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.

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

Leverage advanced location data from high-quality geospatial data covering patterns, behaviours, and trends across diverse industries. With accurate insights from multiple sources, our solutions empower businesses in retail, logistics, real estate, finance, and urban planning to optimize operations, enhance decision-making, and drive strategic growth.

Key use cases where Location Data has helped businesses : 1. Optimize Logistics & Route Planning : Streamline delivery routes, reduce transit times, and enhance operational efficiency with precise location intelligence. 2. Enhance Market Positioning & Competitor Insights : Identify high-traffic zones, analyse competitor locations, and fine-tune business strategies to maximize market presence. 3. Transform Navigation & EV Infrastructure : Power navigation systems, real-time travel recommendations, and EV charging station mapping for seamless location-based services. 4. Enhance Urban & Retail Site Selection : Identify optimal locations for stores, warehouses, and infrastructure investments with in-depth spatial data and demographic insights. 5. Strengthen Spatial Analysis & Risk Management : Leverage advanced geospatial insights for disaster preparedness, public health initiatives, and land-use optimization.

Graph and download economic data for Retail Sales: Supermarkets and Other Grocery (Except Convenience) Stores (MRTSMPCSM44511USN) from Feb 2001 to Apr 2025 about groceries, retail trade, sales, retail, and USA.

The number of food and beverage retailers in the United States in 2022 amounted to about 162,000. Supermarkets and other grocery retailers were the most represented, numbering around 66,000, followed by beer, wine, and liquor stores numbering more than 36,600. Employment size Concerning the number of employees each retailer has, most of them fall on each end of the spectrum. Some retailers have less than five employees, relating them to smaller convenience stores. Other retailers have over 500 employees, which could be connected to bigger supermarkets chains or warehouse stores. Evolution of grocery shopping Due to inflation and global events, grocery shopping sales have grown exponentially across the years. Walmart, and online store Amazon are the leading retailers in the United States, which also shows the rapid expansion of online grocery shopping since the pandemic, and it is projected to continue growing. Traditional supermarkets are nevertheless not out of the picture, since shoppers still prefer to get groceries at a physical store.

The Supplemental Nutrition Assistance Program (SNAP) is a United States Department of Agriculture (USDA) Food and Nutrition Service (FNS) program that helps low-income families and individuals buy healthy food. In Michigan, SNAP benefits are available through the Food Assistance Program (FAP), which is administered by the Michigan Department of Health and Human Services (MDHHS) and through MI Bridges. Participants in the program receive SNAP funds on an Electronic Benefits Transfer (EBT) card known as the "Michigan Bridge card", which works like a debit card. SNAP funds can be used to purchase nutritious fruit, vegetables, meat, dairy, bread and other products from participating retailers. Many different types of retailers accept the MI Bridges card, including grocery stores, convenience stores, farmers' markets, and more. Please see the MDHHS Food Assistance webpage or log into MI Bridges to learn about eligibility and to apply for the program.

This SNAP Retailer dataset includes records from the USDA SNAP Retailer Location dataset that have geographical latitude and longitude coordinates located within the City of Detroit Boundary. A few retailers located outside of Detroit may be included in this dataset if the latitude and longitudinal coordinates provided in the USDA dataset fall within the City of Detroit Boundary. The data is updated every 2 weeks.

Each record in the dataset contains data about a retail location, including the retailer's name, address, and whether they participate in the SNAP Healthy Incentive program. Retailer Type definitions are available from the USDA SNAP Store Type Definitions webpage and include convenience stores, farmers and markets, grocery stores, specialty stores, super stores, supermarkets, and restaurant meals programs.

Information about the federal program and data is available from the USDA/FNS at the Supplemental Nutrition Assistance Program (SNAP) website and the SNAP Retailer Data webpage.

In the dynamic retail industry, access to accurate and comprehensive data is not just an advantage – it's a necessity. dataplor's Global Independent & Multi-National Retail Locations Dataset offers a panoramic view of the worldwide retail landscape, empowering businesses with the knowledge they need to thrive.

Data Points for Precision:

-Retailer Profiles: Detailed information on both independent shops and multinational stores, including official names, and unique identifiers.

-Business Classification: Precise categorization by industry (e.g., apparel, electronics, grocery) and business model (e.g., independent boutique, single-location retailer), ensuring granular insights.

-Location Precision: Exact street addresses, geographic coordinates (latitude and longitude), and operational status (open/closed) for precise mapping and analysis.

-Store Attributes: Comprehensive details such as years in operation, and other relevant attributes to gauge market presence and potential.

Empowering Use Cases:

-Market Entry and Expansion: Identify untapped markets, assess the competitive landscape, and pinpoint optimal locations for new store openings or expansions.

-Competitive Benchmarking: Gain deep insights into competitor strategies, store formats, and geographic trends to inform your own business decisions.

-Targeted Marketing and Promotions: Develop hyper-targeted campaigns based on location demographics and competitor proximity.

-Supply Chain Optimization: Streamline inventory management, distribution logistics, and last-mile delivery routes by understanding store locations and demand patterns.

-Investment and Risk Analysis: Evaluate potential investment opportunities in the retail sector by assessing market saturation, growth potential, and risk factors associated with specific locations.

By providing non-PII mobility data paired with the most comprehensive location data, our product ensures businesses can act with confidence while maintaining data privacy standards.

dataplor's datasets include 55+ attributes such as:

• Unique, Static dataplor ID
• Business Name
• Main/Sub/Business Categories
• Chain ID/Name
• Home Address
• Neighborhood
• City, State, Postal, Country
• Latitude/Longitude
• Open/Closed Status
• Visit Counts
• Confidence Scoring
• First Opened
• Popularity Indices
• Sentiment Indices, and more

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences.

Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics.

This statistic shows the average monthly revenue of retail stores worldwide as of 2018, by retail segment. As of 2018, alcoholic beverage retailers had the highest average monthly revenue amounting to about 51,940 U.S. dollars. In comparison, office supply retailers generated 22,090 U.S. dollars in monthly revenue.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

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
   ...

Graph and download economic data for Retail Sales: Food and Beverage Stores (MRTSSM445USS) from Jan 1992 to Apr 2025 about beverages, retail trade, food, sales, retail, and USA.

Graph and download economic data for Monthly State Retail Sales: Food and Beverage Stores in Florida (MSRSFL445) from Jan 2019 to Feb 2025 about beverages, retail trade, food, FL, sales, retail, and USA.

Complete list of all 611 Xfinity retail store POI locations in the USA with name, geo-coded address, city, email, phone number etc for download in CSV format or via the API.

Physical retail stores were still the most popular locations for consumers looking to shop for Thanksgiving in the United States. In a 2024 survey, 73 percent of respondents picked grocery stores as their preferred locations for Thanksgiving shopping. Mass retailers and club stores were the other two most selected options among Thanksgiving shoppers that year.

United States - Retail Sales: Grocery Stores was 69314.00000 Mil. of $ in February of 2025, according to the United States Federal Reserve. Historically, United States - Retail Sales: Grocery Stores reached a record high of 78601.00000 in December of 2024 and a record low of 25748.00000 in February of 1993. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Retail Sales: Grocery Stores - last updated from the United States Federal Reserve on July of 2025.

In 2018, 65 percent of consumers in the United States stated that they would permit a location-based retail service that would send alerts reminding them to redeem an offer or loyalty reward. However, consumers were not open to all forms of location-based retail services, with only four percent stating that they would allow a retailer to track their location when not using the retailer's app.

Retail Store Item Detection

## Overview

Retail Store Item Detection is a dataset for object detection tasks - it contains Groceries annotations for 3,946 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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!