Retail Sales in the United States increased 0.20 percent in September 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, identified by the series ID RSXFS, is sourced from the U.S. Census Bureau and is available through the Federal Reserve Economic Data (FRED) system of the St. Louis Fed. It provides a monthly measure of retail sales across the United States. The data represents the total value of sales at retail and food services stores, measured in millions of dollars and adjusted for seasonal variations. It is important to note that the most recent month's value is an advance estimate, which is subject to revision in subsequent months as more comprehensive data becomes available. As a key economic indicator, this series is widely used by economists and analysts to gauge consumer spending and assess the overall health of the U.S. economy.

Suggested Use Cases: - This dataset is highly valuable for economic analysis and can be used to: - Conduct time series analysis and modeling. - Track consumer spending patterns. - Forecast future retail sales. - Analyze the impact of economic events on the retail sector.

License The RSXFS dataset is sourced from the U.S. Census Bureau and is considered Public Domain: Citation Requested. This means the data is freely available for use, but you must cite the source and acknowledge that the data was obtained from FRED. If you plan on using any copyrighted series from other data providers on FRED for commercial purposes, you would need to contact the original data owner for permission.

Data Fields: The dataset primarily contains two columns: - observation_date: The date of the monthly data point, recorded as the first day of each month from January 1992 to July 2025. - RSXFS: The value of advance retail sales in millions of dollars.

Citation and Provenance:
Source: U.S. Census Bureau
Release: Advance Monthly Sales for Retail and Food Services
FRED Link: https://fred.stlouisfed.org/series/RSXFS
Citation: U.S. Census Bureau, Advance Retail Sales: Retail Trade [RSXFS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RSXFS, September 8, 2025.

This statistic shows a trend in total retail sales, including food services, in the United States from January 2017 to July 2025. In July 2025, U.S. retail sales had amounted to an estimated *********** U.S. dollars (not adjusted), which is an increase of approximately ** ******* U.S. dollars compared to the same month one year earlier.

View monthly updates and historical trends for US Retail Sales. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

Context

The Monthly State Retail Sales (MSRS) is the Census Bureau's new experimental data product featuring modeled state-level retail sales. This is a blended data product using Monthly Retail Trade Survey data, administrative data, and third-party data. Year-over-year percentage changes are available for Total Retail Sales excluding Non-store Retailers as well as 11 retail North American Industry Classification System (NAICS) retail subsectors. These data are provided by state and NAICS codes beginning with January 2019.

Content

Geography: US

Time period: 2019 - 2022

Unit of analysis: US Census Bureau's Monthly State Retail Sales Data

Variables

Variable	Description
fips	2-digit State Federal Information Processing Standards (FIPS) code. For more information on FIPS Codes, please reference this document. Note: The US is assigned a "00" State FIPS code.
state_abbr	States are assigned 2-character official U.S. Postal Service Code. The United States is assigned "USA" as its state_abbr value. For more information, please reference this document.
naics	Three-digit numeric NAICS value for retail subsector code.
subsector	Retail subsector.
year	Year.
month	Month.
change_yoy	Numeric year-over-year percent change in retail sales value.
change_yoy_se	Numeric standard error for year-over-year percentage change in retail sales value.
coverage_code	Character values assigned based on the non-imputed coverage of the data.

Variable	Description
coverage_code	Character values assigned based on the non-imputed coverage of the data.
coverage	Definition of the codes.

Acknowledgements

Datasource: United States Census Bureau's Monthly State Retail Sales

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View monthly updates and historical trends for US Real Retail Sales. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

Graph and download economic data for Retail Sales: Jewelry Stores (MRTSSM44831USS) from Jan 1992 to Feb 2021 about jewelry, retail trade, sales, retail, and USA.

Standard error reference tables for the Retail Sales Index in Great Britain.

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

Graph and download economic data for Motor Vehicle Retail Sales: Domestic Autos (DAUTOSA) from Jan 1967 to Aug 2025 about headline figure, retail trade, vehicles, domestic, new, sales, retail, and USA.

In fiscal year 2022, the organized retail sales volume was ** billion U.S. dollars. The sales volume of organized retail is likely to increase to *** billion U.S. dollar in the fiscal year 2028 in India.

Retail Sales in the United Kingdom decreased 1.10 percent in October of 2025 over the previous month. This dataset provides the latest reported value for - United Kingdom Retail Sales MoM - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This statistic shows the volume of all retail sales (quantity bought) in Great Britain from 2011 to 2024. In 2024, the retail sales volume had an index value of ****. The figures are seasonally adjusted estimates, measured using the Retail Sales Index (RSI) and published in index form with a reference year of 2022 equal to 100.

This statistic shows the percentage change in the quantity of goods bought (retail sales volume) in Great Britain, from January 2017 to July 2025. The percentage change of all retail sales remained negative between April 2022 and March 2024, but an incerase of 1.1 percent was reported in the most recent period. The low growth rates in retail sales are seen as an impact of the cost of living crisis and inflation in the UK and elsewhere in world.

The sales volume of retail trade generally increased in Turkey from 2010 to 2023. The figures are presented in index form with a reference year of 2015 equal to 100. As of 2023, the sales volume of retail trade sales amounted to ***** index score.

Key information about Italy Retail Sales Growth

• Italy Retail Sales dropped 0.5 % YoY in Sep 2025, compared with a 1.4 % increase in the previous month.
• Italy Retail Sales Growth YoY data is updated monthly, available from Jan 2001 to Sep 2025, with an average growth rate of -1.7 %.
• The data reached an all-time high of 34.5 % in Apr 2021 and a record low of -27.5 % in Apr 2020.
• In the latest reports, Car Sales of Italy recorded 1,793,038.0 units in Dec 2024, representing a drop of 0.3 %.

CEIC calculates monthly Retail Sales: Excl. Motor Vehicle Growth from monthly Retail Trade Index. The Italian National Institute of Statistics provides Retail Trade Index with base 2021=100. Retail Sales exclude Tobacco and Automotive Fuels.

View monthly updates and historical trends for US Retail and Food Services Sales. from United States. Source: Census Bureau. Track economic data with YCha…

In 2024, retail e-commerce sales in the United States reached an estimated **** billion U.S. dollars, roughly double the sales value reached in 2019. E-commerce's growth trajectory Driven by the escalating integration of technology into daily life, e-commerce has witnessed a remarkable surge in popularity. Projections indicate a significant uptick in e-commerce users in the United States, rising from *** million in 2025 to over *** million by 2029. As of 2023, apparel and accessories ranked as the most sought-after e-commerce product category, comprising over ** percent of all retail sales in the U.S. This trend persists despite inflationary pressures, positioning this category among the e-commerce segments experiencing the most significant year-on-year price changes. M-commerce users demographic While the demand for the convenience of purchasing from the palm of one's hand is also rapidly increasing, various demographic factors influence mobile commerce usage. There's a higher proportion of male online shoppers than females, with a split of ** percent versus ** percent. Age is another determinant. Younger consumers exhibit a greater inclination towards m-commerce, with ** percent of mobile shoppers falling within the ** to ** age bracket. Furthermore, income levels also shape mobile shopping habits, with individuals earning less than ****** U.S. dollars annually showing the highest propensity for mobile-based purchases.

Retail Sales in Finland increased 0.40 percent in October of 2025 over the previous month. This dataset provides - Finland Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Global Retail Sales Data provided here is a self-generated synthetic dataset created using Random Sampling techniques provided by the Numpy Package. The dataset emulates information regarding merchandise sales through a retail website set up by a popular fictional influencer based in the US between the '23-'24 period. The influencer would sell clothing, ornaments and other products at variable rates through the retail website to all of their followers across the world. Imagine that the influencer executes high levels of promotions for the materials they sell, prompting more ratings and reviews from their followers, pushing more user engagement.

This dataset is placed to help with practicing Sentiment Analysis or/and Time Series Analysis of sales, etc. as they are very important topics for Data Analyst prospects. The column description is given as follows:

Order ID: Serves as an identifier for each order made.

Order Date: The date when the order was made.

Product ID: Serves as an identifier for the product that was ordered.

Product Category: Category of Product sold(Clothing, Ornaments, Other).

Buyer Gender: Genders of people that have ordered from the website (Male, Female).

Buyer Age: Ages of the buyers.

Order Location: The city where the order was made from.

International Shipping: Whether the product was shipped internationally or not. (Yes/No)

Sales Price: Price tag for the product.

Shipping Charges: Extra charges for international shipments.

Sales per Unit: Sales cost while including international shipping charges.

Quantity: Quantity of the product bought.

Total Sales: Total sales made through the purchase.

Rating: User rating given for the order.

Review: User review given for the order.