Zoro.com Product Data Sample – Explore Structured E-commerce Product Listings

This dataset is a sample extraction of product listings from Zoro.com, a leading industrial supply e-commerce platform. It provides structured product-level data that can be used for market research, price comparison engines, product matching models, and e-commerce analytics.

The sample includes a variety of products across tools, hardware, safety equipment, and industrial supplies — with clean, structured fields suitable for both analysis and model training.

Also available: Grainger Product Datasets – structured data from a top industrial supplier.

Submit your custom data requests via the Zoro products page or contact us directly at contact@crawlfeeds.com.

Ideal for previewing before requesting larger or full Zoro datasets

Use Cases:

• Building product comparison or search engines

• Price intelligence and competitor monitoring

• Product classification and attribute extraction

• Training data for e-commerce AI models

Want More?

This is a sample of a much larger dataset extracted from Zoro.com.
👉 Contact us to access full datasets or request custom category extractions.

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more. NOTICE: This is a sample of the full Amazon Product Dataset, which contains 1K examples. Follow the link to gain access to the full dataset.

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-sample.

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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The Product Catalog Data provides a comprehensive overview of products across various categories. This dataset includes detailed product titles, descriptions, barcodes, category-specific attributes, weight, measurements, and imagery. It's tailored for marketplaces, eCommerce sites, and data analysts who require in-depth product information to enhance user experience, SEO, and product categorization.

Popular Attributes:

✔ Detailed product information

✔ High-quality imagery

✔ Extensive attribute coverage

✔ Ideal for UX and SEO optimization

✔ Comprehensive product categorization

Key Information:

Rich dataset with 30+ attributes per product

Pricing: Flexible subscription models

Update Frequency: Daily updates

Coverage: Global and specific markets

Historical Data: 12 Months +

This dataset contains images of Television, Sofas, Jeans and T-shirt. It Actual raw and unstructured image data extracted from online sites.

All images are of different sites. You may also find some junk images in data for example in television dataset you will find the television remote images.

This dataset is not refined intentionally to make sure practitioners should get taste of What kind of data ML/Data Science Engineer get when they start working on any project in industry.

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

First of all, Amazon product datasets are indispensable for reverse engineering your rivals. For example, you can collect a list of keywords you already rank for or want to, and go through DataForSEO Amazon Products Database to find other sellers appearing as the top results for these terms.

Next, you can narrow down the scope of your contenders to those performing the best. To do so, you can filter out sellers who won the “Amazon’s Choice” and those whose products got listed multiple times on the first page.

Once you’ve compiled the final list of your challengers, Amazon Products Database will help you to quickly examine product titles, descriptions, prices, images, and other details that will let you grasp the main contributors to your competitors’ success. Once you’ve figured that out, you can start optimizing your product listings and pricing strategies to increase conversions.

However, the number of use cases for Amazon product data isn’t limited to competitor analysis. It can be applied to monitoring product rankings, running price comparisons, and more.

This comprehensive IKEA USA products dataset contains detailed information about thousands of authentic IKEA furniture items, home decor, and household products available in the United States market. The dataset provides complete product specifications, pricing, availability, and detailed descriptions for ecommerce analysis, price comparison, and furniture retail research.

Key Features:

• Complete IKEA USA product catalog with real pricing data
• Detailed product descriptions and specifications
• Product URLs, article numbers, and availability status
• Furniture categories including office chairs, storage solutions, outdoor furniture
• Home decor items like candle holders, planters, and textiles
• Kitchen cabinets, wardrobes, and organizational systems
• Material specifications and sustainability information
• Product dimensions, weights, and packaging details

Get Free Sample: Download your free sample dataset now to explore the data quality and structure before purchasing the complete IKEA USA products database. The free sample includes representative product entries with all key fields populated.

Applications: Perfect for furniture market analysis, home improvement research, interior design planning, competitive pricing analysis, and retail intelligence. This dataset enables businesses to understand IKEA pricing strategies, product positioning, and market trends in the home furnishing industry.

Product Categories Included: Office furniture, bedroom furniture, storage solutions, outdoor dining sets, kitchen systems, home organization products, decorative accessories, plant containers, and sustainable furniture options. All products include comprehensive details for business intelligence and market research applications.

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

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

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

Open Food Facts Database

  What is 🍊 Open Food Facts?





  A food products database

Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.

  Made by everyone

Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database.

nfinite-product-masks-sample

Version of the release: 1.0.0-alphaRelease date: 2025/08/30

  Dataset Summary

The nfinite-product-masks-sample dataset is a dataset of images from 3D models for objects usually found in the home & living room space. Each image has been rendered photo-realistically from 3D models.Those 3D models are generic models, from any IP (as explained in the Personal and Sensitive Information part, any resemblance to an object from real life is purely… See the full description on the dataset page: https://huggingface.co/datasets/Nfiniteai/product-masks-sample.

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.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• 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!

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

Amazon Product Description Dataset

This dataset is a cleaned version of Amazon Product Data. Cleaned by team at https://exnrt.com

421K Unique Examples Empty description rows are being removed. Description Smaller then 200 characters are removed Convert to Proper Format Remove non-ASCII characters from both column And few more techniques

  Original Dataset

This original dataset has 10 Million Examples. Original, Un-cleaned DataSet:… See the full description on the dataset page: https://huggingface.co/datasets/Ateeqq/Amazon-Product-Description.

Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

This repository contains the data synthesis pipeline and synthetic product recognition datasets proposed in [1].

Data Synthesis Pipeline:

We provide the Blender 3.1 project files and Python source code of our data synthesis pipeline pipeline.zip, accompanied by the FastCUT models used for synthetic-to-real domain translation models.zip. For the synthesis of new shelf images, a product assortment list and product images must be provided in the corresponding directories products/assortment/ and products/img/. The pipeline expects product images to follow the naming convention c.png, with c corresponding to a GTIN or generic class label (e.g., 9120050882171.png). The assortment list, assortment.csv, is expected to use the sample format [c, w, d, h], with c being the class label and w, d, and h being the packaging dimensions of the given product in mm (e.g., [4004218143128, 140, 70, 160]). The assortment list to use and the number of images to generate can be specified in generateImages.py (see comments). The rendering process is initiated by either executing load.py from within Blender or within a command-line terminal as a background process.

Datasets:

SG3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 851,801 instances of 3,234 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.

SG3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.

SGI3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 838,696 instances of 1,063 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.

SGI3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.

SPS8k - Synthetic Product Shelves 8k (SPS8k) dataset, comprised of 16,224 synthetic shelf images with 1,981,967 instances of 8,112 supermarket products. Instance-level bounding boxes and GTIN class labels are provided for all product instances.

SPS8kt - Domain-translated version of SPS8k, utilizing SKU110k as the target domain. Instance-level bounding boxes and GTIN class labels for all product instances.

Table 1: Dataset characteristics.

Dataset

images

products

instances

labels
translation

SG3k 10,000 3,234 851,801 bounding box & generic class¹ none

SG3kt 10,000 3,234 851,801 bounding box & generic class¹ GroZi-3.2k

SGI3k 10,000 1,063 838,696 bounding box & generic class² none

SGI3kt 10,000 1,063 838,696 bounding box & generic class² GroZi-3.2k

SPS8k 16,224 8,112 1,981,967 bounding box & GTIN none

SPS8kt 16,224 8,112 1,981,967 bounding box & GTIN SKU110k

Sample Format

A sample consists of an RGB image (i.png) and an accompanying label file (i.txt), which contains the labels for all product instances present in the image. Labels use the YOLO format [c, x, y, w, h].

¹SG3k and SG3kt use generic pseudo-GTIN class labels, created by combining the GroZi-3.2k food product category number i (1-27) with the product image index j (j.jpg), following the convention i0000j (e.g., 13000097).

²SGI3k and SGI3kt use the generic GroZi-3.2k class labels from https://arxiv.org/abs/2003.06800.

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition." International Conference on Computer Analysis of Images and Patterns. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023domain, title={Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Analysis of Images and Patterns}, pages={239--250}, year={2023}, organization={Springer} }

🔍 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.

Unlock the potential of Ecommerce data scraping and extraction with APISCRAPY. Dive into Amazon data and tap into the vast Ecommerce market's secrets. Stay ahead of the competition by leveraging our powerful tool for comprehensive Ecommerce data insights.

Access the Home Depot products dataset, a comprehensive collection of web-scraped data featuring home improvement products. Discover trending tools, hardware, appliances, décor, and gardening essentials to enhance your projects. From power tools and building materials to lighting, furniture, and outdoor living items, this dataset provides insights into top-rated products, best-selling brands, and emerging trends.

Download now to explore detailed product data for smarter decision-making in home improvement, DIY, and construction projects.

For a closer look at the product-level data we’ve extracted from Home Depot, including pricing, stock status, and detailed specifications, visit the Home Depot dataset page. You can explore sample records and submit a request for tailored extracts directly from there.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.