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

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.

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

This comprehensive retail point-of-interest (POI) dataset provides a detailed map of retail establishments across the United States and Canada. Retail strategists, market researchers, and business developers can leverage precise store location data to analyze market distribution, identify emerging trends, and develop targeted expansion strategies.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive retail landscape of location intelligence.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive retail store data database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail store locations -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping centers and malls, and more

Why Choose LocationsXYZ for Your Retail POI Data Needs? At LocationsXYZ, we: -Deliver POI data with 95% accuracy for reliable store location data -Refresh POIs every 30, 60, or 90 days to ensure the most recent retail location information -Create on-demand POI datasets tailored to your specific retail data requirements -Handcraft boundaries (geofences) for shopping center locations to enhance accuracy -Provide retail POI data and polygon data in multiple file formats

Unlock the Power of Retail Location Intelligence With our point-of-interest data for retail stores, you can: -Perform thorough market analyses using comprehensive store location data -Identify the best locations for new retail stores -Gain insights into consumer behavior and shopping patterns -Achieve an edge with competitive intelligence in retail markets

LocationsXYZ has empowered businesses with geospatial insights and retail location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge retail POI data and shopping center location intelligence.

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.

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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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.

Context

Nowadays, retail stores save a tremendous amount of data each day.this dataset contains historical purchase data for 5 different brand of chocolate.

Content

For the Segmentation file their is 8 columns ID a unique identifier for the customer.

Sex Biological sex (gender) of a customer. In this data set there are only 2 different options. male -->0 female -->1

Marital status Marital status of a customer.
single -->0 non-single (divorced / married) -->1

Age Integer The age of the customer

Education The level of education of the customer
other / unknown-->0 high school-->1 university-->2 graduate school-->3

Income annual income in US dollars of the customer.

Occupation Category of occupation of the customer. unemployed / unskilled -->0 skilled employee / official-->1 management / self-employed / highly qualified employee-->2

Settlement size The size of the city that the customer lives in.
small -->0 mid-sized city-->1 big city-->2

the purchase data set contains 17 columns ID: a unique identifier for the customer.

Day: Day when the customer has visited the store

Incidence: Purchase Incidence
customer has not purchased -->0 The customer has purchased -->1

Brand: Shows which brand the customer has purchased-->(1-5) No brand was purchased-->0

Quantity: Number of items bought by the customer

Last_Inc_Brand: Shows which brand the customer has purchased on their previous store visit-->(1-5) No brand was purchased-->0

Last_Inc_Quantity: Number of items bought by the customer from the product category of interest during their previous store visit

Price_1: Price of an item from Brand 1 on a particular day

Price_2: Price of an item from Brand 2 on a particular day

Price_3: Price of an item from Brand 3 on a particular day

Price_4: Price of an item from Brand 4 on a particular day

Price_5: Price of an item from Brand 5 on a particular day

Promotion_1: Indicator whether Brand 1 was on promotion or not on a particular day
There is no promotion-->0
There is promotion-->1

Promotion_2: Indicator of whether Brand 2 was on promotion or not on a particular day
There is no promotion-->0 There is promotion-->1

Promotion_3: Indicator of whether Brand 3 was on promotion or not on a particular day
There is no promotion-->0 There is promotion-->1

Promotion_4: Indicator of whether Brand 4 was on promotion or not on a particular day
There is no promotion-->0 There is promotion-->1

Promotion_5: categorical {0,1} Indicator of whether Brand 5 was on promotion or not on a particular day
There is no promotion-->0 There is promotion-->1

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

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.

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: Miscellaneous Store Retailers (MRTSSM453USS) from Jan 1992 to Apr 2025 about miscellaneous, retail trade, sales, retail, and USA.

China Chain: Specialty Store: Sales: Retail data was reported at 1,617.960 RMB bn in 2022. This records an increase from the previous number of 1,532.130 RMB bn for 2021. China Chain: Specialty Store: Sales: Retail data is updated yearly, averaging 1,306.449 RMB bn from Dec 2003 (Median) to 2022, with 20 observations. The data reached an all-time high of 1,628.529 RMB bn in 2018 and a record low of 69.343 RMB bn in 2003. China Chain: Specialty Store: Sales: Retail data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CRAH: Specialty Store.

Sky Packets – AI-Powered Retail, Sentiment & Demographic Data from LATAM

Sky Packets delivers premium, AI-derived retail analytics captured from over 2,000 physical retail locations across Mexico, Peru, Ecuador, and Colombia. Using edge-based computer vision sensors, we extract anonymized insights into foot traffic, demographic profiles (age/gender), dwell time, and emotional sentiment—providing a powerful window into real-world consumer behavior at scale.

Key Highlights:

Data Types: Retail activity, sentiment analytics, demographic estimation

Capture Method: AI-powered computer vision sensors (non-intrusive, privacy-safe)

Geographic Coverage: Mexico, Peru, Ecuador, Colombia

Delivery Formats: CSV & JSON

Frequency: Daily/Weekly/Monthly aggregation options

Use Cases: Site selection, campaign optimization, consumer trend analysis, urban planning, mobility studies, and predictive modeling

All data is aggregated and anonymized to meet strict privacy and ethical data collection standards. No cookies, mobile tracking, or personally identifiable information is used.

Ideal for hedge funds, retailers, brand strategists, market researchers, and smart city developers seeking high-quality N.A. & LATAM consumer behavior data not available through digital channels.

Get real-world visibility where it matters most—with Sky Packets retail intelligence.

InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated. UnitPrice: Unit price. Numeric, Product price per unit in sterling. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal, the name of the country where each customer resides.

Xverum’s Store Location Data offers unmatched global coverage of retail, restaurant, and business locations - spanning 230M+ verified POIs across 5000+ commercial categories in over 249 countries.

Whether you're launching a new retail concept, mapping competitor presence, or enriching your analytics platform with real-world business locations - our bulk dataset helps you unlock rich geospatial context.

What’s Included: ➡️ Store Locations & Addresses: Geocoded with latitude/longitude, city, postal code, country. ➡️ Business Metadata: Brand names, categories & subcategories (e.g., Restaurants, Grocery, Clothing). ➡️ Store Details (if available): Website, phone number, operating hours. ➡️ Structured Delivery: Available in .json via S3 bucket or other cloud storage.

🚫 No Foot Traffic or Mobility Data: Clean, static POI data for precise business intelligence use cases.

Use Cases: ✔️ Retail Site Selection & Market Expansion ✔️ Restaurant Chain Mapping & Competitive Benchmarking ✔️ POI Enrichment for Mapping Platforms & Apps ✔️ Real Estate & Urban Planning Analytics ✔️ Location-Based Targeting & Geospatial Analysis

Why Choose Xverum: ✅ 230M+ Store & Business POIs updated regularly ✅ Global coverage across 249+ countries ✅ 5000+ categories from retail and F&B to professional services ✅ Delivered in bulk only - ideal for enterprise data teams ✅ Privacy-compliant (GDPR/CCPA) & ethically sourced

Request your free sample today and discover how Xverum’s store location data can elevate your retail insights, POI mapping, or expansion planning.

This dataset includes yearlong, one-minute resolution time series profiles for the big box retail grocery stores stores simulated in Phoenix, Houston, Denver, and Minneapolis, as well as electric vehicle charging time series profiles for the various ports, charging levels, and station utilizations produced for the study "Impact of electric vehicle charging on the power demand of retail buildings", published in 2021 (https://doi.org/10.1016/j.adapen.2021.100062). Please cite as: Gilleran, M., Bonnema, E., Woods, J. et al. Impact of electric vehicle charging on the power demand of retail buildings. Advances in Applied Energy 4, (2021). https://doi.org/10.1016/j.adapen.2021.100062

Graph and download economic data for Monthly State Retail Sales: Miscellaneous Store Retailers in Illinois (MSRSIL453) from Jan 2019 to Mar 2025 about miscellaneous, IL, retail trade, sales, retail, and USA.

United States Retail Sales: sa: Department stores ex Leased Departments (DS) data was reported at 12.360 USD bn in Sep 2018. This records a decrease from the previous number of 12.454 USD bn for Aug 2018. United States Retail Sales: sa: Department stores ex Leased Departments (DS) data is updated monthly, averaging 16.813 USD bn from Jan 1992 (Median) to Sep 2018, with 321 observations. The data reached an all-time high of 19.904 USD bn in Jan 2001 and a record low of 12.325 USD bn in Nov 2016. United States Retail Sales: sa: Department stores ex Leased Departments (DS) 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. All estimates for department stores exclude leased departments.

Access the Large Target Store Products Dataset, a detailed collection of web-scraped data featuring a wide range of retail products. Discover trending categories, including groceries, home goods, electronics, clothing, beauty products, and more. Gain insights into product availability, pricing, customer ratings, and brand preferences from one of the leading retailers.

Perfect for market analysis, competitive research, and consumer behavior studies, this dataset offers valuable information to inform retail strategies and decision-making.