Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.

It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.

Description:

Ultra Beauty Products Dataset offers detailed insights into Ulta Beauty's wide range of beauty products. This dataset includes product URLs, titles, SKUs, high-quality images, pricing, availability, and customer reviews, making it an essential resource for e-commerce analytics, competitor research, and digital marketing.

Leverage this dataset to optimize product listings, analyze market trends, and enhance customer engagement.

For access to more beauty and cosmetics datasets, explore the Beauty and Cosmetics Data Collection and power your data-driven strategies today.

Fields:

• url: Product URL on the Ultra Beauty website.
• title: Name of the beauty product.
• sku: Stock Keeping Unit identifier.
• productID: Unique identifier for each product.
• main_image: URL of the main product image.
• price: Retail price of the product.
• currency: Currency used for the price (e.g., USD).
• product_variant: Variant of the product (e.g., size, color).
• summary: Short description or summary of the product.
• raw_summary: Raw, unprocessed summary text of the product.
• availability: Availability status (e.g., in stock, out of stock).
• primary_category: Main category of the product.
• category1: Primary sub-category.
• category2: Secondary sub-category (if applicable).
• breads: Breadth of product categories (if applicable).
• images: Additional product images (URLs).
• description: Detailed description of the product.
• ingredients: Ingredients used in the product.
• raw_ingredients: Raw, unprocessed ingredients list.
• details: Additional product details.
• raw_details: Raw, unprocessed details.
• how_to_use: Instructions on how to use the product.
• raw_how_to_use: Raw, unprocessed usage instructions.
• avg_rating: Average customer rating (out of 5 stars).
• review_count: Number of customer reviews.
• reviews: Customer reviews (text).
• uniq_id: Unique identifier for the record.
• scraped_at: Timestamp when the data was scraped.
  

Source:

The dataset is sourced from Ultra Beauty's product catalog.

Usage:

Ideal for analyzing product trends, pricing strategies, customer preferences, and market research in the beauty industry.

Buy Amazon datasets and get access to over 300 million records from any Amazon domain. Get insights on Amazon products, sellers, and reviews.

The Free Company Data Product is a downloadable data snapshot containing basic company data of live companies on the register. http://download.companieshouse.gov.uk/en_output.html

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The dataset offers a comprehensive view of grocery inventory, covering 990 products across multiple categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. It includes crucial details about each product, such as its unique identifier (Product_ID), name, category, and supplier information, including Supplier_ID and Supplier_Name. This dataset is particularly valuable for businesses aiming to optimize inventory management, sales tracking, and supply chain efficiency.

Key inventory-related fields include Stock_Quantity, which indicates the current stock level, and Reorder_Level, which determines when a product should be reordered. The Reorder_Quantity specifies how much stock to order when inventory falls below the reorder threshold. Additionally, Unit_Price provides insight into pricing, helping businesses analyze cost trends and profitability.

To manage product flow, the dataset includes dates such as Date_Received, which tracks when the product was added to the warehouse, and Last_Order_Date, marking the most recent procurement. For perishable goods, the Expiration_Date column is critical, allowing businesses to minimize waste by monitoring shelf life. The Warehouse_Location specifies where each product is stored, facilitating efficient inventory handling.

Sales and performance metrics are also included. The Sales_Volume column records the total number of units sold, providing insights into consumer demand. Inventory_Turnover_Rate helps businesses assess how quickly a product sells and is replenished, ensuring better stock management. The dataset also tracks the Status of each product, indicating whether it is Active, Discontinued, or Backordered.

The dataset serves multiple purposes in inventory management, sales performance evaluation, supplier analysis, and product lifecycle tracking. Businesses can leverage this data to refine reorder strategies, ensuring optimal stock levels and avoiding stockouts or excessive inventory. Sales analysis can help identify high-demand products and slow-moving items, enabling better decision-making in pricing and promotions. Evaluating suppliers based on their performance, pricing, and delivery efficiency helps streamline procurement and improve overall supply chain operations.

Furthermore, the dataset can support predictive analytics by employing machine learning techniques to estimate reorder quantities, forecast demand, and optimize stock replenishment. Inventory turnover insights can aid in maintaining a balanced supply, preventing unnecessary overstocking or shortages. By tracking trends in sales, businesses can refine their marketing and distribution strategies, ensuring sustained profitability.

This dataset is designed for educational and demonstration purposes, offering fictional data under the Creative Commons Attribution 4.0 International License. Users are free to analyze, modify, and apply the data while providing proper attribution. Additionally, certain products are marked as discontinued or backordered, reflecting real-world inventory dynamics. Businesses dealing with perishable goods should closely monitor expiration and last order dates to avoid losses due to spoilage.

Overall, this dataset provides a versatile resource for those interested in inventory management, sales analysis, and supply chain optimization. By leveraging the structured data, businesses can make data-driven decisions to enhance operational efficiency and maximize profitability.

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.

Gain insights into Amazon’s beauty and personal care market with this comprehensive Amazon Beauty Products Dataset. Covering 47,000 records across skincare, haircare, and makeup, this dataset provides full ingredient lists, product descriptions, pricing, and availability. Ideal for researchers and businesses focused on ingredient transparency, beauty trend analysis, and competitive market insights. Perfect for applications in ingredient research, product development, and e-commerce analysis.

Access a rich Amazon Beauty & Cosmetics dataset with over 200,000+ product records, including detailed ingredients.
Explore more on our Beauty & Cosmetics Data page or view the full Amazon Beauty Dataset

Walmart product dataset featuring detailed ingredient information across categories like beauty, food, personal care, and more.
View Dataset →

The dataset includes the following fields:

• ASIN: Unique Amazon product identifier.
• Product Name and Description: Full titles and descriptions of each product.
• Price and Availability: Current pricing and stock status.
• Categories: Product type classification (e.g., skincare, haircare, makeup).
• Ingredients: Complete ingredient lists, ensuring transparency about product composition.
• Images: High-quality product images.
• Brand and Manufacturer Information: Details of the brand and manufacturer.
• Customer Ratings and Reviews: User-generated content for understanding product popularity and performance.

This dataset is invaluable for:

• Ingredient Analysis: Understanding popular ingredients in beauty products.
• Market Research: Analyzing trends in beauty products, such as ingredient types and product categories.
• Competitive Analysis: Assessing product offerings by brand, price, and ingredients.

Whether you’re focused on skincare, haircare, makeup, or other beauty categories, this dataset provides in-depth information for deep analysis. For any custom requirements or additional data needs, please feel free to reach out.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

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Experience a seamless journey with Oxylabs:

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• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Fashion products dataset list from athleta gap. last extracted on 26 Dec 2021.

Crawl Feeds ready to download fashion datasets

Gain extensive insights with our Amazon datasets, encompassing detailed product information including pricing, reviews, ratings, brand names, product categories, sellers, ASINs, images, and much more. Ideal for market researchers, data analysts, and eCommerce professionals looking to excel in the competitive online marketplace. Over 425M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more

Explore our extensive Amazon Product Dataset, featuring detailed information on prices, ratings, sales volume, and more.

Utilize our Costco products dataset to gain a comprehensive view of new products, categories, pricing, and customer feedback. You can acquire the full dataset or tailor it to fit specific requirements.

Popular use cases include identifying inventory shortages and pinpointing high-demand items, analyzing customer sentiment, and crafting pricing strategies by comparing similar products and categories with your competitors.

The Costco dataset may include these data points: category, product name, description, images, features, price, specifications, review count, review score, review texts, and more. Subsets are available by categories and specific data points.

Whether you're building an eCommerce dashboard, researching market trends, or prototyping beauty intelligence tools — this dataset is a perfect place to start.

Interested in Live Tracking or API?

This dataset represents just a snapshot of what we track in real time at https://beautyfeeds.io/" target="_new" rel="noopener" data-start="2004" data-end="2041">BeautyFeeds:

• Monitor price & stock changes daily or weekly

• Track products from major retailers like Sephora, Ulta, Nykaa, Amazon, and more

• Access via export or live API

• Filter by brand, country, or category

• Assign custom URLs for targeted scraping

👉 Learn more and get 500 free credits at https://beautyfeeds.io/" target="_new" rel="noopener" data-start="2342" data-end="2382">BeautyFeeds.io

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.

This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

2. Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.

3. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can obtain relevant ecommerce data swiftly and consistently.

4. Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.

5. Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.

Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.

To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

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

About this Dataset

Gymboree fashion products dataset is extracted by crawl feeds team using in-house extraction tools. Last extracted on 19 sept 2022. Total records: 300

Category

Product Lists

Keywords

fashion datasets,fashion products data,retail datasets,ecommerce datasets

Row Count

395

Price

Free

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

About this Dataset

Sephora beauty products dataset. Crawl feeds team extracted around 8000 products for reasearch and analysis purposes.

Category

Product Lists

Keywords

ecommerce,Products,Beauty

Row Count

7956

Price

$160.00