Retail Sales in the United States increased 4.30 percent in September of 2025 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.

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.

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 %.

In 2024, total retail and food service sales reached **** trillion U.S. dollars for the first time in the United States. This is more than **** times the sales numbers that were generated in 1992, not adjusting for inflation. Leading retailers and store types In 2023, the leading food and grocery retailer in the United States was by far Walmart, which generated sales numbers of close to *** billion U.S. dollars that year. The Kroger Co., Costco Wholesale Club, and Ahold Delhaize were also among the top U.S. retailers. With a grocery market share of almost ** percent, the supermarket was the top store type in 2018. The warehouse clubs and superstores category stood in second place, accounting for almost a quarter of the U.S. market. Consumer habits The American consumer made an average of a little more than *** and a half trips to the grocery store per week in 2023. The average amount of trips has noticeably decreased, compared to a decade earlier. In recent times, online grocery shopping has also become an option for consumers. The concept is projected to grow considerably in the coming years, reaching roughly *** billion U.S. dollars’ worth of sales numbers in the United States by 2024.

This dataset contains simulated retail transaction data from various branches in Jordan. It includes information about customer demographics, purchase details, product categories, and payment methods. The data is designed to reflect typical retail transactions, capturing customer behavior and preferences across different regions and professions.

This dataset is an good resource for data mining and data analysis training, as it encompasses a diverse range of trends and patterns that can be uncovered through analysis. With detailed customer demographics, purchase behavior, product preferences, and payment methods across various branches, it provides a rich ground for exploring consumer insights and sales performance. The inclusion of business rules and realistic trends such as income-based spending patterns, branch-specific gender preferences, and payment method preferences ensures that trainees can practice identifying and interpreting meaningful patterns in the data. Furthermore, the dataset's comprehensive nature, with data points spread over several years and incorporating fluctuations due to external factors like the COVID-19 pandemic, allows for the development of robust analytical skills, making it an ideal tool for anyone looking to enhance their expertise in data mining and analysis.

Table of Fields

Detailed Descriptions

• order_id: A unique identifier assigned to each order, used to distinguish different transactions.
• branch_name: The name of the branch where the transaction occurred, indicating the geographic location.
• is_customer: A boolean value (TRUE or FALSE) indicating whether the individual making the purchase is a registered customer of the store.
• customer_id: A unique identifier assigned to each customer, used to track customer-specific transactions and demographics.
• customer_profession: The profession of the customer, providing insight into the customer's job and potential buying power.
• customer_income: The monthly income of the customer, expressed in Jordanian Dinar (JOD), used to analyze spending patterns relative to income.
• purchase_date: The date on which the purchase was made, used to track sales over time.
• purchase_time: The specific time at which the purchase was made, providing information on peak shopping hours.
• product_line: An identifier for the product purchased, linked to the product catalog for detailed product information.
• unit_price: The price per unit of the product purchased, in Jordanian Dinar (JOD).
• quantity: The number of units of the product purchased in the transaction.
• total_price: The total price for the quantity of products purchased, calculated as unit_price multiplied by quantity.
• tax_amount: The tax amount ap...

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

Retail Sales in China increased 2.90 percent in October of 2025 over the same month in the previous year. This dataset provides - China Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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...

Retail Sales in Japan increased 1.70 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Japan Retail Sales YoY - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context The Challenge - One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales – the challenge is to predict which departments will be affected and to what extent.

Content You are provided with historical sales data for 45 stores located in different regions - each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.

Within the Excel Sheet, there are 3 Tabs – Stores, Features and Sales

Stores Anonymized information about the 45 stores, indicating the type and size of store

Features Contains additional data related to the store, department, and regional activity for the given dates.

Store - the store number Date - the week Temperature - average temperature in the region Fuel_Price - cost of fuel in the region MarkDown1-5 - anonymized data related to promotional markdowns. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA CPI - the consumer price index Unemployment - the unemployment rate IsHoliday - whether the week is a special holiday week Sales Historical sales data, which covers to 2010-02-05 to 2012-11-01. Within this tab you will find the following fields:

Store - the store number Dept - the department number Date - the week Weekly_Sales - sales for the given department in the given store IsHoliday - whether the week is a special holiday week The Task Predict the department-wide sales for each store for the following year Model the effects of markdowns on holiday weeks Provide recommended actions based on the insights drawn, with prioritization placed on largest business impact

China Commodity Retail: YoY: Year to Date: CW: Chinese Herbal & Traditional Medicine data was reported at 21.300 % in Dec 2009. This records an increase from the previous number of 20.500 % for Nov 2009. China Commodity Retail: YoY: Year to Date: CW: Chinese Herbal & Traditional Medicine data is updated monthly, averaging 14.300 % from Jan 2009 (Median) to Dec 2009, with 12 observations. The data reached an all-time high of 21.300 % in Dec 2009 and a record low of 9.300 % in Jan 2009. China Commodity Retail: YoY: Year to Date: CW: Chinese Herbal & Traditional Medicine 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: Retail Sales of Consumer Goods: Above Designated Size Enterprise: by Commodity .

Russia Retail Sales Volume Index: Year to Date: Same Period PY=100: Common Furniture data was reported at 102.470 Same Period PY=100 in Dec 2018. This records a decrease from the previous number of 102.840 Same Period PY=100 for Sep 2018. Russia Retail Sales Volume Index: Year to Date: Same Period PY=100: Common Furniture data is updated quarterly, averaging 102.335 Same Period PY=100 from Mar 2009 (Median) to Dec 2018, with 40 observations. The data reached an all-time high of 110.400 Same Period PY=100 in Dec 2012 and a record low of 89.800 Same Period PY=100 in Jun 2015. Russia Retail Sales Volume Index: Year to Date: Same Period PY=100: Common Furniture 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.RJB003: Retail Sales Volume Index: Quarterly.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

Russia Average Monthly Wages: Year to Date: OKVED2: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data was reported at 35,015.060 RUB in Dec 2018. This records an increase from the previous number of 34,733.940 RUB for Sep 2018. Russia Average Monthly Wages: Year to Date: OKVED2: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data is updated quarterly, averaging 32,708.400 RUB from Mar 2017 (Median) to Dec 2018, with 8 observations. The data reached an all-time high of 35,015.060 RUB in Dec 2018 and a record low of 30,145.280 RUB in Mar 2017. Russia Average Monthly Wages: Year to Date: OKVED2: Wholesale & Retail Trade; Repair of Motor Vehicles & Motorcycles data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GC007: Average Monthly Wages: by Activity: Quarterly and Annual.

Retail Sales in Chile increased 8.40 percent in October of 2025 over the same month in the previous year. This dataset provides - Chile Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Internet sales have played an increasingly significant role in retailing. In 2025, e-commerce accounted for over ***percent of retail sales worldwide. Forecasts indicate that by 2030, the online segment will make up ***percent of total global retail sales. Retail e-commerce Online shopping has grown steadily in popularity in recent years. In 2024, global e-commerce sales amounted to over ************ U.S. dollars, a figure expected to approach * trillion U.S. dollars by 2030. Digital development boomed during the COVID-19 pandemic, generating unprecedented e-commerce growth in various economies across the globe. This trend correlates strongly with the constantly improving online access, especially in "mobile-first" online communities, which have long struggled with traditional commercial fixed broadband connections due to financial or infrastructure constraints but enjoy the advantages of cheap mobile broadband connections. M-commerce on the rise The order share of online shopping via smartphones and tablets now outperforms traditional e-commerce via desktop computers. As such, e-retailers around the world have caught up in mobile e-commerce sales. Online shopping via smartphones is particularly prominent in Asia. By the end of 2023, South Korea was the top digital market based on the percentage of the population that had purchased something by phone, with nearly ** percent having made a weekly mobile purchase. Malaysia, UAE, and Turkey completed the top of the ranking.

The median annual earnings for full-time employees in the retail and wholesale trade sector in the United Kingdom was 33,158 British pounds in 2025, compared with 32,202 pounds in the previous year.

Annual data on the average retail sales of electricity in the U.S. Data organized by state and by provider,i.e., total electric industry, full-service providers, and energy-only providers. Annual time series extend back to 1990. Based on Form EIA-861 data.