Retail Sales in the United States decreased 0.90 percent in May 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.

Retail Sales in the United States increased 3.30 percent in May 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.

Key information about United States Retail Sales Growth

• United States Retail Sales grew 1.0 % YoY in May 2023, compared with a 1.6 % increase in the previous month.
• US Retail Sales Growth YoY data is updated monthly, available from Jan 1993 to May 2023, with an average growth rate of 4.7 %.
• 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 1,407,152.0 units in May 2023, representing a growth of 22.8 %.

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

Graph and download economic data for E-Commerce Retail Sales (ECOMSA) from Q4 1999 to Q1 2025 about e-commerce, retail trade, sales, retail, and USA.

In 2023, there were a total of **** million retail establishments throughout the United States, a number which has increased over the past couple of years. The chains with the most stores in the United States were Dollar General and Dollar Tree.

ANALYTICS IN RETAIL: With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival. Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers. Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others. DATA AVAILABILITY:  Retail Data.xlsx o This book has three sheets (Customer, Transaction, Product Heirarchy) o Customer: Customers information including demographics o Transaction: Transactions of customers o Product Heirarchy: Product information (cateogry, sub category etc...) BUSINESS PROBLEM: A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories. Create a report and display the below calculated metrics, reports and inferences. 1. Merge the datasets Customers, Product Hierarchy and Transactions as Customer_Final. Ensure to keep all customers who have done transactions with us and select the join type accordingly. 2. Prepare a summary report for the merged data set. a. Get the column names and their corresponding data types b. Top/Bottom 10 observations c. “Five-number summary” for continuous variables (min, Q1, median, Q3 and max) d. Frequency tables for all the categorical variables 3. Generate histograms for all continuous variables and frequency bars for categorical variables. 4. Calculate the following information using the merged dataset : a. Time period of the available transaction data b. Count of transactions where the total amount of transaction was negative 5. Analyze which product categories are more popular among females vs male customers. 6. Which City code has the maximum customers and what was the percentage of customers from that city? 7. Which store type sells the maximum products by value and by quantity? 8. What was the total amount earned from the "Electronics" and "Clothing" categories from Flagship Stores? 9. What was the total amount earned from "Male" customers under the "Electronics" category? 10. How many customers have more than 10 unique transactions, after removing all transactions which have any negative amounts? 11. For all customers aged between 25 - 35, find out: a. What was the total amount spent for “Electronics” and “Books” product categories? b. What was the total amount spent by these customers between 1st Jan, 2014 to 1st Mar, 2014?

This statistic presents the concern of social users in the United States regarding the security of their personal data with online shopping platforms. During the May 2017 survey period, 40 percent of respondents stated that they worried very much about the security of their personal online shopping data.

United States Retail Sales: Book Stores data was reported at 1.320 USD bn in Aug 2018. This records an increase from the previous number of 661.000 USD mn for Jul 2018. United States Retail Sales: Book Stores data is updated monthly, averaging 1.022 USD bn from Jan 1992 (Median) to Aug 2018, with 320 observations. The data reached an all-time high of 2.425 USD bn in Aug 2008 and a record low of 523.000 USD mn in Apr 1992. United States Retail Sales: Book Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Furniture and Home Furnishings Stores (FH) data was reported at 10.166 USD bn in Oct 2018. This records an increase from the previous number of 9.928 USD bn for Sep 2018. United States Retail Sales: Furniture and Home Furnishings Stores (FH) data is updated monthly, averaging 7.615 USD bn from Jan 1992 (Median) to Oct 2018, with 322 observations. The data reached an all-time high of 11.636 USD bn in Dec 2017 and a record low of 3.846 USD bn in Jan 1992. United States Retail Sales: Furniture and Home Furnishings Stores (FH) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Median CV: Nonstore Retailers data was reported at 2.200 % in Mar 2025. This stayed constant from the previous number of 2.200 % for Feb 2025. United States Retail Sales: Median CV: Nonstore Retailers 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 7.500 % in Nov 2002 and a record low of 1.100 % in Jun 2018. United States Retail Sales: Median CV: Nonstore Retailers 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.

Analysis of ‘🏦 US Retail Sales Per Capita by Store Type’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-retail-sales-per-capita-by-store-type-2000-20e on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

I have added a column on the right that shows the compound annual growth rate (CGR) of per capita spending from 2000 to 2015.

source:

This dataset was created by Gary Hoover and contains around 0 samples along with Unnamed: 15, Unnamed: 9, technical information and other features such as: - Unnamed: 18 - Unnamed: 12 - and more.

How to use this dataset

• Analyze Unnamed: 4 in relation to Unnamed: 10
• Study the influence of Unnamed: 14 on Unnamed: 1
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Gary Hoover

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--- Original source retains full ownership of the source dataset ---

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

United States Retail Sales: sa: CC: ow: Clothing Stores data was reported at 16.975 USD bn in May 2018. This records an increase from the previous number of 16.376 USD bn for Apr 2018. United States Retail Sales: sa: CC: ow: Clothing Stores data is updated monthly, averaging 11.885 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 16.975 USD bn in May 2018 and a record low of 6.721 USD bn in Mar 1992. United States Retail Sales: sa: CC: ow: Clothing Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

Small specialty retail stores are influenced by broad macroeconomic variables rather than product-specific trends. Still, individual segments do respond to specific shifts in consumer preferences. In recent years, rising per capita disposable income has sustained demand throughout the retail sector. A recovery from the pandemic boosted consumer spending and encouraged consumers to return to brick-and-mortar stores. Specialty retailers were relatively unaffected by pandemic declines as high-income consumers and tobacco users, two significant markets for the industry, continued to spend. Competition from online and big-box retailers has risen, putting downward pressure on profit. More stores are expanding their online platforms to boost consumer reach and provide additional revenue streams. Rising operational costs have contributed to a slight dip in profit. Revenue for small specialty retailers is expected to swell at a CAGR of 4.0% to $68.4 billion through the end of 2025, including a hike of 2.0% in 2025 alone. Despite intensifying competition from discount department stores and online retailers, specialty retail stores have relied on serving a particular niche to remain successful. Big-box stores offer a one-stop shopping experience with lower prices for similar products. External competition has driven underperforming retailers to exit the industry, leaving nonemployers and small retail stores with low barriers to entry. Still, revenue gains have prompted the emergence of many new specialty retailers seeking to capitalize on the trend of shopping locally and broader sustainability trends. Small retailers have maintained a strong customer base by offering a unique in-store experience and high-quality products. Moving forward, small specialty retailers will continue expanding, albeit slower than in the previous five-year period. A gain in consumer spending and consumer confidence compounded by growing environmental awareness will support specialty retail store sales. Ongoing competition from large-scale retailers and declining smoking rates will mitigate specialty retailers' expansion. More consumers view consumer products, particularly luxury and nostalgic items, as sound investment options. Stores can benefit from this trend by stocking high-end goods that appeal to these consumers, focusing on popular brands. Revenue is expected to expand at a CAGR of 1.4% to $73.3 billion through the end of 2030.

To create this layer, OCTO staff used ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, staff reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a additional examples that meet, or come very close to, the full-service grocery store criteria. The explanation from Office of Planning regarding how the agency created their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”Staff also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

Global Fashion Retail Analytics Dataset

📊 Dataset Overview

This synthetic dataset simulates two years of transactional data for a multinational fashion retailer, featuring:
- 📈 4+ million sales records
- 🏪 35 stores across 7 countries:
🇺🇸 United States | 🇨🇳 China | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France | 🇪🇸 Spain | 🇵🇹 Portugal

Currencies Covered: Each transaction includes detailed currency information, covering multiple currencies:
💵 USD (United States) | 💶 EUR (Eurozone) | 💴 CNY (China) | 💷 GBP (United Kingdom)

Designed for Detailed and Multifaceted Analysis

🌐 Geographic Sales Comparison
Gain insights into how sales performance varies between regions and countries, and identify trends that drive success in different markets.

👥 Analyze Staffing and Performance
Evaluate store staffing ratios and analyze the impact of employee performance on store success.

🛍️ Customer Behavior and Segmentation
Understand regional customer preferences, analyze demographic factors such as age and occupation, and segment customers based on their purchasing habits.

💱 Multi-Currency Analysis
Explore how transactions in different currencies (USD, EUR, CNY, GBP) are handled, analyze currency exchange effects, and compare sales across regions using multiple currencies.

👗 Product Trends
Assess how product categories (e.g., Feminine, Masculine, Children) and specific product attributes (size, color) perform across different regions.

🎯 Pricing and Discount Analysis
Study how different pricing models and discounts affect sales and customer decisions across diverse geographies.

📊 Advanced Cross-Country & Currency Analysis
Conduct complex, multi-dimensional analytics that interconnect countries, currencies, and sales data, identifying hidden correlations between economic factors, regional demand, and financial performance.

Synthetic Data Advantages

Generated using algorithms, it simulates real-world retail dynamics while ensuring privacy.

• Privacy-Safe: All customer and employee data is artificially generated to ensure privacy and compliance with data protection regulations. Personal details, such as emails and phone numbers, are anonymized.
• Scalable Patterns: The data replicates real-world retail dynamics, ensuring scalability of patterns for testing algorithms and analytics models.
• Controlled Complexity: The dataset introduces intentional complexities (e.g., missing job titles, inconsistent phone number formats) to offer a more realistic and challenging exploration experience for exploratory data analysis.
• Customizable for Various Use Cases: Whether you're performing sales forecasting, employee performance analysis, or customer segmentation, this dataset offers a flexible foundation for diverse analytical tasks.

This dataset is an ideal resource for retail analysts, data scientists, and business intelligence professionals aiming to explore multinational retail data, optimize operations, and uncover new insights into customer behavior, sales trends, and employee efficiency.

This statistic shows a trend in total retail sales including food services in the United States from January 2017 to March 2025. In March 2025, U.S. retail sales had amounted to an estimated ************* U.S. dollars (not adjusted), which is an increase of *** compared to the same month one year earlier.

United States Retail Sales: Nonstore Retailers (NR) data was reported at 52.348 USD bn in Jun 2018. This records a decrease from the previous number of 55.149 USD bn for May 2018. United States Retail Sales: Nonstore Retailers (NR) data is updated monthly, averaging 20.709 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 74.619 USD bn in Dec 2017 and a record low of 5.761 USD bn in Jun 1992. United States Retail Sales: Nonstore Retailers (NR) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

Graph and download economic data for All Employees, Retail Trade (USTRADE) from Jan 1939 to Jun 2025 about establishment survey, retail trade, sales, retail, employment, and USA.