ZARA is one of the world's largest apparel and fashion retailers. The CrawlFeeds team has successfully extracted over 10,000 product records from ZARA USA, including titles, prices, images, availability, and more.

You can customize the dataset to match your specific needs, such as format adjustments, re-extraction, or additional data points.

If you're looking for retail data solutions, you can customize the current dataset or extract ZARA product data from other countries like Spain, the UK, and India.

Find here latest zara us products listings (https://crawlfeeds.com/datasets/download-the-complete-zara-product-dataset)

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.

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.

Mercari is japanese e-commerce company. Crawl Feeds team extracted more than 1M+records from the mercari us. Last crawled on 23 Oct 2021.

🔍 TSMPD-US-Public v1.0 – Small Merchant Product Dataset (Public Sample)

This dataset provides a public sample of structured product listings from 355,722 verified small U.S.-based merchants, containing:

~3.2 million product records

Text fields only (vendor, title, description, tags, category, last_updated)

No images or variant (SKU) data

It is designed for LLM research, product grounding, semantic commerce, and agent training.

🔐 Looking for the full dataset?

The Partner/Reserve version includes:

All products per merchant (11.9M+ total)

Product variants (67M SKUs)

Product images (54M URLs)

Store domains and product URLs

Dataset watermark for traceability

📬 To request access: email jim@tokuhn.com

This extended version is offered under a commercial or research license to ensure fair and traceable use in LLM applications.

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!

Retail Sales in the United States decreased 0.90 percent in May 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.

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.

Delve into Comprehensive WebAutomation Dataset: Amazon Best Seller Products with Global Coverage

Explore the depths of eCommerce with our expansive WebAutomation dataset, meticulously curated to provide a comprehensive overview of Amazon's best seller products. With global coverage and an extensive array of data points, including pricing data, eCommerce product details, and seller ratings, our dataset empowers businesses and researchers to extract actionable insights and drive informed decision-making.

What Sets Us Apart:

• Global Coverage: Our dataset spans across various regions and countries, offering insights into Amazon's best seller products on a global scale. Whether you're interested in market trends in North America, Europe, Asia, or beyond, our dataset has you covered.

• Rich Pricing Data: Dive into detailed pricing information for a wide range of products, enabling precise analysis of pricing strategies, competitive landscapes, and market trends. With historical pricing data, track changes over time and identify patterns to inform pricing decisions.

• Comprehensive Product Details: Gain access to a wealth of eCommerce product details, including product descriptions, specifications, images, and customer reviews. Whether you're conducting market research, competitor analysis, or product development, our dataset provides the depth of information needed to make informed decisions.

• Seller Ratings Data: Understand the reputation and performance of sellers on Amazon with our seller ratings data. Evaluate seller reliability, customer satisfaction levels, and overall trustworthiness to guide partnership decisions and enhance the customer experience.

  Use Cases:

• Market Analysis: Analyze market trends, consumer preferences, and competitive landscapes to identify growth opportunities and strategic advantages.

• Price Optimization: Utilize pricing data and historical trends to optimize pricing strategies, maximize profitability, and stay competitive in the market.

• Product Development: Inform product development efforts by leveraging comprehensive product details and customer feedback to identify gaps in the market and tailor offerings to meet customer needs.

• Partnership Evaluation: Evaluate seller ratings and performance metrics to make informed decisions when selecting partners and suppliers, ensuring a seamless and trustworthy customer experience.

  Unlock the Power of Data:

Empower your business with actionable insights derived from our WebAutomation dataset. Whether you're a market researcher, business analyst, or eCommerce professional, our dataset provides the tools and resources needed to stay ahead in today's dynamic marketplace.

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

This dataset includes information on Malian consumer and retailer preferences for dairy products under the Mali Food Security Policy Research Program (PREPOSAM) in Mali. Files include: a) data on consumer preferences; b) consumer survey protocol, and c) consumer survey questionnaire; d) data on retailer preferences; e) retailer survey protocol, and f) retailer survey questionnaire.

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets (also called supercenters), discount department stores, and grocery stores in the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in nearby Rogers, Arkansas in 1962 and incorporated under Delaware General Corporation Law on October 31, 1969. It also owns and operates Sam's Club retail warehouses. In India, Walmart operates under the name of Flipkart Wholesale.

As of July 31, 2022, Walmart has 10,585 stores and clubs in 24 countries, operating under 46 different names. Out of which we have chosen 45 stores for basic analysis.

Walmart is the world's largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in May 2022.

How Walmart uses Big Data?

• Improving Store Checkout: By using Predictive Analysis, the stores can anticipate demand at a certain week and determine how many Sales Representatives / Employees are needed.
• Managing the Steps of Supply Chain: The company optimizes the routes to the shipping dock and tracks the number of times the product is accessed before it reaches the Customer's destination. Also, it uses the data to analyze transportation lanes and routes for the company's trucks. These data help Walmart keep transportation costs down and schedule an appropriate time for drivers.
• Optimizing Product Assortment: By analyzing customer preferences and shopping patterns, Walmart accelerates the decision-making on how to maintain stocks. Big Data provides insights on new items and discontinued products.
• Personalizing Shopping Experience: With Big Data, Walmart analyzes the shopping preferences of the customers to develop a consistent and delightful shopping experience. and much more...

The IRI InfoScan dataset, created by Information Resources, Inc. (IRI)—a market research company later merged with NPD Group to form Circana—contains detailed retail sales data for food and consumer goods. It captures transaction-level data from thousands of U.S. retail outlets, including grocery stores, mass merchandisers, convenience stores, and drugstores, at the Universal Product Code (UPC) level. The dataset includes metrics such as sales volume, revenue, pricing, and product-level transactions, enabling analysis of consumer behavior and market trends. Its primary purpose is to provide market intelligence for industries like consumer packaged goods (CPG), retail, and healthcare. Use cases include tracking sales performance, evaluating marketing campaigns, and benchmarking against competitors. Researchers and government agencies (e.g., USDA) also use it to study food expenditures and price trends, often comparing it with survey data for validation. Key features include its broad retail coverage (over 2,700 outlets at peak), granular UPC-level detail, and weekly updates, making it a dynamic tool for real-time market analysis. The dataset’s proprietary nature and historical depth (dating back decades) further distinguish it, though access is restricted to authorized clients. Post-2022, it operates under Circana’s OmniMarket Core Outlets branding, retaining its role as a critical resource for retail and economic research. (Source: IRI company history, USDA ERS documentation, and Circana product updates)

Potential Applications of the Dataset:

1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview.

2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management.

3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores.

4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence.

5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base.

6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability.

The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

This specialized location dataset offers a detailed mapping of cannabis dispensaries across the United States. Industry investors, market researchers, and strategic planners can leverage precise location information 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 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 -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

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

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

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

Net-a-Porter web scraped data

  About the website

The e-commerce industry, particularly the segment focusing on luxury fashion retail, is rapidly flourishing in the Americas, predominantly in the United States. Companies such as Net-a-Porter offer an extensive range of products, merging the lines between high fashion and accessible purchasing. Online platforms are revolutionizing traditional retail approaches, allowing businesses to stay ahead amid rapidly evolving consumer… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Net.a.Porter.Product.prices.United.States.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

The Superstore Sales Dataset provides detailed transactional data from a retail superstore operating across the United States. It includes records of customer orders made between 2015 and 2019, capturing key sales, shipping, and regional details.

🧾 Key Columns: Order Date & Ship Date – Timeline of transactions and delivery

State & Region – Geographic location of the order

Category & Sub-Category – Type of product sold (e.g., Furniture, Office Supplies)

Sales – Revenue generated per item (in dollars)

Quantity – Number of units sold

Discount – Discount applied on the sale

Shipping Mode – Delivery method (Standard, First Class, Second Class, Same Day)

⚠️ Note: The dataset does not include profit or customer demographic information.

🗺️ Coverage: Time: 5 years (2015–2019)

Location: All major U.S. regions and states

Products: Multiple categories and sub-categories

Shipping: All standard delivery modes

This dataset is ideal for beginners to explore sales trends, regional performance, product analysis, and customer behavior patterns through visualizations and summary statistics.

About this Dataset

ASOS is fashion retailer. Crawl Feeds team extracted more than 100K+ fashion products from ASOS US along with 25 datapoints. Dataset available in CSV Format. Last extracted on 26 Jun 2022.

Category

Product Lists

Keywords

ecoomerce,fashion data,fashion Ecommerce data,retail

Row Count

4001

Price

$130.00