Retail Sales in the United States decreased 0.20 percent in January of 2026 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 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.

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:...

In 2025, the in-store or brick-and-mortar retail channel was forecast to account for **** percent of total retail sales in the United States. By 2027, e-commerce is expected to make up **** percent of all retail sales.

Retail Trade, sales by industries based on North American Industry Classification System (NAICS), monthly.

Overview with Chart & Report: Retail Sales m/m reflect a change in the US retail sails in the reported month compared to the previous one. 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 (RSXFS) from Jan 1992 to Dec 2025 about retail trade, sales, retail, services, and USA.

In fiscal year 2021, the Chinese online retailers JD.com and the global retail giant Amazon ranked as the fastest growing retailers based on CAGR between FY2016 and 2021. Among the leading 10 retailers based on 2021 sales, Walmart, the world's leading retailer, registered a comparatively slower CAGR at 3.3 percent.

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 Retail and Food Services Sales. from United States. Source: Census Bureau. Track economic data with YCha…

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).

Retail sales data for Great Britain in value and volume terms, seasonally and non-seasonally adjusted.

In 2020, global retail sales fell by 2.9 percent as a result of the COVID-19 pandemic, bouncing back in 2021 with a growth of 9.7 percent Global retail sales were projected to amount to around 27.3 trillion U.S. dollars by 2022, up from approximately 23.7 trillion U.S. dollars in 2020. 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.

View quarterly updates and historical trends for US E-Commerce Sales as Percent of Retail Sales. from United States. Source: Census Bureau. Track economic…

Retail Sales in Slovakia decreased 3.70 percent in January of 2026 over the same month in the previous year. This dataset provides - Slovakia Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Online Retail Sales: YoY: Year to Date: Goods data was reported at 10.300 % in Feb 2026. This records an increase from the previous number of 5.200 % for Dec 2025. China Online Retail Sales: YoY: Year to Date: Goods data is updated monthly, averaging 14.300 % from Jun 2014 (Median) to Feb 2026, with 125 observations. The data reached an all-time high of 49.900 % in Sep 2014 and a record low of 3.000 % in Feb 2020. China Online Retail Sales: YoY: Year to Date: Goods data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Online Retail Sales.

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.

Key information about Georgia Retail Sales Growth

• Georgia Retail Sales grew 16.6 % YoY in Dec 2025, compared with a 11.8 % increase in the previous quarter.
• Georgia Retail Sales Growth YoY data is updated quarterly, available from Mar 2010 to Dec 2025, with an average growth rate of 26.0 %.
• The data reached an all-time high of 109.2 % in Dec 2012 and a record low of -14.5 % in Jun 2020.
• In the latest reports, Car Sales of Georgia recorded 3,400.0 units in Dec 2019, representing a drop of 3.4 %.

CEIC calculates quarterly Retail Sales: Excl. Motor Vehicles Growth from quarterly Retail Trade Turnover. The National Statistics Office of Georgia provides Retail Trade Turnover in local currency. Retail Sales: Excl. Motor Vehicles Growth prior to Q1 2018 is based on NACE Rev. 1.1.

Key information about Colombia Retail Sales Growth

• Colombia Retail Sales grew 7.0 % YoY in Dec 2025, compared with a 8.5 % increase in the previous month.
• Colombia Retail Sales Growth YoY data is updated monthly, available from Jan 2014 to Dec 2025, with an average growth rate of 8.1 %.
• The data reached an all-time high of 48.3 % in Apr 2021 and a record low of -32.8 % in Apr 2020.
• In the latest reports, Car Sales of Colombia recorded 26,742.0 units in Dec 2025, representing a growth of 21.3 %.

CEIC calculates monthly Retail Sales: Excl. Motor Vehicles Growth from monthly Real Retail Trade Index. National Administrative Department of Statistics provides Real Retail Trade Index with base 2019=100. Retail Sales: Excl. Motor Vehicles Growth prior to January 2020 is calculated from Real Retail Trade Index with base 2013=100.

Retail Sales in Norway increased 1.60 percent in February of 2026 over the same month in the previous year. This dataset provides - Norway Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.