
Extract detailed product data from Walmart, including prices, SKUs, brands, ratings, reviews, seller information, nutrition facts, and product variants. Ideal for product research, price monitoring, and competitor analysis. Supports structured exports, API access, and third-party integrations.
The Walmart Product Scraper lets you extract structured product data from Walmart in bulk using keywords. Just enter one or more search terms, and the scraper will automatically collect the core product data you need. It is built for product research, competitor monitoring, and smarter operational decision-making.
Compared with manual search, this scraper makes it much easier to collect Walmart product results at scale and turn them into structured output for research, monitoring, and downstream workflows.
Efficient product research: Collect product names, prices, ratings, reviews, and category data in bulk to identify promising products faster. Competitor monitoring: Track how competing products are positioned across different keywords, including pricing, review signals, and seller activity. Price and shelf analysis: Monitor product prices, brands, specifications, and variants over time to support pricing analysis and shelf strategy optimization. Customer insight discovery: Use reviews, ratings, and tags to uncover what customers care about most and identify opportunities for product or content improvement. Automated product data collection: Replace manual search with repeatable workflows so your datasets stay accurate, consistent, and up to date.
| 📝 Product name | 🔗 Product URL, product ID |
| 💲 Price, currency | 🏷️ SKU, GTIN |
| 🌐 Brand | 📂 Category, category URL |
| ⭐ Rating | 💬 Review count, review tags |
| 🖼️ Product images | 📝 Product description |
| 🎨 Size, color | 🛍️ Seller information |
| 🥗 Nutrition information | 🔀 Product variants |
The Walmart Product Scraper is designed to be simple to use, so you can start extracting data quickly even without prior scraping experience.
Enter the product keywords you want to search on Walmart, such as iPhone or leggings. To cover more results, you can combine multiple related but non-duplicative keywords. This usually helps you discover a wider range of products.

When the run is complete, the collected Walmart data is organized under the Output tab as a structured dataset. For easier review, results are displayed in tables and tabs, and can be downloaded in CSV or JSON format.
The Walmart Product Scraper automatically visits Walmart search result pages and converts unstructured page content into structured CSV and JSON output. You only need to provide product keywords and related parameters, and the scraper handles the collection process for you.
The scraper can typically return product URLs, prices, SKUs, currency, GTINs, brands, reviews, descriptions, product names, categories, images, ratings, sizes, colors, seller information, nutrition facts, and product variants. Exact fields may vary depending on the product type and page content.
Yes. In addition to the visual dashboard, the scraper can also be integrated into API-based workflows, making it easier for developers to connect collection jobs to internal systems, data pipelines, or automation tasks.
Yes. Each run collects the latest publicly available Walmart data rather than relying on cached results, which helps keep the output fresh and useful.
No. The Walmart Product Scraper is a no-code solution built for both technical and non-technical users. You can configure tasks, run jobs, and download results through a simple graphical interface.
Yes. You can use the all_variations parameter to control whether all product variants should be extracted. This is especially useful for products with multiple sizes, colors, or packaging options.
The output can be exported in structured formats such as CSV and JSON, making it easy to use in spreadsheets, BI tools, databases, and internal systems.
Publicly available product data is commonly used for research, analytics, and business intelligence. That said, users should still comply with local data privacy regulations and review the target website’s terms of service and compliance requirements.
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