BIG W Product Scraper

Extract structured product data from BIG W (bigw.com.au), Australia's major discount department store — by keyword search, whole-category browse, or a list of product URLs. Every row carries live pricing and deals, barcodes, variants, images, specifications, and Bazaarvoice ratings & reviews — as clean JSON, CSV or Excel.

Built on BIG W's own JSON API (no fragile HTML scraping), so results are fast, complete and reliable.

Who uses this

• Price-intelligence & competitive-monitoring teams / brands — track BIG W prices, was-prices, promo badges and stock for a category or brand.
• Resellers & marketplace sellers — monitor competitor pricing, spot clearance arbitrage, and pull GTIN/EAN for catalogue matching.
• Market researchers & analysts — assortment, brand presence, and customer sentiment from ratings and reviews.
• RetailTech / SaaS developers & AI agents — a stable product + review feed to power apps, dashboards and models.

What you get — output fields

Field	Type	Description
article_id	string	BIG W numeric product id
name, brand	string	Product name and brand
url, slug	string	Product page URL
gtin, ean, barcode	string	Barcodes
price	number	Current price (AUD)
was_price, rrp	number	Previous / recommended retail price
save_amount, save_percent	number	Savings in $ and %
price_label, promo_badge	string	e.g. SPECIAL, CLEARANCE, 20% OFF
unit_price	string	Comparative unit price, e.g. $0.27 / 1 EA
is_clearance, on_sale	boolean	Deal flags
in_stock, listing_status, max_quantity	mixed	Availability
sold_by, product_channel	string	BIG W vs Marketplace seller
deliverable, collectable, pickup	boolean	Fulfilment options
category, category_id, categories	mixed	Category + full breadcrumb
image, images	string/array	Product imagery
specifications	array	Name/value specs
variants, num_variants	array/int	Per colour/size: code, SKU, barcode
rating, reviews_count, recommended_count	number	Rating summary
rating_distribution	array	Star breakdown
reviews	array	Individual reviews (title, text, author, pros/cons, photos)
scraped_at	string	ISO-8601 timestamp

High-value use cases

1. Competitive price monitoring — schedule a daily run over your categories and diff price / was_price / promo_badge to detect price moves.
2. Clearance & deal sourcing — filter for is_clearance / save_percent to find arbitrage or bargains across the catalogue.
3. Catalogue matching / PIM — join to your own catalogue on gtin / ean and enrich with images, specs and variants.
4. Review & sentiment analysis — pull ratings and full review text for product research or model training.
5. Assortment & brand tracking — browse a category to map every product, brand and price point BIG W carries.

Input parameters

Field	Type	Default	Description
searchQueries	string[]	[]	Keywords to search
categoryIds	string[]	[]	Browse whole categories by id
productUrls	string[]	[]	Product URLs or bare codes
maxItems	integer	200	Global result cap
maxPagesPerQuery	integer	5	Pages per query (48/page)
sort	enum	relevance	relevance / name / price
onlyInStock	boolean	false	In-stock only
minPrice, maxPrice	integer	–	Price band (AUD)
brands	string[]	[]	Restrict to brands
soldBy	enum	any	BIG W / Marketplace Seller
includeFullDetail	boolean	true	Description + variants
includePricing	boolean	true	was/RRP/savings/promo
includeReviewStats	boolean	true	Rating summary
includeReviews	boolean	false	Individual reviews
maxReviewsPerProduct	integer	20	Cap reviews per product
proxyConfiguration	object	Apify AUTO	Optional egress

Tip: a single BIG W search is capped at 2000 results. To pull an entire large category, browse by categoryIds (and optionally narrow by brands or a price band) rather than one broad keyword.

Example input

{
    "searchQueries": ["coffee machine"],
    "maxItems": 100,
    "onlyInStock": true,
    "includeReviews": true,
    "maxReviewsPerProduct": 20
}

Example output (one row, trimmed)

{
    "article_id": "80247",
    "name": "LEGO DUPLO Classic Deluxe Brick Box 10914 Building Toy",
    "brand": "LEGO",
    "url": "https://www.bigw.com.au/product/.../p/80247",
    "gtin": "5702016617757",
    "price": 55.0,
    "save_amount": 14.0,
    "save_percent": 20.3,
    "price_label": "SPECIAL",
    "promo_badge": "20% OFF",
    "in_stock": true,
    "sold_by": "BIG W",
    "category": "LEGO Duplo",
    "rating": 4.93,
    "reviews_count": 28,
    "variants": [
        { "code": "80247", "barcode": "5702016617757", "uom": "pieces" }
    ],
    "reviews": [{ "rating": 5, "title": "Brilliant", "author": "AlexandraO" }],
    "scraped_at": "2026-07-31T02:00:00Z"
}

Scheduling & integrations

• Schedule daily/weekly runs from the Apify Console for fresh price and stock snapshots.
• Webhooks fire on run completion to push new data into your systems.
• Export to JSON, CSV, Excel, or XML, or sync to Google Sheets, Make, Zapier, Airbyte, or S3 via Apify integrations.
• Pull results with the Apify API or client libraries.

Using it from an AI agent (MCP)

This Actor works over the Apify MCP server, so AI assistants (Claude, ChatGPT, Cursor, etc.) can call it as a tool — e.g. "get current BIG W prices and reviews for coffee machines" — and receive structured JSON back.

FAQ

How many products can I get per run? As many as you set in maxItems. A single search is capped at 2000 results by BIG W; browse by category (optionally sliced by brand/price) to cover large sections of the catalogue.

Is pricing live? Yes — current price, was-price, RRP, savings and promo badges come from BIG W's live price API at scrape time.

Are reviews included? Ratings summaries are on by default; enable includeReviews to also collect individual review text, authors and photos.

How fresh is the data? It is fetched live on each run. Schedule runs to keep a time series for price/stock tracking.

Do I need proxies or an unblocker? No external subscription is required — the Actor talks to BIG W's official JSON API directly. Apify Proxy is used for a stable egress IP and can be switched to Australian residential if needed.

Is this legal? The Actor collects only publicly available product data. You are responsible for using the data in line with BIG W's terms and applicable law.