# Google Shopping-dataset-samples

A sample dataset of 1001 Google Shopping

A Google Shopping dataset sample of over 1000 records. Dataset was extracted using the Bright Data API.

Some of the data points that are included in the Google Shopping dataset:

• url: The URL or link to the product
• product_id: Unique identifier for the product
• title: Title or name of the product
• product_description: Description of the product
• rating: Average rating of the product
• reviews_count: Number of ratings or reviews for the product
• images: Images of the product
• variations: Product variations
• tags: Tags associated with the product
• product_details: Additional details about the product
• amount_of_stars: Distribution of star ratings
• seller_name: Name of the seller or vendor
• delivery_price: Price for delivery
• return_policy: Details of the return policy
• item_price: Price of the item
• total_price: Total price including all costs
• product_specifications: Specifications of the product
• related_items: Related items

And a lot more.

This is a sample subset which is derived from the "Google Shopping" dataset which includes more than 14.37K records.

Available dataset file formats: JSON, NDJSON, JSON Lines, CSV, or Parquet. Optionally, files can be compressed to .gz.

Dataset delivery type options: Email, API download, Webhook, Amazon S3, Google Cloud storage, Google Cloud PubSub, Microsoft Azure, Snowflake, SFTP.

Update frequency: Once, Daily, Weekly, Monthly, Quarterly, or Custom basis.

Data enrichment available as an addition to the data points extracted: Based on request.

Get the full Google Shopping dataset.

What are the Google Shopping datasets use cases?

1. Consumer Sentiment

Leverage a Google Shopping dataset to understand customer sentiment toward your products. Extract valuable insights to make informed business decisions by analyzing trending categories, popular regional brands, and changes in consumer demand and product popularity.

2. Pricing Optimization

Businesses can shape customer purchasing decisions by utilizing insights from a Google Shopping dataset. Analyze factors such as price sensitivity and demand to refine product pricing, maintain competitiveness, and boost revenue and profitability.

3. Product Personalization

Analyze a Google Shopping dataset to uncover best-selling products, emerging trends, and underperforming items. Stay competitive by determining which products to stock, optimal stock levels, and the best timing for restocking to maximize profitability.

Free access to web scraping tools and datasets for academic researchers and NGOs

The Bright Initiative offers access to Bright Data's Web Scraper APIs and ready-to-use datasets to leading academic faculties and researchers, NGOs and NPOs promoting various environmental and social causes. You can submit an application here.