To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

• 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied

Buy eCommerce leads from our eCommerce leads database today. Reach out to eCommerce companies to expand your business. Now is the time to buy eCommerce leads and start running a campaign to attract new customers. We provide current and accurate information that will assist you in achieving your goals.

Our database is made up of highly valuable and interested leads who are ready to make online purchases. You can always filter our data and choose the database that best meets your needs if you need eCommerce leads based on industry.

We have millions of eCommerce data ready to go no matter where you are. We’ve acquired hundreds of clients from all over the world over the years and delivered data that they’re happy with.

We were able to do so by obtaining data from various locations around the world. As a result, our database is widely accessible, and anyone can use it from any location on the planet. Please contact us if you want the best eCommerce leads .

We sell eCommerce leads that can be filtered by industry. We know what you’re going through and what you’ll need for your next project. As a result, we’ve compiled a list of eCommerce leads that are exactly what you require. With the most potential data we provide, you can grow your business and achieve your business goals. All of our eCommerce leads are generated professionally, with real people – not bots – entering data.

We’re a leading brand in the industry because we source data from the most well-known platforms, ensuring that the information you receive from us is accurate and reliable. That’s especially true because we verify each and every piece of information in order to provide you with yet another benefit in your life.

The majority of our customers have had success with the information we’ve provided. That is why they keep contacting us for our services. You can count on our business-to-business eCommerce sales leads. Contact us to work with one of the most effective lead generation companies in the industry, which has already helped thousands of potential members achieve success.

Every month, we update our eCommerce store sales leads in order to provide our clients with the most accurate data possible. We have a team of professionals who strive for excellence when it comes to gathering the right leads to ensure you get the number of sales you need. Our experts also double-check that all of the sales data we receive is genuine and accurate.

The accuracy of our eCommerce database is why the majority of our clients choose us. Furthermore, we offer round-the-clock support to provide on-demand solutions. We take care of everything so you can spend less time evaluating our product database and more time becoming one of them.

Comprehensive dataset of 13 Shop supermarket furniture stores in California, 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.

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.

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

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

Comprehensive dataset of 8 Shop supermarket furniture stores in Texas, United States as of August, 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.

Comprehensive dataset of 1 Shop supermarket furniture stores in Nevada, United States as of August, 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.

Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.

• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"

Gain exclusive access to verified Shopify store owners with our premium Shopify Users Email List. This database includes essential data fields such as Store Name, Website, Contact Name, Email Address, Phone Number, Physical Address, Revenue Size, Employee Size, and more on demand. Leverage real-time, accurate data to enhance your marketing efforts and connect with high-value Shopify merchants. Whether you're targeting small businesses or enterprise-level Shopify stores, our database ensures precision and reliability for optimized lead generation and outreach strategies. Key Highlights: ✅ 3.9M+ Shopify Stores ✅ Direct Contact Info of Shopify Store Owners ✅ 40+ Data Points ✅ Lifetime Access ✅ 10+ Data Segmentations ✅ FREE Sample Data

Comprehensive dataset of 2 Shop supermarket furniture stores in Washington, 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.

This feature layer provides access to OpenStreetMap (OSM) shops data for North America, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).In OSM, a shop is a place selling retail products or services, such as a supermarket, bakery, or florist. These features are identified with a shop tag. There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on a feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

Note: updates to this beta layer are currently paused while we sync new versions of the OSM layers for Europe.This feature layer provides access to OpenStreetMap (OSM) shops data for Europe, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).In OSM, a shop is a place selling retail products or services, such as a supermarket, bakery, or florist. These features are identified with a shop tag. There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on the feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

United States Retail Sales: 2012p: ORS: NR: Electronic Shop & Mail-order Houses data was reported at 54.486 USD bn in Jun 2018. This records an increase from the previous number of 53.567 USD bn for May 2018. United States Retail Sales: 2012p: ORS: NR: Electronic Shop & Mail-order Houses data is updated monthly, averaging 21.861 USD bn from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 54.486 USD bn in Jun 2018 and a record low of 11.041 USD bn in Mar 2002. United States Retail Sales: 2012p: ORS: NR: Electronic Shop & Mail-order Houses 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 2018: 2012 Price.

This specialized cannabis dispensary POI data offers a detailed mapping of store locations across the United States. Industry investors, market researchers, and strategic planners can leverage precise dispensary location data to understand market distribution, identify expansion opportunities, and develop targeted strategies in the emerging cannabis retail sector.

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 cannabis location intelligence landscape.

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

Why Choose LocationsXYZ for Cannabis Dispensary Data? At LocationsXYZ, we: -Deliver cannabis POI data with 95% accuracy -Refresh store locations every 30, 60, or 90 days to ensure the most recent information -Create on-demand dispensary datasets tailored to your specific needs -Handcraft boundaries (geofences) for cannabis outlet locations to enhance accuracy -Provide dispensary POI data and polygon data in multiple file formats

Unlock the Power of Cannabis Location Intelligence With our point-of-interest data for dispensary locations, you can: -Perform thorough market analyses for cannabis retail expansion -Identify the best locations for new dispensary stores -Gain insights into consumer behavior in cannabis markets -Achieve an edge with competitive intelligence in the marijuana retail sector

LocationsXYZ has empowered businesses with geospatial insights and cannabis 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 dispensary POI data.

Comprehensive dataset of 3 Shop supermarket furniture stores 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.

Malaysia Retail Shop Space Occupancy data was reported at 12,613,830.000 sq m in 2017. This records an increase from the previous number of 11,982,119.000 sq m for 2016. Malaysia Retail Shop Space Occupancy data is updated yearly, averaging 5,828,973.500 sq m from Dec 1992 (Median) to 2017, with 26 observations. The data reached an all-time high of 12,613,830.000 sq m in 2017 and a record low of 633,133.000 sq m in 1992. Malaysia Retail Shop Space Occupancy data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB095: Office & Retail Shop Space Statistics: Kuala Lumpur (Annual).

The shops database is a record of a comprehensive survey of all shops in Edinburgh to provide a long-term perspective on retail change in the city.

Weekly Issue #68 (Store Closings through January 15, 2019) This list of future store closings announced during 09/14/2018 – 09/21/2018 includes Shop 'n Save (20 locations closing), Aldi (1), Bed Bath & Beyond (1), GNC (1) locations from 28 other companies. Each listing includes the projected effective date and the precise location of the closing, including geo-coordinates. Please contact us for access to the entire store closing database, or to learn more about how to stream store closings in real-time via our new API.

Locations of Hardware Stores, which are deemed essential following hurricanes or other disaster scenarios.This dataset is fed from revenue with weekly updates