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.

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

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.

Graph and download economic data for Advance Retail Sales: Retail Trade (RSXFS) from Jan 1992 to Aug 2025 about retail trade, sales, retail, services, and USA.

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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Overview with Chart & Report: Core Retail Sales m/m reflects retail sales in the US in the reported month compared to the previous month. The indicator is calculated based on statistics received from 5,000 retail stores of

Graph and download economic data for Advance Retail Sales: Retail Trade and Food Services (MARTSMPCSM44X72USS) from Feb 1992 to Aug 2025 about retail trade, food, percent, sales, retail, services, and USA.

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.

Data Overview

• Scale: All sales figures are reported in millions of dollars.
• Size: The dataset contains 40,479 rows and 5 columns.
• Time Frame: January 1992 - September 2024.
• Industries Covered: Over 60 industries, including food, clothing, footwear, office supplies, automobiles, electronics, books, beverages, furniture, grocery and many more.
• Attributes:
  • naics_code
  • kind_of_business
  • sales_month
  • sales
  • estimate_type
• Source: This dataset was sourced from the publicly available U.S. Census Bureau retail sales data.

Cleaning & Preprocessing

• Missing Values:
  Some entries contained (NA) and (S) values, which were converted to null values.
  • (S): Estimate does not meet publication standards due to high sampling variability (coefficient of variation greater than 30%) or poor response quality (low total quantity response rate).
• Formatting:
  The downloaded data included headings, subheadings, and notes embedded within the tables. These extraneous elements were removed to ensure a clean and consistent dataset.
• Data Compilation:
  The original dataset was spread across multiple sheets, with each sheet containing data for a specific year. These sheets were consolidated into a single, unified table.
• Feature Engineering:
  A new column was created to provide both seasonally adjusted and non-seasonally adjusted sales values, enabling more nuanced analysis. Estimates are adjusted for seasonal variations, as well as holiday and trading-day differences, but not for price changes.

Use Cases

This dataset can be applied to a variety of analytical and machine learning tasks, including:

• Data Cleaning: Practice handling missing values, stray entries, and working with datetime data.
• Time Series Analysis: Perform trend analysis, seasonality detection, and forecasting.
• Exploratory Data Analysis (EDA): Gain insights into industry-specific trends and patterns.
• Machine Learning: Use it for predictive modeling and classification tasks.
• Market Research: Analyze industry performance to inform business strategies.

The U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.

United States Retail Sales: Shoe Stores data was reported at 3.072 USD bn in May 2018. This records an increase from the previous number of 2.829 USD bn for Apr 2018. United States Retail Sales: Shoe Stores data is updated monthly, averaging 2.053 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 4.268 USD bn in Dec 2016 and a record low of 1.161 USD bn in Feb 1993. United States Retail Sales: Shoe Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

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.

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

United States Retail Sales Nowcast: sa: YoY data was reported at 4.271 % in 01 Dec 2025. This records an increase from the previous number of 4.203 % for 24 Nov 2025. United States Retail Sales Nowcast: sa: YoY data is updated weekly, averaging 6.828 % from Feb 2020 (Median) to 01 Dec 2025, with 303 observations. The data reached an all-time high of 44.471 % in 17 May 2021 and a record low of -13.873 % in 25 May 2020. United States Retail Sales Nowcast: sa: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

Retail Sales in Estonia increased 5.90 percent in October of 2025 over the previous month. This dataset provides - Estonia Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

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.

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.