In the first half of 2022, grocery was the most popular category for online shoppers in the United Kingdom among regular e-shoppers. Up to 57 percent of survey respondents had bought groceries via the internet in the previous six months as of July 2022. The second most-purchased products were fashion items, at 56 percent. Fresh food and beverages came in third, with 52 percent of UK shoppers ordering them online.

The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

This database contains medical device names and associated information developed by the Center. It includes a three letter device product code and a Device Class that refers to the level of CDRH regulation of a given device.

According to a survey on e-commerce and online shopping in Thailand as of January 2023, around 70 percent of the respondents prefer to shop fashion products online. This was followed by beauty and personal care products with around 49.7 percent of the survey participants.

Context

This dataset was created by our in house teams at [PromptCloud and DataStock. This dataset contains 30K records in it. You can download the whole dataset here.

Content

This dataset contains the following:

Total Records Count: 697053  Domain Name: amazon.com  Date Range: 01st Jan 2020 - 31st Jan 2020   File Extension: CSV

Available Fields: Uniq Id, Product Name, Brand Name, Asin, Category, Upc Ean Code, List Price, Selling Price, Quantity, Model Number, About Product, Product Specification, Technical Details, Shipping Weight, Product Dimensions, Image, Variants, SKU, Product Url, Stock, Product Details, Dimensions, Color, Ingredients, Direction To Use, Is Amazon Seller, Size Quantity Variant, Product Description 

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud and DataStock.

About this Dataset

A downloadable list of Amazon Bestseller categories and subcategories, along with the top 10 products in each category/subcategory.

Category

Product Lists

Keywords

ecommerce

Row Count

361650

Price

$30.00

Asos is the biggest Fashion platform in the USA. An eCommerce big dataset from a multi-category refers to a collection of information that has been gathered from various online stores across multiple product categories. This dataset contains over 60,000 data points and includes information such as product names, prices, descriptions, categories, and user reviews.

The dataset can be used to analyze consumer behavior and trends across various product categories, such as electronics, fashion, home and garden, sports, and many more. It can also be used to train machine learning models for various applications, such as product recommendation systems, demand forecasting, and price optimization.

Product Listing From Amazon India

30K Records of Brand, Offers, Prices, and Images

By PromptCloud [source]

About this dataset

This e-commerce dataset contains detailed records on over 553724 products from Amazon India over a one month period (October 1st, 2019 - October 31st, 2019). It includes valuable data points such as product title, description, brand name, price, stock availability and more. It’s the perfect resource for budding entrepreneurs to market their businesses in the ever expanding e-commerce world. With fields such as unique ID tracking and offers associated with each product listing in the form of combos and discounts - this dataset can be ideal for understanding the pricing strategies employed by different brands and optimize campaigns depending one marketplace trends. This would allow individuals to make informed decisions when it comes to developing an effective e-commerce strategy. With numerous insights that can be derived from this set of 30k listings - it shows potential researchers important factors like which category is most heavily saturated or what campaigns are being used by various brands.. Start your creative analytics journey now by downloading this comprehensive record store today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Using this dataset, retailers can compare their current product pricing strategies to their competitors and make adjustments to maximize profit. They can also track particular brands if they want to increase their market share of those products.
• Researchers can use this dataset to analyze the effect of different packaging sizes and offers on consumer purchase decisions – helping them understand what combination will help them boost sales effectively.
• Organizations could use the data in this dataset to identify trends among different demographics at specific locations and segment target markets for effective campaigns or promotions with advertisements that resonate best with those audiences

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: home_sdf_marketing_sample_for_amazon_in-ecommerce_20191001_20191031_30k_data.csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------------| | Crawl Timestamp | The timestamp of when the data was crawled. (DateTime) | | Category | The category of the product. (String) | | Product Title | The title of the product. (String) | | Product Description | The description of the product. (String) | | Brand | The brand of the product. (String) | | Pack Size Or Quantity | The size or quantity of the product. (Integer) | | Mrp | The maximum retail price of the product. (Float) | | Price | The current price of the product. (Float) | | Site Name | The name of the website where the product is listed. (String) | | Offers | Any offers associated with the product. (String) | | Combo Offers | Any combo offers associated with the product. (String) | | Stock Availibility | The availability of the product. (Boolean) | | Product Asin | The Amazon Standard Identification Number of the product. (String) | | Image Urls | The URLs of the images associated with the product. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit PromptCloud.

In 2023, the prevailing product category purchased on social media in the United States was apparel. As indicated by a survey, 25.6 percent of users reported this category as their primary choice for making purchases on social networks. Following closely were beauty products and home goods, with 19.4 percent and 13.5 percent of respondents favoring these respective categories.

About this Dataset

A downloadable product list for the top search results in the category of Cameras on Amazon

Category

Product Lists

Keywords

ecommerce

Row Count

1694

Price

$15.00

This statistic shows the most popular product categories on Tokopedia according Indonesian online shoppers as of 2018. That year, 22 percent of the surveyed respondents replied that the mobile and electronics category was the most popular on Tokopedia, followed by fashion with 12 percent.

About this Dataset

A downloadable product list for the top search results in the category of Computers on Amazon

Category

Product Lists

Keywords

ecommerce

Row Count

1570

Price

$15.00

Companies are often posed with the problem of cataloging products effectively to help customers in navigating to the product of requirement. Each class of product is assigned an index that can be used to track its type.

The task is to classify the products based on the description and title and identify the correct index. This dataset is for educational purposes. For validating the results split the training file into two sets.

Task: Develop a text classification pipeline to identify product categories.

Metrics: Top-1 Accuracy

he Amazon Products and Reviews dataset provides a comprehensive view of the products available on Amazon, as well as customer reviews and ratings. It includes detailed information such as product descriptions, prices, seller information, product categories, and more.

This dataset is highly valuable for researchers, analysts, and businesses looking to gain insights into product trends, customer behavior, and consumer sentiment. It can be used to analyze the performance of products, identify popular product categories, and inform product development strategies.

In addition to the product details, the dataset also includes customer reviews and ratings, providing a wealth of information about customer preferences and opinions. The reviews data includes information such as review text, rating, helpfulness score, and more.

The Amazon Products and Reviews dataset is regularly updated to reflect changes in the Amazon marketplace, making it a valuable resource for anyone seeking to stay informed about the latest developments in the e-commerce industry. Overall, this dataset is an indispensable tool for businesses looking to gain a competitive edge in the fast-paced world of online retail.

Almost 30 percent of U.S. Amazon Prime buyers bought home goods in occasion of the Amazon Prime Day in July 2023. During the survey period, it was found that 24 percent of responding Prime Day purchasers in the United States had purchased apparel and shoes during the sales event.

About this Dataset

A downloadable product list for the top search results in the category of Automotive on Amazon

Category

Product Lists

Keywords

ecommerce

Row Count

2503

Price

$15.00

This dataset is a collection of 30000 women fashion products. Categories covered in this dataset is western wear, Indian wear, perfumes and fragrances, watches and nightwear. ✨ You can use this dataset to apply your data cleaning, visualization and analytical skills. Column description is mentioned below:
BrandName: Mentions the brand of the product Details: Deatils about the product
Size: Sizes available
MRP: This is max retail price
SellPrice: This is the price after discount
Category: Category of the product
Nan value is null value

I hope you liked the dataset. I'd love to get you feedback.🙌 Please upvote this dataset.👍

The given dataset appears to be a sales dataset containing information about different orders. Here is a description of the data:

1. Row ID: An identifier for each row in the dataset.
2. Order ID: Unique identifier for each order.
3. Order Date: The date when the order was placed.
4. Ship Date: The date when the order was shipped.
5. Ship Mode: The mode of shipping chosen for the order.
6. Customer ID: Unique identifier for each customer.
7. Customer Name: Name of the customer who placed the order.
8. Segment: The segment to which the customer belongs (e.g., consumer, corporate).
9. Country: The country where the order was placed (in this case, United States).
10. City: The city where the order was placed.
11. State: The state where the order was placed.
12. Postal Code: The postal code associated with the order's location.
13. Region: The region of the country where the order was placed.
14. Product ID: Unique identifier for each product.
15. Category: The category to which the product belongs (e.g., furniture, office supplies).
16. Sub-Category: The sub-category to which the product belongs (e.g., bookcases, chairs).
17. Product Name: The name of the product.
18. Cost: The cost of the product.
19. Price: The price at which the product was sold.
20. Profit: The profit made from the sale of the product.
21. Quantity: The quantity of the product ordered.
22. Sales: The total sales generated from the order (quantity multiplied by price).

The dataset provides detailed information about each order, including customer details, product details, sales information, and shipping information. It can be used to analyze various aspects of the sales data, such as profitability, customer segments, product categories, and regional sales performance.

In 2019, entertainment products were the most popular category among online shoppers in Costa Rica, with 82 percent of respondents saying they usually buy these. Electronic and IT products came in second, with 69 percent of participants, followed by clothing, shoes, and jewerly, with 50 percent.

Mobile devices and their accessories were the most purchased category in marketplaces in Spain in 2021, with 54 percent of respondents reporting buying these items over this channel. Furniture and technology ranked second with 52 percent of respondents, followed by gaming items with 48 percent. Online marketplaces offer products and services from multiple third parties. In 2021, Amazon was by far the marketplace with the highest net sales in Spain.