Graph and download economic data for E-Commerce Retail Sales (ECOMSA) from Q4 1999 to Q1 2025 about e-commerce, retail trade, sales, retail, and USA.

United States RS: ARTS: E-Commerce: ES: Office Equipment & Supplies data was reported at 10.497 USD bn in 2018. This records an increase from the previous number of 9.005 USD bn for 2017. United States RS: ARTS: E-Commerce: ES: Office Equipment & Supplies data is updated yearly, averaging 5.975 USD bn from Dec 1999 (Median) to 2018, with 17 observations. The data reached an all-time high of 10.497 USD bn in 2018 and a record low of 592.000 USD mn in 1999. United States RS: ARTS: E-Commerce: ES: Office Equipment & Supplies 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.H003: Retail Sales: Annual Retail Trade Survey: NAICS.

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.

Provides current estimates of sales at retail and food services stores and inventories held by retail stores throughout the United States.

The Annual Retail Trade Survey (ARTS) produces national estimates of total annual sales, e-commerce sales, end-of-year inventories, inventory-to-sales ratios, purchases, total operating expenses, inventories held outside the United States, gross margins, and end-of-year accounts receivable for retail businesses and annual sales and e-commerce sales for accommodation and food service firms located in the U.S.

Provides an early indication of sales of retail and food service companies throughout the United States.

Quarterly retail e-commerce sales are estimated from the same sample used for the Monthly Retail Trade Survey (MRTS) to estimate preliminary and final U.S. retail sales. Coverage includes all retailers whether or not they are engaged in e-commerce. Online travel services, financial brokers and dealers, and ticket sales agencies are not classified as retail and are not included in either the total retail or retail e-commerce sales estimates.

Retail Sales in the United States increased 0.50 percent in July of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains every wholesale purchase of liquor in the State of Iowa by retailers for sale to individuals since January 1, 2012. The State of Iowa controls the wholesale distribution of liquor intended for retail sale, which means this dataset offers a complete view of retail liquor sales in the entire state. The dataset contains every wholesale order of liquor by all grocery stores, liquor stores, convenience stores, etc., with details about the store and location, the exact liquor brand and size, and the number of bottles ordered. In addition to being an excellent dataset for analyzing liquor sales, this is a large and clean public dataset of retail sales data. It can be used to explore problems like stockout prediction, retail demand forecasting, and other retail supply chain problems. The data dictionary is available from the State of Iowa's Alcoholic Beverages Division , within the Iowa Department of Commerce . There are some minor discrepancies in the data, discussed in the web view of the data . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery.

To provide an early indication of sales of retail and food service companies. The United States Code, Title 13, authorizes this survey and provides for voluntary responses.

The revenue in the e-commerce market in the United States was modeled to amount to 1.18 trillion U.S. dollars in 2024. Following a continuous upward trend, the revenue has risen by 754.29 billion U.S. dollars since 2017. Between 2024 and 2029, the revenue will rise by 655.91 billion U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

Guizhou: Chain: Department Store: Sales data was reported at 0.108 RMB bn in 2019. This records an increase from the previous number of 0.078 RMB bn for 2018. Guizhou: Chain: Department Store: Sales data is updated yearly, averaging 0.049 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 0.176 RMB bn in 2006 and a record low of 0.021 RMB bn in 2011. Guizhou: Chain: Department Store: Sales data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CRAG: Department Store: Guizhou.

This dataset presents statistics for Retail Trade: Floor Space by Selected Industry for the U.S. and States

By the end of 2024, total retail sales reached approximately 7.26 trillion U.S. dollars, around a quarter of a billion U.S. dollar increase from the year before. Retail sales have steadily increased since 2009, as the economy recovered from the downward trend due to the recession following the 2007-2008 financial crisis, and most recently from the impact of the coronavirus (COVID-19) crisis. The United States as retail powerhouse The United States is home to many of the leading retail companies in the world, including Walmart, Costco, and Amazon. Amazon, in particular, has seen extreme levels of growth in revenue in tandem with the increase of e-commerce globally. The rise of e-commerce and mobile shopping E-commerce is responsible for a growing percentage of total retail sales, partially due to a surge in mobile shopping, with customers increasingly using their mobile devices for various online shopping activities. Smartphones accounted for more retail website visits than desktops or tablets, and matched desktops in generating online shopping orders.

Inner Mongolia: Chain: Department Store: Sales: Retail data was reported at 0.563 RMB bn in 2019. This records a decrease from the previous number of 0.570 RMB bn for 2018. Inner Mongolia: Chain: Department Store: Sales: Retail data is updated yearly, averaging 0.373 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 1.447 RMB bn in 2013 and a record low of 0.196 RMB bn in 2008. Inner Mongolia: Chain: Department Store: Sales: Retail data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CRAG: Department Store: Inner Mongolia.

In the first quarter 2025, the share of e-commerce in total U.S. retail sales stood at **** percent, up from the previous quarter. From January to March 2025, retail e-commerce sales in the United States hit over *** billion U.S. dollars, the highest quarterly revenue in history. How e-commerce measures up in total U.S. retail In 2024, the reported total value of retail e-commerce sales in the United States amounted to over ****trillion U.S. dollars—impressive, but the figure pales compared to the total annual retail trade value of ******trillion U.S. dollars. Rising e-commerce segments Online shopping is popular among all age groups, though digital purchases are most common among Millennial internet users. In 2022, around ** percent of Millennials purchased items via the internet. Mobile commerce is also growing in popularity, as consumers increasingly rely on their smartphones and mobile apps for shopping activities. In the fourth quarter of 2022, m-commerce spending made up ** percent of the overall online spending in the United States.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Taiwan CS: Retail Trade: General Merchandise: Department Stores data was reported at 39,560.423 NTD mn in Oct 2018. This records an increase from the previous number of 25,439.151 NTD mn for Sep 2018. Taiwan CS: Retail Trade: General Merchandise: Department Stores data is updated monthly, averaging 19,039.205 NTD mn from Jan 1999 (Median) to Oct 2018, with 238 observations. The data reached an all-time high of 39,560.423 NTD mn in Oct 2018 and a record low of 9,766.516 NTD mn in Sep 1999. Taiwan CS: Retail Trade: General Merchandise: Department Stores data remains active status in CEIC and is reported by Ministry of Economic Affairs. The data is categorized under Global Database’s Taiwan – Table TW.H007: Commerce Sales: Ministry of Economic Affairs.

Internet sales in Great Britain by store type, month and year.

Department stores have continued their long-term dip, with revenue falling amid shifting market dynamics. The industry's slump has been fueled by a 7.2% revenue drop in 2021 alone, caused by relatively low consumer confidence levels and high unemployment. Industry performance has been challenged by rising inflationary pressure since 2022 and the competitive presence of e-commerce rivals. Another rising trend is the increasing number of major retailers that have expanded their product ranges to include groceries, providing a heightened level of convenience. This transitions the revenue of these retailers to the Warehouse Clubs and Supercenters industry (IBISWorld report 45291), reducing industry participation. Revenue for department stores is expected to dip at a CAGR of 2.7% to $187.4 billion through the end of 2025, including a slump of 0.3% in 2025 alone, when profit will account for 3.7% of revenue. Online companies are increasingly undercutting traditional department store prices to save on operational costs. Companies with brick-and-mortar stores incur higher operational costs than online-based businesses because they pay for high-traffic retail space and require sales associates. Retailers have lowered selling prices and offered increased promotional deals to better compete. In April 2024, Nordstrom launched its digital Marketplace to expand its online presence and appeal to a wider audience. Through online platforms, retailers can offer a wider selection of brands, sizes and products. Similarly, department stores have since launched their digital stores and integrated them into their operations to provide an omnichannel shopping experience. While these efforts have helped retain some customers, profit has dropped because of inflationary pressures on the industry, resulting in retailers making more cost-cutting decisions, which has tempered declines. In the coming years, accelerating competition from e-commerce businesses and the transition of department stores to supercenters will continue to pressure revenue. Some department stores will shutter more locations. However, disposable income growth will help lessen these factors' blow on future revenue. Department stores like Macy's and Nordstrom will continue to benefit from strong brand recognition, particularly as older customers become more comfortable with online shopping. Investments in online platforms will pay off for retailers and help department stores be more competitive in a tough business landscape. Revenue for department stores is expected to slump at a CAGR of 0.2% to $185.1 billion through the end of 2030.