Target Australia Scraper

Extract products and customer reviews from target.com.au — Australia's Target department store. Search by keyword or paste product, category, and search URLs. Every product comes back as one flat record with pricing, images, category, stock, variations, and (optionally) full ratings and reviews.

What you get

• Product identity — name, brand, product id, product URL, category path
• Pricing — current price, was-price, OnePass price, on-sale flag, currency
• Details — description, feature list, care instructions
• Merchandising — colour, size/colour variants, images, stock, exclusivity flags
• Ratings & reviews (optional) — average rating, review count, rating distribution, recommended count, and each review's author, rating, title, body, date, verified-purchase flag, helpfulness, and sub-ratings

Modes

Search mode — provide one or more keywords and, optionally, a brand, colour, size, price range, and a sort order (relevance, latest, rating, price, brand).

URL mode — paste any mix of:

• product URLs — https://www.target.com.au/p/<slug>/<code>
• category URLs — https://www.target.com.au/c/<path>/<id>
• search URLs — https://www.target.com.au/search?text=<keywords>

Both modes walk pages forward until the result set or your Max products cap is reached.

Example input

{
  "mode": "search",
  "searchTerms": ["linen shirt"],
  "sortBy": "price-asc",
  "maxItems": 50,
  "fetchReviews": true,
  "maxReviews": 20
}

Example output

The record below is illustrative sample data.

{
  "kind": "product",
  "id": "W1234567890",
  "name": "Sample Linen Long Sleeve Shirt",
  "url": "https://www.target.com.au/p/sample-linen-long-sleeve-shirt/12345678",
  "brand": "Sample Brand",
  "price": 45.0,
  "wasPrice": 60.0,
  "onePassPrice": null,
  "currency": "AUD",
  "onSale": true,
  "clearance": false,
  "description": "A lightweight, breathable everyday shirt.",
  "features": ["Regular fit", "Button-through front", "Curved hemline"],
  "careInstructions": ["Warm delicate machine wash", "Do not bleach"],
  "category": "Clothing & Accessories > Clothing > Shirts & Tops",
  "topLevelCategory": "Women",
  "colour": "White",
  "inStock": true,
  "onlineDate": "2026-05-24",
  "image": "https://assets.target.com.au/transform/00000000-0000-0000-0000-000000000000/sample_1",
  "images": [
    "https://assets.target.com.au/transform/00000000-0000-0000-0000-000000000000/sample_1",
    "https://assets.target.com.au/transform/11111111-1111-1111-1111-111111111111/sample_2"
  ],
  "variations": [
    { "colour": "White", "size": "10", "price": 45.0, "inStock": true }
  ],
  "averageRating": 4.8,
  "reviewCount": 18,
  "ratingDistribution": { "5": 15, "4": 3 },
  "recommendedCount": 17,
  "reviewsCollected": 2,
  "reviews": [
    {
      "id": "100000001",
      "rating": 5,
      "title": "Great everyday shirt",
      "body": "Comfortable and true to size.",
      "author": "Sample Reviewer",
      "location": "Victoria",
      "date": "2026-03-19T12:18:09.000+00:00",
      "verifiedPurchase": true,
      "isRecommended": true,
      "helpfulCount": 3,
      "subRatings": { "Quality": 5, "Value For Money": 5 }
    }
  ]
}

Ratings & reviews

Turn on Fetch ratings and reviews to attach ratings and reviews to each product. Products with no reviews leave the rating and review fields absent (they are never faked). Review photos and videos are intentionally not included.

Filters & sorting

• Sort: relevance, latest, rating, price (low→high / high→low), brand
• Filters: brand, colour, size (exact facet values), and a price range

Connection

Runs on Apify Proxy — the default automatic pool is enough. Target Australia serves its home market, so residential Australian exits are used automatically as a reliability fallback if needed.

Export to your apps (optional)

Pipe results straight into Notion, Linear, Airtable, or Apify via Model Context Protocol (MCP) connectors. Authorize a connector under Settings → API & Integrations, then select it in the input. This is a side-channel; it never changes the dataset.

Notes

• Prices are in AUD.
• One dataset row per product; reviews are nested under each product.
• Use Max products and Max pages per search to bound a run.