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.

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 & Food Services: Median CV: excl MV &Parts &GasStations data was reported at 1.200 % in Apr 2025. This records a decrease from the previous number of 1.300 % for Mar 2025. United States Retail Sales & Food Services: Median CV: excl MV &Parts &GasStations data is updated monthly, averaging 0.900 % from Mar 2018 (Median) to Apr 2025, with 86 observations. The data reached an all-time high of 1.300 % in Mar 2025 and a record low of 0.600 % in Dec 2018. United States Retail Sales & Food Services: Median CV: excl MV &Parts &GasStations data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.H: Retail Sales: Measures of Sampling Variability: NAICS.

This statistic shows the trend in the volume of sales (quantity bought) across all retailing sectors in Great Britain monthly from January 2017 to July 2025. Retail sales volumes have generally increased until April 2021, except during the first months of 2020, when a low index value of 77.8 was reached in April 2020, and again in the beginning of 2021. After April 2021, retail sales volume started to have a decreasing trend, despite fluctuations. As of July 2025, volume of retail sales was measured at an index level of 98.4.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.

United States Retail Sales: Median CV: Sporting Goods, Hobby, Book & Music Stores data was reported at 2.600 % in Mar 2025. This stayed constant from the previous number of 2.600 % for Feb 2025. United States Retail Sales: Median CV: Sporting Goods, Hobby, Book & Music Stores data is updated monthly, averaging 2.600 % from May 2001 (Median) to Mar 2025, with 287 observations. The data reached an all-time high of 5.100 % in Apr 2013 and a record low of 1.600 % in Jan 2014. United States Retail Sales: Median CV: Sporting Goods, Hobby, Book & Music Stores data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.H011: Retail Sales: Measures of Sampling Variability: NAICS.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

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:...

United States Retail Sales: Median CV: MV: Automobile & Others data was reported at 1.800 % in Apr 2025. This records an increase from the previous number of 1.600 % for Mar 2025. United States Retail Sales: Median CV: MV: Automobile & Others data is updated monthly, averaging 1.400 % from Apr 2001 (Median) to Apr 2025, with 289 observations. The data reached an all-time high of 2.600 % in Dec 2012 and a record low of 0.800 % in Dec 2015. United States Retail Sales: Median CV: MV: Automobile & Others data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.H: Retail Sales: Measures of Sampling Variability: NAICS.

This statistic shows the quarterly trend in the volume of retail sales (quantity bought) in non-specialized, predominantly non-food stores (i.e. department stores) in Great Britain from 1st quarter 2015 to 1st quarter 2024. The figures are seasonally adjusted estimates, measured using the Retail Sales Index (RSI) and published in index form with a reference year of 2019 equal to 100. Department store sales hit a low in the second quarter of 2020, when the index measured at 85.1. By the first quarter of 2024, sales had recovered and the index measured at 92.6.

Market Size statistics on the Retail Trade industry in the US

The 'Retail Sales MoM' in Japan measures the monthly percentage change in the total value of sales at the retail level, reflecting consumer demand and spending trends.

The 'Retail Sales Control Group MoM' in the USA measures the monthly change in retail sales, excluding autos, gasoline, building materials, and food services.-2026-03-16

Global retail sales were projected to amount to around **** trillion U.S. dollars by 2026, up from approximately **** trillion U.S. dollars in 2021. The retail industry encompasses the journey of a good or service. This typically starts with the manufacturing of a product and ends with said product being purchased by a consumer from a retailer. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. American retailers worldwide As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail. Retail in the U.S. The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

United States Retail Sales: Median CV: Electronics & Appliance Stores data was reported at 2.100 % in Mar 2025. This stayed constant from the previous number of 2.100 % for Feb 2025. United States Retail Sales: Median CV: Electronics & Appliance Stores data is updated monthly, averaging 1.700 % from May 2001 (Median) to Mar 2025, with 287 observations. The data reached an all-time high of 3.100 % in Oct 2016 and a record low of 1.200 % in Mar 2020. United States Retail Sales: Median CV: Electronics & Appliance Stores data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.H011: Retail Sales: Measures of Sampling Variability: NAICS.

The Japanese retail industry recorded around *** trillion Japanese yen in sales in 2023, the highest value in the past 15 years.While non-food products constitute a significant share of the industry, food and beverages represent the largest segment in domestic retail trade. Retail channels The Japanese retail market is characterized by the strong presence of brick-and-mortar store retailing. Even though the e-commerce sector is emerging, consumers prefer physical stores to purchase everyday goods, cosmetics, and foodstuff in particular. However, non-store retailers are gradually gaining traction as potential customers are taking advantage of fast shipping and efficient customer services. Retail stores Retail stores in Japan encompass a variety of physical stores, ranging from small specialty retailers to large department stores. Within the food and beverage segment, supermarkets and convenience stores, so-called konbini, are the main points of sale for grocery shopping. In the non-food segments, department stores with their broad product range are competing with specialty retailers. Additionally, supermarkets are joining the competition, with fashion items reported as a major revenue source after foodstuff.

Russia Retail Sales: Alcohol: Annual: Total as Absolute Measurement data was reported at 96.800 dal mn in 2016. This records a decrease from the previous number of 99.200 dal mn for 2015. Russia Retail Sales: Alcohol: Annual: Total as Absolute Measurement data is updated yearly, averaging 120.300 dal mn from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 139.900 dal mn in 1995 and a record low of 74.400 dal mn in 1992. Russia Retail Sales: Alcohol: Annual: Total as Absolute Measurement data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Wholesale, Retail and Catering Sector – Table RU.RJB016: Retail Sales: Alcohol.

This statistic depicts the market size of the retail sales sector in Chile in 2015, broken down by category. That year, the main category of retail sales in the South American country was non-edible goods retail, with a market size of more than ** billion U.S. dollars.

This statistic illustrates the monthly trend in the value of mail order retail sales in Great Britain from January 2016 to July 2024, as an index of sales per week. Despite slight fluctuations, mail order sales made via non-store retailers showed an overall increase during this period of time, peaking in June 2020 at an index value of 152.6. In July 2024 the sales index measured at 144.2. The figures are seasonally adjusted estimates, measured using the Retail Sales Index (RSI) and published in index form with a reference year of 2019 equal to 100.

The statistic depicts the sales value of the global retail market from 2011 to 2021, by segment. In 2016, the global non-luxury retail market was valued at approximately ***** trillion U.S. dollars.

Total retail sales in Canada represents the aggregate value of goods sold through retail channels, measured in billions of Canadian dollars. This includes sales across all retail subsectors such as food and beverage stores, motor vehicle and parts dealers, clothing and accessories, furniture and home furnishings, electronics, building materials, gasoline stations, health and personal care, and general merchandise stores. Data encompasses both brick-and-mortar and e-commerce transactions. Data is sourced from Statistics Canada's Monthly Retail Trade Survey and is presented in chained 2017 dollars.