We started with 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, we 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 few more examples that meet or come very close to the full-service grocery store criteria. Here’s the explanation from OP regarding how they came to create 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.”We 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.

Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

Why Choose Success.ai for Retail Store Data?

Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

Comprehensive Use Cases for Retail Store Data:

1. Market Entry and Expansion:

Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

1. Personalized Marketing Campaigns:

Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

1. Competitive Benchmarking:

Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

1. Supplier and Vendor Selection:

Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

1. Customer Engagement and Retention:

Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

APIs to Amplify Your Results:

Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

Tailored Solutions for Industry Professionals:

Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

What Sets Success.ai Apart?

70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

Empower Your Business with Success.ai:

Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

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Business location data for Maptitude mapping software are from Caliper Corporation and contain point locations for businesses.

Massive POI database covering 6 million locations across 11 industries in the US and Canada. Includes 40+ rich data attributes for each location. Empowers data-driven decision-making across various sectors, from retail to healthcare, with high-quality, diverse location intelligence.

GapMaps curates up-to-date and high-quality POI datasets tracking store openings and closures for leading retail brands in Indonesia across 16 categories. Get the insights you need to make more accurate and informed business decisions or incorporate POI data into your location services applications.

Location includes a wealth of point of interest information, complete with detailed metadata. This helps businesses in various industries—from retail and logistics to fintech and quick-service restaurants—to make data-driven decisions.

dataplor’s Point of Interest (POI) data offers a rich set of 55+ attributes that provide in-depth insights into each location.

Key data attributes include:

• Business name
• Business Category
• Product Category
• Address
• Latitude and longitude
• NAIC
• SIC
• H3 Index
• Phone number
• Website
• Opening hours
• Visitation metrics

Data for the online SNAP Retailer Locator application. The data is a list of all currently authorized retailers in the USDA SNAP program. The data is updated every 2 weeks. The data contains the retailer's name, address, lat/long, store type classification and if they participate in the SNAP Healthy Incentive program.The data structure was updated in September 2023.

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

MealMe provides grocery and retail POI and SKU-level product data, including prices, from the top 100 retailers across the USA and Canada. Our real-time data empowers businesses with insights for location and business strategies ensuring accurate and actionable intelligence

For any given market, we identify the major and regional shopping centres and, every month, update our dataset of the tenants in those centres, and tag the appropriate categories (eg Clothing Retailer, Restaurant, Bank etc).

Major firms like Stockland REIT and GapMaps leverage this data to inform:

• Better leasing decisions, such as identifying brands that are expanding/reducing their presence.
• Tenant mix analysis
• Shopping centre valuations
• New development location strategy
• Centre competitor analysis

We have data available off-the-shelf for a number of major markets, and can create new market datasets as quickly as 3-4 weeks.

Why work with us? - Clean, comprehensive and credible datasets - 61% cost savings compared to traditional data sourcing and processing methods - 89% time savings compared to traditional data sourcing and processing methods - Customizable datasets to your needs

Detailed store polygon data for household appliances, furniture, electronics, and related stores in the US and Canada. Essential for retail sector analysis, market strategies, and competitive intelligence in the home goods industry.

DRAKO's Building Footprint Data empowers businesses with detailed building insights. Utilize our extensive dataset, which includes: Building Footprints, Store Location Data, Point of Interest (POI) Data, and Places Data, to find relevant locations for decision-making and operational strategies.

Retail Scanner Data consist of weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets.

Store Demographics: Includes store chain code, channel type, and area location. Retailer names are masked to protect identity.

Weekly Product Data: For each UPC code, participating stores report units, price, price multiplier, baseline units, baseline price, feature indicator, and display indicator. Products: Weekly product data for 2.6-4.5* million UPCs including food, nonfood grocery items, health and beauty aids, and select general merchandise aggregated into 1,100 product categories store environment variables (i.e., feature and display indicators) from a subset of stores. The 1,100 product categories are categorized into 125 product groups and 10 departments. The structure matches that of the consumer panel data. All private-label goods have a masked UPC to protect the identity of the retailers.

Product Characteristics: All products include UPC code and description, brand, multipack, and size, as well as NielsenIQ codes for department, product group, and product module. Some products contain additional characteristics (e.g., flavor).

Geographies: Scanner Data from 35,000-50,000* participating grocery, drug, mass merchandiser, and other stores, covering more than half the total sales volume of US grocery and drug stores and more than 30 percent of all US mass merchandiser sales volume. Data cover the entire United States, divided into 52 major markets, and include the same codes as those used in the consumer panel data.

Retail Channels: Food, drug, mass merchandise, convenience, and liquor.

The data set contains information on retail market spot check audit purchases of tuna in airtight containers. Data are available from May 2001 to present with new data appended annually. Information includes the date, location, product type, store information where random spot check purchases were made throughout the United States and Puerto Rico. Information on purchased product allows the manufacturer, distributor or importer to track the tuna back to harvest and verify the dolphin-safe status of the tuna product.

GapMaps curates up-to-date and high-quality Point of Interest (POI) datasets tracking store openings and closures for leading retail brands across Asia and MENA. Get the insights you need to make more accurate and informed business decisions.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

Accurate store location data for the home improvement sector, covering battery and lighting stores, paint shops, and houseware retailers. Optimized for market research, supply chain management, and business expansion.

The Detroit Greenway Coalition's mission is to create, conserve, and promote greenways and green spaces in order to connect people, places, and nature. The dataset includes the location of bike retail stores in the City of Detroit.For more information please visit https://detroitgreenways.org/.This dataset replaces the previous Bike Retail Locations dataset, which is now deprecated as of August 6th, 2024.

According to the survey conducted in 2021, around 93.3 percent of the surveyed 85 key department store operators in China said they collected consumer data to better understand consumer preferences. According to the source, almost 99 percent of surveyed department store operators indicated that they have been collecting customer data by various means.

In-Store Analytics Market Valuation – 2024-2031

In-Store Analytics Market was valued at USD 1532.7 Million in 2024 and is projected to reach USD 5213.2 Billion By 2031, growing at a CAGR of 18.24% during the forecast period 2024 to 2031.

In-Store Analytics Market: Definition/ Overview

In-store analytics refers to the collection, measurement, and analysis of data related to customer behavior and store operations within a retail environment. Utilizing technologies such as sensors, cameras, and data analytics platforms, it provides insights into how customers navigate the store, their interaction with products, and overall shopping patterns. This data helps retailers understand shopper preferences, optimize store layouts, and enhance the shopping experience.

In-store analytics is applied to various aspects of retail operations. For example, it can optimize store layouts by analyzing foot traffic patterns to place high-demand products in strategic locations. Retailers also use it to monitor real-time inventory levels, ensuring popular items are stocked appropriately and reducing out-of-stock scenarios. Additionally, the data helps in personalizing marketing efforts by tracking customer behavior and tailoring promotions to increase engagement and sales.