Walmart — Product Search & Data Extraction

You can extract Walmart product data without a browser by using HTTP GET requests to fetch search pages and parsing the __NEXT_DATA__ SSR payload to retrieve structured fields like price, availability, and ratings.

Parsing __NEXT_DATA__ for Walmart web scraping works by accessing the server-side rendered JSON payload embedded in the page HTML, which contains structured product details, specifications, highlights, and customer reviews without needing browser rendering.

Yes, you can extract Walmart product reviews and pricing using SSR data. The skill parses the __NEXT_DATA__ payload to pull structured fields including priceString, wasPrice, savings, averageRating, numberOfReviews, and full customer review text.

When extracting Walmart search results programmatically, available fields include usItemId, name, brand, price, linePrice, wasPrice, savings, averageRating, numberOfReviews, availability, isSponsored, url, and thumbnailUrl.

Yes, this Walmart data extraction method handles pagination and sponsored listings. It supports keyword searches across multiple pages, parses two item stacks, and explicitly flags sponsored products using the isSponsored output field.

The main limitation of browserless Walmart product scraping is that it relies entirely on the static __NEXT_DATA__ SSR payload, meaning it cannot execute JavaScript or interact with dynamically loaded content that requires browser rendering.