A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

Retail Sales in the United States increased 0.50 percent in July 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 core point of interest dataset consists of 1M location information of retail stores in the US and Canada. The POI database includes electronic stores, supermarkets and groceries, specialty retailers, home improvement and convenience stores, and apparel and accessories shops.

A global survey from Capgemini showed that retail companies were lagging behind consumer products enterprises in the use of data. The gap was significant in the automation of processes and in data collecting: only ** percent of retailers automated data collection, against ** percent of consumer goods companies. However, one in **** organizations in both categories reported to have implemented practices involving data engineering, machine learning, and DevOps.

SNAP is the largest nutrition assistance program in the US. Understanding where SNAP dollars can be redeemed is thus a critical part of understanding food access environments.Data compiled from the USDA Food and Nutrition Service SNAP Retail locator downloaded from https://www.fns.usda.gov/snap/retailer-locatorXY coordinates for each retailer were re-verified using google maps due to geocoding inaccuracies in the USDA database. Retailers are categorized into five types of stores. “Big Box” are large commercial retail chains (superstores) that sell a range of products such as clothing, electronics, furniture, hardware, household supplies, pharmaceuticals and groceries. “Grocery” are retailers that primarily sell food and are distinguished from their counterparts by offering a wide diversity of perishable and nonperishable items including vegetables, fruit, meat, poultry, fish, bread and cereal, and dairy products. Most grocery stores are WIC approved. “Small Box” are retail store chains that sell a range standardized food products with minimal perishable options along with clothing, electronics, hardware, household supplies and pharmaceuticals. “Convenience” are retail locations that stock prepared food items, snacks, beverages, and only a very limited range of foods for home preparation. “Farmers Markets” sell agricultural produce including vegetables, fruits, meats, poultry, and dairy products usually in the form of direct farm to consumer sales. “Specialty” retailers carry limited food items focusing on a select product types such as international foods, butcheries, bakeries etc.All stores were called to identify whether they carry fresh produce and accept WIC payments. These categories are reflected in the data as well.

Graph and download economic data for Retailers Sales (RETAILSMSA) from Jan 1992 to Jun 2025 about retail trade, sales, retail, and USA.

United States Retail Sales: sa: Nonstore Retailers (NR) data was reported at 58.063 USD bn in Sep 2018. This records an increase from the previous number of 57.436 USD bn for Aug 2018. United States Retail Sales: sa: Nonstore Retailers (NR) data is updated monthly, averaging 20.741 USD bn from Jan 1992 (Median) to Sep 2018, with 321 observations. The data reached an all-time high of 58.063 USD bn in Sep 2018 and a record low of 6.018 USD bn in Mar 1992. United States Retail Sales: sa: 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 Retailers Inventories (RETAILIMSA) from Jan 1992 to May 2025 about inventories, retail, and USA.

United States RS: ARTS: Nonstore Retailers(NR) data was reported at 696.849 USD bn in 2018. This records an increase from the previous number of 629.204 USD bn for 2017. United States RS: ARTS: Nonstore Retailers(NR) data is updated yearly, averaging 255.579 USD bn from Dec 1992 (Median) to 2018, with 27 observations. The data reached an all-time high of 696.849 USD bn in 2018 and a record low of 78.501 USD bn in 1992. United States RS: ARTS: Nonstore Retailers(NR) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H003: Retail Sales: Annual Retail Trade Survey: NAICS.

United States Retail Firms: Purchases: Miscellaneous Store Retailers data was reported at 73.506 USD bn in 2022. This records an increase from the previous number of 69.171 USD bn for 2021. United States Retail Firms: Purchases: Miscellaneous Store Retailers data is updated yearly, averaging 52.152 USD bn from Dec 1992 (Median) to 2022, with 31 observations. The data reached an all-time high of 73.506 USD bn in 2022 and a record low of 26.172 USD bn in 1992. United States Retail Firms: Purchases: Miscellaneous Store 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.O020: Retail Firms: Purchases: Annual Retail Survey: NAICS 2017.

United States Retail Sales: 2009p: ORS: Miscellaneous Store Retailers data was reported at 12.963 USD bn in May 2018. This records an increase from the previous number of 12.636 USD bn for Apr 2018. United States Retail Sales: 2009p: ORS: Miscellaneous Store Retailers data is updated monthly, averaging 9.748 USD bn from Jan 1999 (Median) to May 2018, with 233 observations. The data reached an all-time high of 13.160 USD bn in Jan 2018 and a record low of 8.466 USD bn in Sep 2001. United States Retail Sales: 2009p: ORS: Miscellaneous Store Retailers data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.H006: Retail and Food Services Sales: NIPA 2013: 2009 Price.

United States RS: ARTS: Taxes: MS: Other Miscellaneous Store Retailers data was reported at 3.314 USD bn in 2018. This records an increase from the previous number of 3.194 USD bn for 2017. United States RS: ARTS: Taxes: MS: Other Miscellaneous Store Retailers data is updated yearly, averaging 2.289 USD bn from Dec 2004 (Median) to 2018, with 15 observations. The data reached an all-time high of 3.314 USD bn in 2018 and a record low of 1.929 USD bn in 2004. United States RS: ARTS: Taxes: MS: Other Miscellaneous Store Retailers data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H003: Retail Sales: Annual Retail Trade Survey: NAICS.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

MealMe provides comprehensive grocery and retail POI and 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!

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

United States RFS: Retail Sales: 2005p: Miscellaneous Store Retailers data was reported at 11.262 USD bn in May 2013. This records an increase from the previous number of 11.125 USD bn for Apr 2013. United States RFS: Retail Sales: 2005p: Miscellaneous Store Retailers data is updated monthly, averaging 9.206 USD bn from Jan 1995 (Median) to May 2013, with 221 observations. The data reached an all-time high of 11.262 USD bn in May 2013 and a record low of 6.688 USD bn in Jan 1995. United States RFS: Retail Sales: 2005p: Miscellaneous Store Retailers data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.H008: Retail and Food Services Sales: NIPA 2009: 2005 Price.

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.

Comprehensive dataset of 322 Lottery retailers in Illinois, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Gain insight into product margins through web-sourced product availability and discounting data for beauty, fashion, and household brands. Retail pricing data is sourced from dozens of US online retailers.

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
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