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.

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

This dataset provides detailed insights into retail sales, featuring a range of factors that influence sales performance. It includes records on sales revenue, units sold, discount percentages, marketing spend, and the impact of seasonal trends and holidays.

Key Features:

• Sales Revenue (USD): Total revenue generated from sales.
• Units Sold: Quantity of items sold.
• Discount Percentage: The percentage discount applied to products.
• Marketing Spend (USD): Budget allocated to marketing efforts.
• Store ID: Identifier for the retail store.
• Product Category: The category to which the product belongs (e.g., Electronics, Clothing).
• Date: The date when the sale occurred.
• Store Location: Geographic location of the store.
• Day of the Week: Day when the sale took place.
• Holiday Effect: Indicator of whether the sale happened during a holiday period.

Use Cases:

• Predictive Modeling: Build models to forecast future sales based on historical data.
• Marketing Analysis: Evaluate the effectiveness of marketing spend and discount strategies.
• Seasonal Trend Analysis: Examine how different seasons and holidays impact sales.
• Revenue Optimization: Identify strategies to optimize pricing and marketing for increased revenue.

Notes:

This dataset is synthetic and generated for analysis purposes. It reflects typical retail sales patterns and is designed to support a wide range of data science and business analytics projects.

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.

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

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 was sourced directly from Kaggle and centers on retail sales data involving 2,514 transactions. It captures multiple dimensions of each purchase, including product category, customer gender, day of the week, and revenue. It's a rich sample designed to support exploratory analysis of consumer behavior and sales trends.

The inspiration behind using this dataset lies in uncovering what drives revenue, whether it's weekend shopping patterns, gender-based preferences, or popular product types like beauty, clothing, and electronics. It’s ideal for anyone practicing Power BI dashboards, refining their data storytelling, or studying retail KPIs.

📁 Sources Dataset URL: Retail Sales on Kaggle

Includes:

.pbix file for Power BI visualization

.csv export of the raw data

.pdf snapshot of dashboard output

Screenshot for quick preview

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.

The retail industry encompasses the journey of a good or service. This typically starts with the manufacture of a product and ends with said product being purchased by a consumer from a retailer. As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Based on retail sales generated in the financial year 2023, Walmart was by far the world's leading retailer with retail revenues reaching over 648 billion U.S. dollars. U.S. companies dominate global retail Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business around the world. The domestic retail market in the United States is also very competitive, with many companies recording substantial retail sales. 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 in line with the rise of e-Commerce worldwide.

1) Data Introduction • The Retail Sales Dataset is data designed to analyze retail sales and customer behavior in a virtual retail environment, including transaction history, customer demographics, and product information.

2) Data Utilization (1) Retail Sales Dataset has characteristics that: • This dataset details retail sales and customer characteristics such as transaction ID, date, customer ID, gender, age, product category, purchase volume, unit price, total amount. (2) Retail Sales Dataset can be used to: • Customer Segmentation and Marketing Strategy: By analyzing purchase patterns by age, gender, and product category, you can use them to establish a customized marketing strategy. • Sales Trends and Inventory Management: It can be used to streamline retail operations such as inventory management and promotion planning by analyzing sales trends by period and product.

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

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

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.

Graph and download economic data for Retail Sales: Sporting Goods Stores (MRTSMPCSM45111USN) from Feb 1992 to Aug 2025 about sport, retail trade, sales, retail, goods, and USA.

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:...

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.

Graph and download economic data for Advance Retail Sales: Department Stores (RSDSELD) from Jan 1992 to Mar 2025 about leases, retail trade, sales, retail, and USA.

This report analyses retail sales volumes in the United Kingdom. The data is sourced from the Office for National Statistics (ONS), in addition to estimates by IBISWorld, and is adjusted for seasonality. The figures are averaged over each financial year (i.e., April through March) from an index of sales with a base value of 100 for the calendar year 2018. Retail sales data is collated by the ONS on a monthly basis. The retail sales index comprises consideration of food retailing, non-food retailing, and non-store retailing (e.g., mail order), albeit excluding sales of automotive fuel. Both the volume and value of UK retail sales are sensitive to: changes in consumer confidence; the propensity of consumers to make discretionary purchases, relative to household disposable income levels; the availability of consumer credit; monetary policy, whereby changes in interest rates can affect the yield on savings or facilitate consumer spending; and a number of other socio-economic factors. Seasonal factors (e.g., Christmas shopping at the tail end of a given calendar year, higher temperatures that may persuade many to pursue recreational rather than retail activities) can result in cyclical deviations from underlying retail sales trends. However, the data presented is adjusted for seasonality.

In 2021, the average retail revenue of the leading retailers located in the United States amounted to about **** billion U.S. dollars. In the same year, the average retail sales of the world's leading 250 retailers reached to approximately **** billion 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.