4,943,411.000 CAD th in Dec 2020 and a record low of 789,553.000 CAD th in Feb 2016. Canada Retail E-Commerce Sales data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.H021: Retail E-Commerce Sales.

Analysis of ‘E-Commerce Sales Dataset 🛒’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/berkayalan/ecommerce-sales-dataset on 28 January 2022.

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

Context

This dataset contains sales details of an E-Commerce platform. It covers 20.000 unique customers and 150.000 basket transactions.

Datasets

Customer Details

• customer_id: Unique ID given to the Customer.
• sex: Sex of customer.
• customer_age: Age of customer.
• tenure: tenure of customer as month

Basket Details

• customer_id: Unique ID given to the Customer.
• product_id: Unique ID of the product which has been added to the basket by the customer.
• basket_date: Date of transaction.
• basket_count: How many times this event occured during this date?

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

United States Retail Sales: E Commerce to Total Sales data was reported at 9.100 % in Sep 2018. This records an increase from the previous number of 9.000 % for Jun 2018. United States Retail Sales: E Commerce to Total Sales data is updated quarterly, averaging 3.950 % from Dec 1999 (Median) to Sep 2018, with 76 observations. The data reached an all-time high of 10.400 % in Dec 2017 and a record low of 0.700 % in Dec 1999. United States Retail Sales: E Commerce to Total Sales data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H002: Retail Sales: By NAIC System: Quarterly.