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.

A series of retail sales data for Great Britain in value and volume terms, seasonally and non-seasonally adjusted.

Metadata

Description:

This dataset contains 100,000 records of sales transactions from a retail business. It includes information such as product ID, transaction date, price, quantity sold, customer demographics, and payment method. This data can be used for various tasks such as sales trend analysis, customer segmentation, and demand forecasting.

Columns:

• TransactionID: Unique identifier for each sales transaction.
• ProductID: Unique identifier for each product sold.
• Date: Date when the transaction occurred.
• Price: The price of the product.
• Quantity: The quantity of the product sold in the transaction.
• CustomerID: Unique identifier for each customer.
• PaymentMethod: The payment method used (e.g., credit card, cash).
• Region: Geographical region where the transaction took place.

Potential Use Cases:

• Sales forecasting
• Product demand analysis
• Customer segmentation and behavior analysis
• Marketing insights
• ML Prediction

tags: - Sales - Retail - Transactions - E-commerce - Business Analytics - Machine Learning

licenses: - MIT

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

This is a fairly simple data set with the original data set containing just over 1000 rows of data. In the data, there's retail sales information with dates, genders, product categories, quantity, unit price, and sale amount. I created a basic dynamic dashboard to allow the user to explore the data and draw conclusions. Exploring this dashboard will show case trends and interesting information that this fictional retail store could use to help them improve. There is also a simple forecast summary that shows what the sales numbers would look like with 3% and 5% increases.

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

Retail Sales in the United States increased 3.20 percent in January of 2026 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - 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.

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.

Total sales and average weekly spending estimates for each retail sector in Great Britain in thousands of pounds.

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

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.

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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A first estimate of retail sales in value and volume terms for Great Britain, seasonally and non-seasonally adjusted.

Retail Sales - Report on Monthly Survey of Retail Sales [Report]

Total retail sales in the United States were projected to amount to *** trillion U.S. dollars in 2028, up from around * trillion U.S. dollars in 2022. These figures included e-commerce and retail sales. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. There are around ************ retail establishments in the United States. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. 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. American retailers worldwide Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business on a global scale. The success of U.S. retailers can also be seen through their performance in online retail. Amazon is a prime example of this, with the company’s sales revenue flourishing over the previous years.

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.

Key information about United States Retail Sales Growth

• United States Retail Sales grew 4.1 % YoY in Sep 2025, compared with a 4.9 % increase in the previous month.
• US Retail Sales Growth YoY data is updated monthly, available from Jan 1993 to Sep 2025, with an average growth rate of -8.8 %.
• The data reached an all-time high of 42.0 % in Apr 2021 and a record low of -15.8 % in Apr 2020.
• In the latest reports, Car Sales of US recorded 1,491,440.0 units in Aug 2025, representing a growth of 2.4 %.

CEIC calculates monthly Retail Sales: Excl. Motor Vehicles Growth from monthly Retail Sales excluding Motor Vehicle and Parts. The U.S. Census Bureau provides Retail Sales excluding Motor Vehicle and Parts in USD. Retail Sales include Food Services.

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.

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:...